Process control systems and methods having learning features

A system for operating a process includes a processing circuit that uses a self-optimizing control strategy to learn a steady-state relationship between an input and an output. The processing circuit is configured to switch from using the self-optimizing control strategy to using a different control strategy that operates based on the learned steady-state relationship.

BACKGROUND

Self-optimizing control strategies such as extremum seeking control can be effective tools for seeking optimum operating conditions in a process control system. Some loss (e.g., a hunting loss) and equipment wear, however, may be associated with any self-optimizing control strategy that uses a varying signal to conduct the search for optimum operating conditions. It is challenging and difficult to develop robust process control systems and methods.

SUMMARY

One embodiment of the invention relates to a system for operating a process. The system includes a processing circuit that uses a self-optimizing control strategy to learn a steady-state relationship between a manipulated variable and an output variable. The processing circuit is configured to switch from using the self-optimizing control strategy to using a second control strategy that operates based on the learned steady-state relationship.

Another embodiment of the invention relates to a system for operating a process. The system includes a processing circuit. The processing circuit includes at least one sensor input, an extremum seeking controller, and a model-based controller. The processing circuit is configured to switch between using the extremum seeking controller to control the process and using the model-based controller to control the process. The processing circuit is configured to store process characteristics of a steady-state of the extremum seeking controller and the processing circuit is configured to operate the model-based controller using the stored process characteristics.

Another embodiment of the invention relates to a method for operating a process. The method includes using a self-optimizing control strategy to learn a steady-state relationship between measured inputs and outputs that minimizes energy consumption. The method further includes switching from using the self-optimizing control strategy to using an open-loop control strategy that operates based on the learned steady-state relationship between measured inputs and outputs that minimizes energy consumption.

Another embodiment of the invention relates to a method for operating a process. The method includes using a processing circuit to cause an extremum seeking controller to control the process. The method further includes storing process characteristics of a steady-state of the extremum seeking controller in a memory device. The method yet further includes switching from using the extremum seeking controller to control the process to using a model-based controller to control the process. The method also includes operating the model-based controller using the stored process characteristics.

Another embodiment of the invention relates to a method for operating a process. The method includes using a self-optimizing controller to learn a steady state relationship between a manipulated variable and an output variable. The method also includes switching from using the self-optimizing controller to using a second controller that operates based on the learned steady state relationship. The second control strategy may be an open loop control strategy that conducts open loop control based on control variables observed while the process was operating in the learned steady state relationship. The method may further include operating the process using the second controller primarily and operating using the self-optimizing controller periodically. The method can also or alternatively include operating the process using the self-optimizing controller during at least one of a start-up state and a training state of the process. In some embodiments, the method can include detecting whether a steady state has been obtained and learning the steady state relationship between a manipulated variable and an output variable by recording calculated and/or sensed parameters existing during the steady state relationship, the calculated and/or sensed parameters provided to the second controller for operation of a model-based control strategy. The self-optimizing controller may be an extremum seeking controller.

Another embodiment relates to a system for controlling a cooling tower that cools condenser fluid for a condenser of a chiller. The system includes a cooling tower fan system that controllably varies a speed of at least one fan motor. The system further includes an extremum seeking controller that receives inputs of power expended by the cooling tower fan system and of power expended by the chiller. The extremum seeking controller provides an output to the cooling tower fan system that controls the speed of the at least one fan motor. The extremum seeking controller determines the output by searching for a speed of the at least one fan motor that minimizes the sum of the power expended by the cooling tower fan system and the power expended by the chiller. The system further includes a model-based controller for controlling the speed of the at least one fan motor. The system also includes a processing circuit configured to store process characteristics associated with a steady-state of the extremum seeking controller. The processing circuit is further configured to switch from using the extremum seeking controller to using the model-based controller to control the fan speed. The processing circuit operates the model-based controller using the stored process characteristics. The processing circuit may be configured to use the extremum seeking controller during an initial training period. The stored process characteristics associated with the steady state of the extremum seeking controller and used by the model-based controller can include PLRtwr,cap(the part-load ratio at which the tower operates at its capacity) and βtwr(the slope of the relative tower airflow versus the part-load ratio). The processing circuit may be configured to store the maximum part load ratio (PLRmax) during the training period and the minimum part load ratio (PLRmin) during the training period. The processing circuit may be configured to monitor the part load ratio during the model-based control and wherein the processing circuit is configured to switch back to using the extremum seeking controller if the part load ratio exceeds PLRmaxor drops below PLRminduring operation using the model-based controller.

DETAILED DESCRIPTION

Before turning to the figures, which illustrate the exemplary embodiments in detail, it should be understood that the disclosure is not limited to the details or methodology set forth in the description or illustrated in the figures. It should also be understood that the terminology is for the purpose of description only and should not be regarded as limiting.

