Abstract:
A controller device and a method for controlling a system that utilizes an adaptive mechanism to self-learn the system characteristics and incorporates this adaptive self-learning ability to predict a control parameter correctly to provide precise control of a system component.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Patent Application No. 61/377,331, filed Aug. 26, 2010, the disclosures of which are incorporated by reference herein. 
    
    
     BACKGROUND OF THE INVENTION 
     This invention relates in general to systems and in particular to a method for controlling such systems. 
     One example of a common system that needs control is a Heating, Ventilating and Air Conditioning (HVAC) system, which is actually a flow conducting system. A typical HVAC system includes either an expansion valve or a fixed orifice valve that modulates refrigerant flow from a condenser to an evaporator in order to maintain enough suction superheat to prevent any un-evaporated refrigerant liquid from reaching the system compressor. This is done by controlling the mass flow of refrigerant entering the evaporator so that it equals the rate at which it can be completely vaporized in the evaporator by absorption of heat. In the past, capillary tubes and thermostatic expansion valves have been widely used in refrigerating machines as refrigerant flow regulating devices. Now Electrically or Electronically driven Expansion Valves (EEVs) are commonly utilized and permit more advanced control. However, with this type of regulating device, it becomes necessary to choose a control algorithm. 
     Referring now to  FIG. 1 , there is shown a typical HVAC system  10  that includes two shell and tube heat exchangers that function as an evaporator  12  and a condenser  14 , respectively, and a reciprocating compressor  16 . As shown in  FIG. 1 , the evaporator  12  is used with a flow of water and antifreeze mixture as a secondary refrigerant fluid, whereas the condenser  14  is water cooled. The refrigerant fluid is vaporized inside tubes disposed within the evaporator  12 , while its condensation occurs outside the tube bundle in the condenser  14 . The refrigerant travels about a closed loop that is labeled  18 . The flow of the refrigerant is controlled by an EEV  20 . Air to be cooled is forced through the evaporator  12  by a blower  22  connected to an intake vent  24 . The cooled air is discharged through a discharge vent  26 . Typically, ductwork (not shown) routes the air before and after passage through the evaporation  12 . 
     In the past, the EEV  20  has been an electronic valve controlled by the displacement of a magnet in a magnetic field created by a coil. The displacement of the magnet induced a linear movement of the needle and, consequently, a proportional throttling of the valve. The EEV  20  typically had a precise positioning control loop with a stroke resolution of up to 1:1000 and a positioning time that may be as fast as less than one second. The control signal needed to operate this valve was usually obtained by a package that contained a PID controller and a pressure and temperature sensor. More recently, micro-valve arrays, such as those available from DunAn Microstaq, Inc., of Austin, Tex., have been substituted for the EEVs to achieve energy savings through more precise and rapid control of superheat. 
     The differences among the micro-valve systems are based upon geometry, actuation mechanisms, membrane material, flow path design and fabrication techniques. Among these differences, the actuation mechanism is the most commonly used property to classify the micro-valve systems. For multi channel applications, micro-valve arrays are used. With micro-valve arrays, more precise flow is achievable since flow regulation is easier with an array than one valve. Another advantage of micro-valve arrays is they also have a relatively low cost. 
     Micro-valves may be micro-machined from silicon; however, other materials also may be used. Silicon has many advantages for MEMS (micro-electrical mechanical system) applications. It is one of very few materials that can be economically manufactured in single crystal substrates. Its crystalline nature provides significant electrical and mechanical advantages. Besides, silicon is abundant and can be produced in high purity and is an elastic and robust material. Good sealing properties make silicon the most used material for micro-valve applications. In particular, spin-on silicone rubber is very attractive for micro-valve applications since it has low modulus, high elongation, good compatibility with IC processes, and good sealing properties. Glass, polymers, and thin metal films such as Ni, Ti, Fe, and Cu may also used in micro-valve fabrication. 
     The use of micro-valves has enabled more precise operation of the control systems for HVAC systems. However, difficulty of controlling the superheat in HVAC systems continues to be experienced as thermal load on the HVAC system changes due to changing environmental conditions. Accordingly, it would be desirable to provide a stable control algorithm for micro-valves in HVAC systems that would compensate for thermal load changes. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic diagram of a typical HVAC system. 
         FIG. 2  is a schematic diagram for a HVAC system controller that is in accordance with the present invention. 