Referring generally to the Figures, systems and methods are shown for operating a process. A system includes a processing circuit that uses a self-optimizing control strategy to learn a steady-state relationship between a manipulated variable and an output variable. The processing circuit is configured to switch from using the self-optimizing control strategy to using a different control strategy that operates based on the learned steady-state relationship.

Referring now toFIG. 1, a block diagram of a system for operating a process102is shown, according to an exemplary embodiment. Process102may be any type of process that can be controlled via a process controller. For example, process102may be an air handling unit configured to control temperature within a building space. In other embodiments, process102can be or include a chiller operation process, a damper adjustment process, a mechanical cooling process, a ventilation process, or any other process where a variable is manipulated to affect a process output or variable.

Process controller104operates process102by outputting and controllably changing a manipulated variable provided to process102. An output variable affected by process102or observed at process102(e.g., via a sensor) is received at process controller104. Process controller104includes logic that adjusts the manipulated variable to achieve a target outcome for process102(e.g., a target value for the output variable).

In some control modes, the logic utilized by process controller104utilizes feedback of an output variable. The logic utilized by process controller104may also or alternatively vary the manipulated variable based on a received input signal (e.g., a setpoint). The setpoint may be received from a user control (e.g., a thermostat), a supervisory controller, or another upstream device.

Process controller104is shown to include a self-optimizing controller106, a model-based controller108, and a control strategy switching module110. Self-optimizing controller106may be configured to search for values of the manipulated variable that optimize the output variable (i.e., a controlled variable, a measured output variable, a calculated output variable, etc.). In an exemplary embodiment, self-optimizing controller106is an extremum seeking control (ESC) module or controller.

Extremum seeking control is a class of self-optimizing control that can dynamically search for the unknown and/or time varying input or inputs of a process to optimize a certain performance index (e.g., approach a target value for one or more output variables). Extremum seeking control can be implemented using gradient searching through the use of dithering signals (e.g., sinusoidal, square-wave, etc.). That is, the gradient of the process's output (e.g., the output variable) with respect to the process's input (e.g., the manipulated variable) is typically obtained by perturbing (e.g., varying in a controlled manner, oscillating, etc.) the manipulated variable and applying a corresponding demodulation on the observed changes in the output variable. Improvement or optimization of system performance is sought by driving the gradient toward zero by using integration. Extremum seeking control is typically considered a non-model based control strategy, meaning that a model for the controlled process is typically not relied upon by the extremum seeking controller to optimize the system. While self-optimizing controller106is preferably an extremum seeking controller, in some alternative embodiments self-optimizing controller106can use other self-optimizing control strategies. Some embodiments of self-optimizing controller106may implement the extremum seeking control systems or methods described in one or more of U.S. application Ser. No. 11/699,589, filed Jan. 30, 2007, U.S. application Ser. No. 11/699,860, filed Jan. 30, 2007, U.S. application Ser. No. 12/323,293, filed Nov. 25, 2008, U.S. application Ser. No. 12/683,883, filed Jan. 7, 2010, and U.S. application Ser. No. 12/650,366, filed July 16.

Referring now toFIG. 2in addition toFIG. 1, process controller104uses self-optimizing controller106to learn a steady-state relationship between a manipulated variable and an output variable (step202of process200). Process controller104switches from using a self-optimizing controller106to using a model-based controller108that uses the learned steady-state relationship (step204of process200).

Learning a steady state relationship can include detecting a steady state condition for process102and storing, for example, a manipulated variable that corresponds with a target output variable. In other embodiments, learning a steady state relationship can be or include storing a multiplier, a coefficient, a residual, or another variable or set of variables that describes a determined mathematical relationship between a manipulated variable and an output variable. In yet another example, a table of values around a steady-state operating point may be established for responding to varying input signals or varying process conditions. For example, where the process controller is configured to set an air handling unit damper position to cause a room temperature to approach a setpoint input signal, the process controller104can store a matrix that relates a plurality of possible temperatures to damper positions based on a learned steady state relationship. Accordingly, non-linear relationships between the manipulated variable and the output variable may be stored based on steady-state relationships between the two variables. In other embodiments, multiple coefficients of a multi-variable equation describing the relationship between the manipulated variable and the output variable may be determined and stored to describe non-linear steady-state relationships.

The decision to switch from using the self-optimizing controller106to using the model-based controller108may be completed by control strategy switching module110or another logic module of process controller104.

The model-based controller108may be a closed-loop controller, a feedback controller, a feedforward controller, an open-loop controller, or any other controller that uses one or more models to determine control adjustments to the manipulated variable or variables.

Referring now toFIG. 3, a more detailed block diagram of a system300for operating a process302is shown, according to an exemplary embodiment. Process controller304includes a processing circuit312that uses a self-optimizing controller306to learn a steady state relationship between a manipulated variable and an output variable. The processing circuit312is configured to switch from using the self-optimizing controller306to using a different control strategy (e.g., that of model-based controller308) that operates based on the learned steady-state relationship.

Process controller304is shown to include processing circuit312. Processing circuit312is shown to include a processor314and a memory316. According to an exemplary embodiment, processor314and/or all or parts of processing circuit312can be implemented as a general purpose processor, an application specific integrated circuit (ASIC), one or more programmable logic controllers (PLCs), one or more field programmable gate arrays (FPGAs), a group of processing components, one or more digital signal processors, other suitable electronics components, or a combination thereof.

Memory316(e.g., memory unit, memory device, storage device, etc.) is one or more devices for storing data and/or computer code for completing and/or facilitating the various processes described in the present disclosure. Memory316may be or include volatile memory or non-volatile memory. Memory316may include database components, object code components, script components, or any other type of information structure for supporting the various activities described in the present disclosure. According to an exemplary embodiment, memory316is communicably connected to processor314via processing circuit312and includes computer code for executing (e.g., by processor314) one or more processes described herein. Memory316may also include various data regarding the operation of one or more of the control loops relevant to the system (e.g., performance map data, historical data, behavior patterns regarding process behavior, state machine logic, start-up logic, steady-state logic, etc.).

Interfaces324,326,328may be or include any number of jacks, wire terminals, wire ports, wireless antennas, or other communications interfaces for communicating information or control signals (e.g., a control signal of the manipulated variable output at interface326, sensor information received at input interface324, setpoint information received at communications interface328, etc.). Interfaces324,326may be the same type of devices or different types of devices. For example, input interface324may be configured to receive an analog feedback signal (e.g., an output variable, a measured signal, a sensor output, a controlled variable) from a controlled process component (or a sensor thereof) while communications interface328may be configured to receive a digital setpoint signal from upstream supervisory controller332via network330. Output interface326may be a digital output (e.g., an optical digital interface) configured to provide a digital control signal (e.g., a manipulated variable) to a controlled process component. In other embodiments, output interface326is configured to provide an analog output signal. In some embodiments the interfaces can be joined as one or two interfaces rather than three separate interfaces. For example, communications interface328and input interface324may be combined as one Ethernet interface configured to receive network communications from a supervisory controller. In other words, the supervisory controller may provide both the setpoint and process feedback via an Ethernet network (e.g., network330). In such an embodiment, output interface326may be specialized for the controlled process component of process302. In yet other embodiments, output interface326can be another standardized communications interface for communicating data or control signals. Interfaces324,326,328can include communications electronics (e.g., receivers, transmitters, transceivers, modulators, demodulators, filters, communications processors, communication logic modules, buffers, decoders, encoders, encryptors, amplifiers, etc.) configured to provide or facilitate the communication of the signals described herein.

Memory316includes master control module318, model-based controller308, self-optimizing controller306, and control parameter storage module322. Master control module318may generally be or include software for configuring processing circuit312generally and processor314particularly to operate process302using a self-optimizing control strategy (via self-optimizing controller306) to learn a steady-state relationship between a manipulated variable and an output variable. Once the relationship is learned or in response to one or more other conditions (e.g., a time expiring), master control module318switches control of operation of the process from the self-optimizing control strategy to a different control strategy (e.g., via model-based controller308). Master control module318causes the model-based controller to operate based on the steady-state relationship learned using the self-optimizing controller.

As the self-optimizing controller306operates (e.g., seeking optimal values for the manipulated variable), the output variable (and, in some embodiments, any other inputs used by the self-optimizing controller) are provided to control parameter storage module322. The manipulated variable output from the self-optimizing controller306is also provided to control parameter storage module322.

In some embodiments, control parameter storage module322may be configured to store a detailed history of output variable to manipulated variable data sets. For example, control parameter storage module322may be configured to store output variable to manipulated variable data pairs with a timestamp on an every-minute basis. In other embodiments, different intervals of control parameter recording may be effected by control parameter storage module322(e.g., every second, every ten minutes, hourly, etc.). Control parameter storage module322may be configured to store data as it is received. In other embodiments, control parameter storage module322may be configured to smooth, average, aggregate, or otherwise transform the data for storage or use. For example, in one embodiment, control parameter module322may store an exponentially weighted moving average of output variables and an exponentially weighted moving average of manipulated variables. In some embodiments, other than conducting some basic transformation and storage relative to output variables (or other system inputs) and the manipulated variable, the control parameter storage module322does not conduct significant additional processing. In other embodiments, control parameter storage module322can further evaluate the received variables or the stored information to build or identify a model for use by the model-based controller308. For example, the control parameter storage module322may be configured to describe the relationship between the output variable and the manipulated variable as a complex expression, as a system of coefficients for an equation, as a coefficient matrix, as a system of rules, or as another model for describing the relationship between the output variable(s) and the manipulated variable. When control of the process302is switched from the self-optimizing controller306to the model-based controller308, the control parameter storage module322provides stored parameters, coefficients, rules, or other model descriptors to model-based controller308so that model-based controller308can operate the process302using the relationship learned by operation of the process302using self-optimizing controller306.