         FIG. 3  is a state diagram for the HVAC system controller shown in  FIG. 2 . 
         FIG. 4  is a flow chart for a control algorithm utilized in the HVAC system controller shown in  FIG. 2 . 
         FIG. 5  is a flow chart for a modeling subroutine that is included in the control algorithm shown in  FIG. 4 . 
         FIG. 6  is a flow chart for a stability subroutine that is included in the control algorithm shown in  FIG. 4 . 
         FIG. 7  is a graph and a chart illustrating an application of the HVAC system controller shown in  FIG. 2  to an HVAC system. 
         FIG. 8  is two graphs illustrating an application of the HVAC system controller shown in  FIG. 2  to an HVAC system. 
         FIG. 9  is two graphs illustrating an application of the HVAC system controller shown in  FIG. 2  to an HVAC system. 
         FIG. 10  is another graph illustrating an application of the HVAC system controller shown in  FIG. 2  to an HVAC system. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
     The present invention is directed toward the application of a predictive controller that is combined with a self learning mechanism to provide precise control of fluid flow, and further toward an associated method of control of a valve. In order to describe the invention, the control of superheat in an HVAC system with one or more microvalves is used as an illustrative example. However, it will be appreciated that the predictive controller system and method for control described and illustrated herein are intended to be utilized to control other valves than microvalves and are not limited to use in HVAC systems. Indeed, the predictive controller system and method for control may be used in the control of electronically controlled valves in a variety of applications, including flow control and pressure control. With regard to HVAC systems, one application could be to help ensure that the superheat set point is appropriate for the system using an intelligent self-correcting algorithm. 
     Referring again to the drawings, there is illustrated, in  FIG. 2 , a two stage HVAC system controller  30  that is in accordance with the present invention. As mentioned above, the invention is illustrated as being applied to an HVAC system, and a portion of the HVAC system shown in  FIG. 1  is again shown in  FIG. 2 . Any components in  FIG. 2  that are similar to components shown in  FIG. 1  have the same numerical identifiers. The first stage, or phase I, of the HVAC controller  30 , which is labeled  32 , is utilized to ensure a stable response around a set point and includes a predictive adaptive controller. The second stage, or phase II, of the HVAC controller  30 , which is labeled  34 , calculates a set point for the system as a function of the system operating data. 
     As the HVAC system runs, the HVAC system controller  30  periodically collects system input data, such as, for example, a command signal going to the valve  20  (which can be determined from the output of the valve  20  as shown in  FIG. 2 ) and output data, such as, for example, superheat temperature at the exit of the evaporator  12  of the HVAC system  10 . The system input data is supplied to a self learning, or adaptive, mechanism  35  that is operable to determine an optimal set point, i.e., a self adjusted set point that also is stable. The output from the self-learning mechanism  35  is supplied to an HVAC system model  36  that estimates operating parameters for the controlled system, such as, for example, time constant and system gain. The gains and other output from the system model  36  are tuned in the box labeled  37 . The HVAC system controller  30  also generates a set point value, as determined by a set point calculation labeled  38  as a function of the superheat temperature. From the tuned estimated model output parameters and the calculated set point, a command signal generator  40  predicts the future response of the system and generates a corresponding command signal that is sent to the valve  20 . The HVAC system controller  30  illustrated in  FIG. 2  generates a Pulse Width Modulated (PWM) waveform signal with a variable duty cycle that is applied to the valve  20  as a control command. However, the invention also contemplates using other control commands than the PWM shown. 