In the embodiment shown inFIG. 3, master control module318includes a steady state evaluator320and a control strategy switching module310. Steady state evaluator320is configured to receive parameters from control parameter storage module322. The steady state evaluator320can determine whether the self-optimizing controller306has reached a steady state. Steady state evaluator320can evaluate a steady state by establishing thresholds and checking for whether control parameters stay within the thresholds for a period of time. In other embodiments, steady state evaluator320can wait for a standard deviation of one or more standard deviations of a control parameters to shrink below a certain value, can initiate a timer when the standard deviations first fall below the certain value, and can determine that a steady state condition exists when the timer has elapsed a predetermined amount of time. Steady state evaluator320can provide a result of its determination and/or other state describing information to control strategy switching module310. In an exemplary embodiment, control strategy switching module310causes and coordinates the switch between self-optimizing controller306and model-based controller308. Control strategy switching module310can wait a predetermined (or random, quasi-random) period of time after steady state evaluator320indicates a steady state to effect a switch from self-optimizing controller306to model-based controller308.

Control strategy switching module310can cause the switch from self-optimizing controller306to model-based controller308via an instant or hard switch. For example, for a first time period the process302may be entirely controlled by self-optimizing controller306and in a second time period the process302is entirely controlled by model-based controller308. In another embodiment, the control strategy switching module310may be configured to include one or more logic mechanisms for smoothing the switch from one controller to another controller. In one such example, the control strategy switching module310may restrict output from model-based controller308but may begin providing inputs to model-based controller308some seconds or minutes early.

In an alternative embodiment to that shown inFIG. 3, control strategy switching module310and master control module318may be located downstream of model-based controller308and self-optimizing controller306. In such embodiments, control strategy switching module310may be configured to average, blend, or otherwise smooth the transition of control from one controller to the other controller.

In some embodiments, steady state evaluator320and control strategy switching module310are configured to cause control to be switched back to self-optimizing controller306from model-based controller308. For example, steady state evaluator320may be configured to receive the same inputs that are being provided to model-based controller308. If process302changes such that the inputs to model-based controller308begin significantly changing, steady state evaluator320can communicate such a change to control strategy switching module310. Control strategy switching module310, in response to such a communication, can then cause self-optimizing controller306to resume control of the process302and for model-based controller308to discontinue control. Control strategy switching module310may then cause self-optimizing controller306to operate until a new steady state is detected by steady state evaluator320. This cycle may operate continuously. In other words, control strategy switching module310can cause self-optimizing controller306to seek optimal manipulated variable to output variable relationships until the process302is in or is brought to a steady state. Once a steady state is detected and a control relationship between the manipulated variable and the control variable is learned by the self-optimizing controller306and stored in control parameter storage module322, the model-based controller308conducts control. When a condition is detected (e.g., a power outage, a restart, a significantly different setpoint, an unstable process condition, deviation from steady state boundaries, etc.), the method repeats with self-optimizing controller306again conducting its seeking and learning behaviors. During times when the model-based controller308is operating process302, the process components may advantageously be subjected to less energy loss and less equipment wear as compared to a self-optimizing controller that constantly oscillates the manipulated variable (and therefore process equipment) to seek optimal parameters.

While in some embodiments control strategy switching module310may only switch back to operation using self-optimizing controller306when a steady state is no longer active, in other embodiments the control strategy switching module310may periodically cause control to be switched back to the self-optimizing controller306from the model-based controller308. Operation by the self-optimizing controller306can be used to help determine whether a steady state still exists or can be used to determine whether performance of process302has changed. Operating self-optimizing controller306may allow control strategy switching module310to determine that process302performance has shifted or otherwise changed. In other words, the relationship that was originally learned between a manipulated variable and one or more output variables may no longer be true or optimal. In yet other embodiments, periodic control by self-optimizing controller306can be used to detect faults in the process302. For example, if a newly detected relationship between an optimal manipulated variable and the output variable indicates a significant change from a steady state or fault free state known to previously exist, the master control module318may cause a fault alert and/or send related fault information to supervisory controller332via network330. Such information may be used to display fault information or alerts to a user via an electronic display or other user interface device. The user may then be able to check into and resolve the fault rather than allowing control to be learned relative to a faulty state. Advantageously, however, periodic learning provided by self-optimizing controller306may allow relatively optimal process system performance given the fault. In a system which operates only on a fixed model, changed circumstances can result in an incorrect model and highly undesirable results. A model learned by systems and methods of the present application can be optimal given even undesirable circumstances.

Referring now toFIG. 4, a detailed flow chart of a method400for operating a process system is shown, according to an exemplary embodiment. Method400includes starting-up a controller (step402). Starting-up of the controller may include one or more variable initiation tasks, timer initiation tasks, feedback tasks, diagnostics tasks, or other control tasks. The start-up routine may include or be followed by causing self-optimizing control operation of the controlled process system (step404). According to varying exemplary embodiments, the self-optimizing control operation may be an extremum seeking control operation. Method400includes checking whether start-up is complete (step406). Checking whether start-up is complete can include determining whether a start-up timer has elapsed, checking for whether a set of post start-up conditions have been met, checking for whether a start-up routine has successfully completed, or otherwise. If start-up is completed, the method moves on to the next step. If start-up is not complete, the controller continues self-optimizing control operation until start-up is complete.

Method400further includes determining whether a steady state has been attained by the self-optimizing control or the process system that the self-optimizing control is controlling (step408). Determining whether a steady state has been attained by the self-optimizing control can include determining whether the manipulated variable is making steps or sinusoidal changes above a certain amplitude and/or frequency, determining whether the output variable is within a certain range, determining whether relationships identified during the self-optimizing control fit a post start-up model, or conducting any other control or decision task relevant in determining whether a steady state has been attained. If the system has not reached a steady state, the controller continues self-optimizing control operation until a steady state is attained.

When step408results in a determination that a steady state has been reached, the controller then records or updates one or more control relationships in memory (step410). Recording or updating of control relationships can continuously occur when self-optimizing control is operating the process system. For example, new manipulated variable to output variable relationships or values for describing the relationships may be updated in memory for every regular time period of the process controller or process being controlled. Updating in memory may include replacing previous variables, updating a moving average, or other tasks that may help the controller more accurately or reliably describe a relationship observed during the self-optimizing control process. In some embodiments, steps404through410can be considered a training period for the model-based control using a self-optimizing control loop as the training mechanism.

Method400further includes causing a switch to the model-based control for operation of the process system (step412). The switch from the self-optimizing control to the model-based control may be as described above with reference to control strategy switching module110shown inFIG. 1, as described above with reference to control strategy switching module310shown inFIG. 3, or completed by another control strategy switching module or mechanism.

When the switch to the model-based control is effected, method400can start or restart a periodic model update timer (step414). The periodic model update timer is used later in the method to determine whether to switch back to the self-optimizing control for a control model update. In varying alternative embodiments, method400may not utilize periodic model updating, step414, or a periodic model update timer. In other embodiments, the periodic model update timer may be user adjustable and set to zero to disable the feature. In an exemplary embodiment, the periodic model update timer may initially be set for a relatively small period of time (e.g., 30 minutes). If the model observed by the self-optimizing controller is determined to be relatively accurate in finding an accurate control model from self-optimizing control cycle to self-optimizing control cycle, the controller may be configured to automatically begin lengthening the periodic model update timer (e.g., in ten minute increments, in half-hour increments, in hour long increments, etc.). For example, if the model is substantially unchanged from one self-optimizing control cycle to another, a periodic model update timer that is initially set to 45 minutes may eventually allow the model-based control to operate for four hour periods of time before a “refresh” or update by the self-optimizing controller.

Model-based control using the recorded control relationship or relationships continues or begins at step416. As described above, the model-based control can be feedforward-based, feedback-based, an open loop control strategy, or another model-based control strategy.

When the model-based control is operating, the method includes determining whether the periodic model update timer has elapsed (step418). If the periodic update time has elapsed, the controller loops back to step404and again causes self-optimizing control to operate the process system and to record or update control models or relationships (at step410).

If the periodic model update timer has not elapsed, method400determines whether operation of the model-based control strategy has moved out of one or more control boundaries (step420). The control boundary may be or include a threshold value for the output variable, a threshold value for the manipulated variable, one or more other thresholds relating to a relationship between the manipulated variable and the output variable, one or more coefficients describing a relationship between the manipulated variable and the output variable, a performance index parameter threshold, or another control boundary suitable for determining whether the model operation has continued within a desired range or bounds. If the model operation is not outside of some boundary, then the model-based control using the recorded control relationships continues at step416.