     To accomplish the self learning processes, a well known Root Least Square (RLS) method is used in block  35 . To perform the predictive mechanism, predictive functional control also is used in block  35 . The main goal of the control algorithm is to ensure that a stable condition exists irrespective of load conditions. It has been observed that, when a control parameter, such as a superheat set point, is fixed, the system stability can be affected due to load conditions. For example, in the case of the HVAC system  10  illustrated in  FIGS. 1 and 2 , it is desired to maintain a stable, i.e., a relatively constant, amount of superheat in the refrigerant exiting the evaporation  12 , regardless of changing load and environment conditions, so as to maintain optimum efficiency. Instead of all superheated vapor coming out of the evaporator  12  at a constant temperature, saturated vapor, or even slugs of liquid refrigerant may be passing a temperature gauge at the evaporator outlet, causing varying temperature indications. But if the temperature is constant, it may indicate that insufficient refrigerant is being supplied to the evaporator, so all is not only evaporated, but may be superheated too much. Superheated vapor removes less heat (British Thermal Units) from the air side of the evaporator than does a phase change from liquid, resulting in less cooling of the air in the evaporator. Therefore, it is desirable to reduce the superheat set point to reduce superheat, increase refrigerant flow, and increase efficiency. Accordingly, the second phase  34  of the HVAC system controller  30  is utilized to identify stability issues and address them to prevent the predictive-adaptive controller  30  from working in an unstable state. The possible system condition is identified by recognizing the current system state and then taking appropriate action based on the state. Below are definitions of each state that are utilized by the controller:
         UNSTABLE: ((Number of oscillations around set-point&gt;Threshold) AND (Percentage of seconds outside the Set point range (+−1° C.));   DRIFT: Number of seconds outside the Set-point range (+−1° C.). Will be reset as soon as it enters the range;   INIT: Initialized state where the system has booted and we are ready to run the predictive adaptive algorithm;   OFF: System is OFF; and   STABLE: If the system is not drifting and unstable, then it is in a stable state.       

     The HVAC system controller  30  contemplates that both phases  32  and  34  run continuously, but at different rates, with the first phase  32  running a higher rate than the second phase  34 . Thus, for example, the first phase  32  may have iterations of one second while the second phase  34  may have iterations of five minutes. It will be appreciated that the above iterations times are meant to be exemplary and that the invention may be practiced with other iteration time periods. 
     The HVAC system controller  30  utilizes an algorithm that always starts in an ‘INIT’ state which is basically an initial reset-state of our algorithm. From the INIT-state data is collected over a time-period to determine the next state, and appropriate action is taken to transfer the system to the desired state. If it is determined that the system is in an ‘UNSTABLE’ state, the algorithm will increase the superheat set point to account for the instability. If it is determined the system is in a stable state for a long period of time, then the superheat setpoint is reduced to improve efficiency. As will be explained below, the stability of the system is determined by monitoring oscillations, variation and drift of the superheat temperature about the set point. The state machine is re-initialized when the system is turned OFF to assure that the HVAC system controller  30  is in a starting condition when the system is turned back ON. The interrelations between the states are illustrated by the state diagram shown in  FIG. 3 . 
     Turning now to  FIG. 4 , there is shown a flow chart for an algorithm  50  for controlling a flow valve that is in accordance with an aspect of our novel method. The algorithm  50  is entered through block  52  and proceeds to functional block  54  where the algorithm, and the associated system, is initialized by setting all variables to stored initial values and setting all internal timers to zero. Included in the initialization would be an initial control parameter set point value, which for the HVAC system shown in  FIG. 2  would be the superheat set point. The algorithm continues to functional block  56  where system data is collected. For the system shown in  FIG. 2 , the collected data would include the temperature and pressure at the outlet of the evaporator  12  that would allow determination of the degrees of super heat. The algorithm then advances to decision block  58 . 
     In decision block  58 , it is determined whether or not the system  10  is active, or ON. For the system shown in  FIG. 2 , the determination may consist of checking the motor compressor  16  for operation. If the system  10  is OFF, the algorithm  50  transfers to functional block  60  where the control command is set to zero. From block  60 , the algorithm next proceeds to functional block  61  to generate a command signal for the valve  20 . In the preferred embodiment, the command signal is a Pulse Width Modulated (PWM) signal with a variable duty cycle, with the duty cycle adjusted in functional block  61 . Thus, for the zero command signal called for in functional block  60 , the duty cycle of the PWM would be set to zero in functional block  61 . 
     The algorithm then continues to decision block  62  and determines whether or not it should continue. If the algorithm is to continue, it transfers back to functional block  56  to collect more data. If, in decision block  62 , the algorithm is not to continue, it exits through block  66 . 
     Returning now to decision block  58 , if it is determined that the system  10  is ON the algorithm  50  transfers to decision block  68 . In decision block  68 , an internal timer is compared to a second time limit T2, which is the length of time required to obtain enough data to determine whether or not the control parameter is stable. If the timer has reached the second phase time limit, the algorithm transfers to a stability subroutine  70  which is shown in detail in  FIG. 6 . As shown in  FIG. 4 , the algorithm  50  exits the subroutine  70  and proceeds either to functional block  54 , where the system  50  is re-initialized with new parameters determined by the stability subroutine, or to functional block  72 , where another subroutine computes an update for the adaptive predictive model  36 , as shown in detail in  FIG. 5 . If, in decision block  68 , the timer has not reached the second phase time limit, the algorithm transfers to decision block  74 . 