When the model operation is determined to be outside of bounds at step420, the controller can then cause a switch to self-optimizing control operation (step422). After some period of time, the controller can then determine whether the self-optimizing control operation has reached a steady state (step424). If the self-optimizing control operation has not reached a steady state, then the self-optimizing control continues at step422. When a steady state has been determined at step424, method400proceeds to determine whether the recorded relationships are still valid (step426) (e.g., the relationships recorded at step410). Determining whether the recorded relationships are still valid can include comparing coefficient values of an equation describing the relationships, comparing a sensor-obtained measurement to a setpoint, or conducting a number of other comparing, computing, or logic tasks.

If the recorded relationships are determined not to be valid, then the controller determines whether one or more system faults have occurred (step428). Determining whether one or more system faults have occurred can include further evaluation of self-optimizing control states or relationships, performance indexes, measures sensor values, comparison of one or more variables to one or more rules, or other analysis, computing, or determining tasks. If a system fault is detected at step428, then the system reports the fault to the user and requests user input (step430). Simple inspection or cleaning of the device may resolve the fault or faults. If more complex fault analysis is required, the user may take the system offline and restart the controller and method at step402. Due to the learning capabilities of the system, the self-optimizing control operation can record new relationship models for any new system performance realities (e.g., at step410) prior to operation by a model-based control strategy. If the controller determines that there are not system faults that need to be reported to a user or otherwise addressed, the method loops back to step404and causes or continues self-optimizing control operation such that control relationship models are recorded or updated at step410.

If the recorded relationships are determined to be valid at step426, the controller may then update model operation bounds (step432). Step432can include widening the bounds or otherwise making step420less restrictive. In an exemplary embodiment, the bounds can be narrowed after step428(e.g., in situations where the model-operation was determined to be within the established bounds but the relationships were found to be invalid). Once the bounds are updated (e.g., widened, made less restrictive, etc.) in step432, method400loops back to step412, causing a switch to model-based control using the updated bounds. Adjusting the bounds to be more or less restrictive can advantageously adaptively the amount of time the system spends using self-optimizing control versus model-based control (e.g., by widening bounds in response to a determination that the model learned by the self-optimizing control is still good, the system can learn to operate the model-based control for a longer period of time before resorting back to self-optimizing control.

FIGS. 5A-5Crelate to a particular implementation for one or more control systems or processes described above, according to an exemplary embodiment. Cooling towers are used to remove heat from chilled water provided to chiller condensers. Additional explanation and diagrams for exemplary cooling tower systems are contained in U.S. application Ser. No. 12/777,097.FIG. 5Adepicts one model for determining tower airflow as a function of a chilled water load. Tower airflow may be computed as a linear function of the part load ratio (i.e., chilled water divided by design total chiller cooling capacity) with the following equation (“equation 1”):
Gtwr=1−βtwr(PLRtwr,cap−PLR) for 1.0<PLR<0.25
where
Gtwr=tower airflow divided by maximum airflow with all cells operating at high speed
PLR=chilled-water load divided by design total chiller plant cooling capacity (part-load ratio)
PLRtwr,cap=part-load ration (value of PLR) at which tower operates at its capacity (Gtwr=1)
βwr=slope of relative tower airflow (Gtwr) versus part-load ratio (PLR)

When chiller operation is below 25% of the full load, the tower airflow is ramped to zero as the load goes to zero according to (“equation 2”):
Gtwr=4PLR[1−βtwr,cap(PLRtwr,cap−0.25)] for PLR<0.25
The results of equation 1 or 2 are constrained between 0 and 1. The fraction of tower capacity may then be converted to fan speed, fan sequencing parameters, and other particular outputs by one or more other controllers, sequencers, or variable speed drives. Equations 1 and 2 above are described in greater detail in Chapter 41 of the 2007 ASHRAE Handbook of HVAC Applications at pages 41.12-41.15. Equations 1 and 2 are examples of open-loop or mode-based control strategies that may be switched to after learning parameters using an extremum seeking control strategy.

Referring now toFIG. 5B, an illustration of the relationship between the cooling tower fan power and the corresponding chiller's power is shown. As airflow increases, fan power increases but there is a reduction in the chiller power consumption due to a decreasing temperature of the chilled water provided to the condenser of the chiller. As is illustrated inFIG. 5B, a minimum (i.e., optimal) total power can be obtained by finding the right chiller power consumption and tower fan power consumption. U.S. application Ser. No. 12/777,097 describes systems and methods for using self-optimizing control to find the minimum total power in a system represented byFIG. 5B.

Equation 1 above for Gtwris an open loop model where PLRtwr,capis one of the inputs that drives tower airflow (Gtwr). PLRtwr,capmay be adjusted to change Gtwrfor a given PLR. Such an activity would move the airflow and therefore the total energy plotted inFIG. 5B.

Using the training data, parameters PLRtwr,capand βtwrmay be identified using a measurement, calculation, a least squares method, or another approach. For example, if a linear line does not fit the training data, then an alternative equation (e.g., quadratic) may be used to determine the relationship between the part load ratio and relative total airflow. Other methods such as artificial neural networks or non-parametric curve estimation methods could be used to estimate the optimal tower airflow from the part-load ratio. Other descriptors such as standard deviation of the errors from the curve fit may be calculated and stored.

Once the relationship between optimal tower airflow and optimal chilled water load (e.g., in terms of PLRtwr,capand/or βtwr) are determined, control of the cooling tower can be switched from the extremum seeking controller to an open loop model-based controller operating according to equations 1 and equation 2. PLRtwr,capand βtwrlearned during the extremum seeking control are used in equations 1 and 2. PLRtwr,capand βtwrmay be considered to be the result of learning a relationship between a manipulated variable (e.g., tower airflow) and an output variable (e.g., relative chilled water load) when the relative cooling tower airflow is at a value that minimizes the total power of the chiller and fan.

When control switches from the extremum seeking controller to equations 1 and 2, and the PLR is between PLRminand PLRmax, then equations 1 and 2 (e.g., a model-based controller implementing equations 1 and 2) continue controlling the cooling tower in a open loop control. If the PLR is less than PLRminor greater than PLRmax, then the training period may be repeated and the optimal relationship between tower airflow and power may be relearned, with the relationship being communicated to the mode-based controller at the end of the extremum control using updated values for PLRtwr,cap, βtwr, PLRmin, and PLRmax.

Due to equipment wear, system faults, or improper maintenance, the performance of the cooling towers and chillers may be time varying. Periodically (once per week), the extremum seeking control (e.g., a shortened version of the training activity) may be used to determine a new data set for the relative chilled water load (e.g., part load ratio).

In another embodiment, whether extremum seeking control for training is desired can be determined by a main control module by estimating the PLR from equation 1. If the actual optimal air flow ratio is significantly different than the estimated optimal airflow ratio, then the performance of the chiller and the cooling tower can be estimated to have changed. In an embodiment, the standard deviation of the residuals from the least squares method can be used to determine if there is a significant difference between the estimated and actual optimal airflow ratio. The operator should be informed of the change and extremum seeking can be reinitiated to retain the system.

Referring now toFIG. 5C, a block diagram of an HVAC system500is shown, according to an exemplary embodiment. The HVAC system500is shown to include a controller540that is generally configured to provide an airflow command to cooling tower501. Controller540includes a model-based controller542and a self-optimizing controller544. In an exemplary embodiment, model-based controller542is configured to operate the open-loop equations 1 and 2 listed above. Self-optimizing controller544may be configured to use extremum seeking control to seek a total airflow parameter that results in a minimum tower fan power plus chiller power expenditure.

Self-optimizing controller544may be used to find parameters PLRtwr,capand βtwrassociated with a near optimal relationship between airflow (i.e., the manipulated variable) and total power (i.e., the output variable). Parameters PLRtwr,capand βtwrcan be provided to the model-based controller (e.g., at the end of a training period) as described above.

Self-optimizing controller544is configured to control the speed of fan502by providing a control signal to fan motor503or to a variable speed drive504associated with fan motor503. Throughout this disclosure, any reference to controlling the speed of the cooling tower fan can be or include controlling the speed of the fan motor, providing an appropriate control signal to the fan motor's variable speed drive, or any other control activity that affects the cooling tower fan speed of cooling tower system505.

Self-optimizing controller544determines the fan speed by searching for a fan speed (e.g., an optimum fan speed) that minimizes the sum of the power expended by cooling tower fan system505and the power expended by chiller514(e.g., power expended by the chiller's compressor). The power demand of the chiller's compressor (and/or other components) is affected by the condenser water supply temperature—the temperature of the water supplied by cooling tower501to chiller514. Increasing the air flow of the cooling tower501(e.g., increasing the fan speed) provides a lower condenser water temperature, which reduces the chiller's power requirement (primarily the power expended by the chiller's compressor). Increasing the fan speed, however, causes an increase in tower fan power consumption. As shown inFIG. 5B, there is an optimal cooling tower air flow rate that minimizes the sum of the expended chiller power and the power expended by the cooling tower fan system.