     In decision block  74 , the internal timer is compared to a first time limit T1, which is the length of time required to obtain enough data to run the compute model subroutine. If the timer has reached the first time limit T1, the algorithm transfers to functional block  72  where an update for the adaptive predictive model is computed from the system time constant and system gain settings. If the timer has not reached the first time limit T1, the algorithm transfers to functional block  56  to collect the next iteration of data. Typically, the algorithm computes an update for the model  36  during each iteration; however, the use of the first time limit T1 and decision block  74  provide an option of collecting data over several iterations before updating the adaptive predictive model  36 . Once the adaptive predictive model has been updated, the algorithm  50  continues to functional block  61  to generate a command signal for the valve  20 . After generating the command signal, the algorithm proceeds to decision block  62  where the algorithm determines whether or not to continue. If the decision is to not continue, the algorithm exits through block  66  while, if the decision is to continue, the algorithm returns to functional block  56  and continues as described above. 
     Referring now to  FIG. 5 , there is shown a flow chart for the subroutine  72  that is utilized by the algorithm for updating the adaptive predictive system model  36 . As shown in  FIG. 5 , the subroutine  72  is entered from decision block  74  when the first time limit T1 is reached and the algorithm proceeds to decision block  82 . In decision block  82 , the current control parameter is compared to an acceptable control parameter range. If the control parameter is within the acceptable range, the subroutine transfers to functional block  84  where the model is frozen and hence the controller gains are not updated. The subroutine  72  then continues to functional block  61  in the algorithm  50  and the algorithm  50  continues as described above. If, however, in decision block  82 , it is determined that the control parameter is not within the acceptable control parameter range, the subroutine  72  transfers to functional block  86  where the model is computed with possible parameter value input from the stability subroutine  70 . The subroutine  72  then proceeds to functional block  88  where the controller gains are tuned in accordance with the results obtained from the system model computation in functional block  86 . Once the controller gains have been tuned, the subroutine  72  is exited to functional block  61  in the algorithm  50  and the algorithm  50  continues as described above. 
     Referring now to  FIG. 6 , there is shown a flow chart for the subroutine  70  that is utilized by the algorithm for updating the adaptive predictive model  36  with regard to control parameter stability. The subroutine  70  is entered when decision block  68  transfers to decision block  90  upon the timer exceeding the second time limit T2. In decision block  90  the drift of the control parameter, which is the superheat setpoint for the system  10  shown in  FIG. 1 , is compared to a drift threshold. If the setpoint exceeds the drift threshold, drift is not OK and the subroutine  70  transfers to decision block  92  where the duration of time that the control parameter has exceeded the threshold is checked. If the duration is greater than twice the second time limit T2, that is, two iterations of the time period for checking stability, the subroutine transfers to functional block  94  where the system model is recalibrated. The subroutine  70  then exits by returning to functional block  54  where the system is reinitialized, although with a recalibrated system model. If, on the other hand, the duration of time that the control parameter has not exceeded two iterations of the time period for checking stability, in decision block  92 , the subroutine  70  is exited by a transfer directly to functional block  54  where the system is reinitialized, but without a recalibrated system model. 
     Returning now to decision block  90 , if the drift is OK, the subroutine  70  transfers to decision block  96  where the number of oscillations of the control parameter about the set point are compared to an oscillation threshold. If the number of oscillations during the second time limit T2 exceeds the oscillation threshold, it is an indication that the control parameter is unstable, and the subroutine  70  transfers to functional block  98 , where the control parameter is increased and the model and timer are reset. The subroutine  70  then continues to functional block  100  where an unstable state is set, typically by setting a flag. The subroutine  70  than returns to the main algorithm via functional block  72 . 