Self-optimizing controller544receives an input517of power expended by cooling tower fan system505and an input515of power expended by chiller514(e.g., chiller514's compressor). Self-optimizing controller544implements an extremum seeking control strategy that dynamically searches for an unknown input (e.g., optimal tower fan speed) to obtain system performance (e.g., power expended by the cooling tower and the chiller) that trends near optimal. Self-optimizing controller544operates by obtaining a gradient of process system output (e.g., power expended by the cooling tower and the chiller) with respect to process system input (fan speed) by slightly perturbing or modulating the fan speed and applying a demodulation measure to the output. Self-optimizing controller544provides control of the process system (e.g., the fan speed and therefore the tower and chiller power demand) by driving the obtained gradient toward zero using an integrator or another mechanism for reducing a gradient in a closed-loop system.

Inputs506and515may be summed outside of self-optimizing controller544via summing block516to provide combined signal517(e.g., which may be representative of total power demand of tower fan system505and chiller514). In various other embodiments, self-optimizing controller544conducts the summation of summing block516. In either case, self-optimizing controller544can be said to receive inputs506and515(even if inputs506and515are provided as a single summed or combined signal517).

Chiller514is shown as a simplified block diagram. Particularly, chiller is shown to include a condenser, an evaporator, a refrigerant loop, and a compressor. Chiller514also includes at least one expansion valve on its refrigerant loop between the condenser and the evaporator. Chiller514can also include any number of sensors, control valves, and other components that assist the refrigeration cycle operation of chiller514.

A chilled fluid pump520pumps the chilled fluid through the loop that runs through the building (e.g., through piping522and524, through chiller514, and to one or more air handling units526). In the embodiment shown inFIG. 5C, the chilled fluid is supplied via piping522to an air handling unit526that is an economizer type air handling unit. Economizer type air handling units vary the amount of outdoor air and return air used by the air handling unit for cooling. Air handling unit526is shown to include economizer controller528that utilizes one or more algorithms (e.g., state based algorithms, extremum seeking control algorithms, etc.) to affect the actuators and dampers or fans of air handling unit526. The flow of chilled fluid supplied to air handling unit526can also be variably controlled and is shown inFIG. 5Cas being controlled by proportional-integral (PI) control530. PI control530can control the chilled fluid flow to air handling unit526to achieve a setpoint supply air temperature. Economizer controller528, a controller for chiller514, and PI control530can be supervised by one or more building management system (BMS) controllers532. BMS controller532can use BMS sensors534(connected to BMS controller532via a wired or wireless BMS or IT network) to determine if the setpoint temperatures for the building space are being achieved. BMS controller532can use such determinations to provide commands to PI control530, chiller514, economizer controller528, or other components of the building's HVAC system.

In an exemplary embodiment, self-optimizing controller544does not receive control commands from BMS controller532or does not base its output calculations on an input from BMS controller532. In other exemplary embodiments self-optimizing controller544receives information (e.g., commands, setpoints, operating boundaries, etc.) from BMS controller532. For example, BMS controller532may provide self-optimizing controller544with a high fan speed limit and a low fan speed limit. A low limit may avoid frequent component and power taxing fan start-ups while a high limit may avoid operation near the mechanical or thermal limits of the fan system.

While controller540is shown as separate from BMS controller532, controller540may be integrated with BMS controller532. For example, controller540may be a software module configured for execution by a processor of BMS controller532. In such an embodiment, the inputs of expended chiller power515and tower system fan power506may be software inputs. For example, software executed by BMS controller532may use model-based calculations to determine the expended power. The models may relate, for example, fan speed to power expended by cooling tower fan system505and, for example, compressor pump speed to power expended by chiller514. In yet other exemplary embodiments the inputs of expended power may be “real” (e.g., a current sensor coupled to the power input of variable speed drive504of cooling tower fan system505may be wired to an input of controller540or self-optimizing controller544, summing element516, or BMS controller532, and a current sensor coupled to the power input of the variable speed compressor motor may be wired to another input of controller540, summing element516, or BMS controller532).

Where air handling unit526is an economizer, one or more controllers as described herein may be used to provide for control of air handling unit526during one or more of the operational states of the economizer. For example, economizer controller528may include an extremum seeking controller or control module configured to utilize an extremum seeking control strategy to change the position of one or more outdoor air actuators or dampers. One or more of the systems and methods described with reference toFIGS. 1-4may be implemented in or by economizer controller528.

Other exemplary embodiments may include a configuration different than that shown inFIG. 5C. In such different configurations, for example, additional or different inputs or outputs may be used by controller540. For example, controller540may manipulate one or more variables of chiller514or pump518alone or in concert with one or more manipulated variables for cooling tower system505. Power expenditures of chiller514, pump518, cooling tower system505and/or other cooling system outputs may all be summed at element516. One or more of the manipulated variables may be adjusted by self-optimizing controller544to seek an optimal aggregate power expenditure. Accordingly, a single or multi-variable steady state relationship may be learned by self-optimizing controller544and used by model-based controller542after a switch from the self-optimizing controller to the model-based controller542occurs.