     If, in decision block  96 , the number of oscillations during the second time limit T2 does not exceeds the oscillation threshold, the subroutine transfers to decision block  102 , where the system is checked with regard to being in a stable state. If it is determined that the previous state was stable, the subroutine  70  transfers to functional block  104  where the control parameter is decreased by an incremental amount. The subroutine continues to functional block  106  where a stable state is set, typically by setting a flag, and then to the main algorithm via functional block  72 . If, in decision block  102 , it is determined that the system previous state is not a stable state, the subroutine transfers to decision block  108  where the timer is compared to a third time limit T3, that is greater than the second time limit T2. If the timer has not reached the third time limit T3, the subroutine returns to the main algorithm via functional block  72 . If, on the other hand, the timer has reached the third time limit T3, the subroutine transfers to functional block  104  and continues as described above. 
     It will be appreciated that the flow charts shown in  FIGS. 4 through 6  are intended to be exemplary and that the invention also may be practiced with algorithms other than those shown in the figures. 
     EXAMPLE 
     The control algorithm of the present invention was implemented in a superheat controller in a testing program. A commercially available three ton HVAC unit and a 1.3 ton HVAC unit were used in testing. The superheat controller included microvalves to control flow of refrigerant into an evaporator. The system parameters and the predictive control gain were initialized to arbitrary values. The sampling time was one second with the controller updating the controller output with a valve command every second. The controller was able to control the HVAC unit superheat at different set points precisely with different load conditions as shown in  FIGS. 7 through 10 . 
     The advantages of an electronic superheat controller are numerous. The evaporator is always optimally filled with refrigerant. Even with large load variations, which mean an extremely wide range of partial-load operating conditions, exactly the right amount of refrigerant can be injected. This is done by constantly sensing the actual superheat value in the evaporator by means of a pressure transducer (labeled “P” in  FIG. 1 ) and a very sensitive temperature sensor (labeled “T” in  FIG. 1 ) and conveying this information to the controller in near real time. 
     With this information, the controller can act to achieve an optimally low superheat level. This adaptive regulation of refrigerant injection leads to optimal utilization of the evaporator and thus, to the highest possible evaporating pressure that can be achieved in the system concerned. This not only results in higher Coefficient Of Performance (COP) values, but also leads to energy savings because the COP value is equal to the cooling capacity divided by power consumption. The predictive controller system and method of this invention provide constant superheat optimization because the superheat always adjusts to a minimum stable signal of the evaporator, which reliably prevents any signal drift into an instable region. 
     The predictive controller system and method of this invention provide the following specific advances over prior art control systems:
         Eliminate the operator need for tuning HVAC controller gains as is the case for conventional controllers used with electronic valves after initial configuration.   Difficulty of controlling the superheat for HVAC system as the system thermal load on the changes due to environmental conditions.   May be used in controlling the superheat for HVAC systems that include variable speed compressors that have slow time constants.   Enhancing HVAC system efficiency by running the HVAC system near to the minimum stable superheat line for the system.       

     While the predictive controller system and method of this invention provide have been illustrated and described above for a microvalve, or an array of microvalves, included in a HVAC system, it will be appreciated that the predictive controller system and method described herein also may be practiced for the control for other types of electronically controlled expansion valves that are included in systems other than a HVAC system or with other types of non-regulating valves. Thus, the invention also may be utilized to control non-microvalve type valves and may used to control electronically controlled valves in a variety of applications, including pressure control and flow control application. 
     In summary, an aspect of this disclosure deals with a method for controlling a system component. In a first step at least one system component that is operational to control an operating parameter of a system is provided. Next, at least one control parameter for the system component is sensed and the stability of the control parameter is determined. Then, a model for operation of the system that includes the control parameter is developed and utilized to tune a predictive controller, the predictive controller generating a control command for the system component, the control command including at least one operating parameter for the system component. 
     Another aspect of this disclosure deals with a device for controlling a system that includes at least one system component that is operational to control an operating parameter of the system and at least one sensor mounted within the system with the sensor operative to sense a control parameter for the system component. The device also includes a controller connected to the system component that is operative to monitor the at least one control parameter and to determine the stability of the control parameter. The controller also is operative to adjust the control parameter, as needed, as a function of the stability determination and to develop a model for operation of the system that includes the control parameter. The controller is further operative to utilize the system model to tune a predictive controller with the predictive controller being operative to generate a control command for the system component, where the control command including at least one operating parameter for the system component. 
     In accordance with the provisions of the patent statutes, the principle and mode of operation of this invention have been explained and illustrated in its preferred embodiment. However, it must be understood that this invention may be practiced otherwise than as specifically explained and illustrated without departing from its spirit or scope.