Abstract:
A method for controlling a controlled process in response to an input signal and a disturbance signal includes modeling the controlled process in a process model; controlling the process model by a first controller; isolating the first controller from the disturbance signal so that the first controller may be designed for an optimal response to the input signal; driving the first controller by a first drive signal proportional to the difference between the input signal and a process model output signal; isolating a second controller from the input signal so that the second controller may be designed for an optimal response to the disturbance signal; and driving the second controller by a second drive signal proportional to difference between a process output signal and the process model output signal.

Description:
This is a Divisional Application of U.S. patent application Ser. No. 10/789,221, filed on Feb. 27, 2004, which matured into U.S. Pat. No. 6,959,218, which is a Divisional Application of U.S. patent application Ser. No. 09/531,057, filed on Mar. 20, 2000 and which matured into U.S. Pat. No. 6,721,608, the entire disclosures of which are incorporated herein by reference. 

   FIELD 
   The present invention relates generally to control systems, and more particularly to process control systems in a two degree of freedom system. 
   BACKGROUND 
   A process control system implements a controller to shape the response of a process to an input signal. The control system can add gain, time varying properties, frequency components, or a combination of these characteristics to the process signal. By properly choosing these characteristics, the control system can stabilize the response of the process, determine overshoot, set acceptable error bounds and satisfy other performance criteria. 
   A two degree of freedom controller is generally implemented in a two degree of freedom system. Such a two degree of freedom system could consist of a setpoint and a disturbance. Within this system, the controller should track the setpoint and reject any disturbances. Controllers of this type, for example, include the precompensator  10  of  FIG. 1 . 
   The precompensator  10  of  FIG. 1  includes a prefilter  12  and a load controller  14 . These two control elements  12  and  14  shape a process input  16  for a process  18 . The prefilter  12  shapes a prefilter response  20  to an input variable  22 . The load controller  14  shapes the input  16  to the process  18  based on the prefilter response  20  and a process state  30  that is feedback for the system. The process state  30  is altered by a second variable  32  and the transfer function  36  of the second variable  32 . 
   In the configuration of the precompensator  10 , the load controller  14  must shape the process input  16  based in part on the prefilter response  20 . Any inaccuracies from error in the prefilter  12  are propagated through the load controller  14 . 
   SUMMARY 
   A method for controlling a controlled process in response to an input signal and a disturbance signal comprises modeling the controlled process in a process model; controlling the process model by a first controller; isolating the first controller from the disturbance signal so that the first controller may be designed for an optimal response to the input signal; driving the first controller by a first drive signal proportional to the difference between the input signal and a process model output signal; isolating a second controller from the input signal so that the second controller may be designed for an optimal response to the disturbance signal; and driving the second controller by a second drive signal proportional to difference between a process output signal and the process model output signal. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  is a schematic diagram of a prior art two degree of freedom control system; 
       FIG. 2  is a schematic diagram of a two degree of freedom control system comprising a preferred embodiment of the present invention; 
       FIG. 3  is a model referenced adaptive control system that includes the preferred embodiment of the present invention; and 
       FIG. 4  is a self-tuning adaptive control system that includes the preferred embodiment of the present invention. 
   

   DETAILED DESCRIPTION 
   A control structure  50  comprising a preferred embodiment of the present invention is shown in  FIG. 2 . The control structure  50  comprises a first controller  52 , a second controller  54 , and a process model  56 . These three components of the control structure  50  control a system process by regulating a process  58  with a process control signal  60  based on values of a first variable, C,  62  and process feedback. The process feedback is the sum of a second variable, L,  64  and a partial process output  66 . The second variable  64  is an external component to the system process to affect the process output  68 . 
   The first controller  52  and the process model  56  are located in a partitioned feedback loop  70 . Within the partitioned feedback loop  70 , the first controller  52  and the process model  56  are part of the forward path of the partitioned loop  70 . A feedback signal  72  is a predicted process output that is fed back to the first controller  52  from the process model  56 . The first variable  62  is the input of the partitioned feedback loop  70 . A first difference junction  74  calculates the difference between the first variable  62  and the predicted process output  72 . The output from the first difference junction  74  is a predicted error  78  of the process  58 . The transfer function, G C1 , of the first controller  52  receives the predicted error  78  as an input and outputs an idealized control signal  80 . The idealized control signal  80  is the input for the process model  56 . The process model transfer function, G P *, takes the idealized control signal  80  as an input and generates the predicted process output  72 . 
   The second controller  54  is located on a main loop  90  of the control structure  50 . The second controller  54  is parallel to the first controller  52 . The second controller  54  feeds a control signal into the process  58 . A feedback signal  92  is the value of the process output  68 . A second difference junction  100  calculates the difference between the first variable  62  and the measured output  92 . The output from the second difference junction  100  is fed into a third difference junction  102 . The third difference junction  102  calculates the difference between the output of the second difference junction  100  and the predicted error  78  from the partitioned feedback loop  70 . 
   The transfer function G C2 , of the second controller  54  manipulates the output of the third difference junction  102  to generate a second control signal  110 . A first summing junction  120  sums the second control signal  110  with the idealized control signal  80  from the partitioned feedback loop  70 . The output of the first summing junction  120  is the process control signal  60  for the process  58 . The partial process output  66  is the result of the transfer function, G P , of the process  58  responding to the process control signal  60 . 
   The second variable  64  acts upon the process system through a transfer function G L  in a load process  126 . The output of the load process  126  is a load output  128 . The load output  128  is summed with the partial process output  66  by a second summing junction  130 . The output of the second summing junction  130  is the process output  68 . The second variable  64  thus adds a disturbance to the process output  68 . 
   As can be seen by following the signals through the block diagram, the first variable  62  is shaped by the first controller  52  when the process model  56  matches the process  58 . The difference junctions in the loops  70  and  90  isolate the second controller  54  from the first variable  62 . The input to the second controller  54  then consists of the difference between the predicted process output  72  and the feedback of the process output  68 . This difference is the value of the load disturbance created by the second variable  64  when the process model  56  matches the process  58 . 
   The partitioned feedback loop  70  is isolated from the second variable  64 . No signal is received in the partitioned feedback loop  70  from the main loop  90 . The first controller  52  is isolated from any input from the second variable  64 . Since each controller  52  and  54  is isolated from one of the variables  62  and  64 , each controller can be independently designed for the desired response to a single variable. 
   The performance of the process model  56  can be measured by the response of the second control signal  110  to a change in the first variable  62 . A change in the first variable  62  will not cause the second control signal  110  to change if the process model  56  matches the process  58 . If the process model  56  does not match the process  58 , the second control signal  110  will vary. The second control signal thus is a measure of fitness of the process model  56  to the process  58  and serves as an indicator to the need to adjust the process model  56  to more correctly model the process  58  as the process  58  changes. 
   The structure  50  can also be examined analytically by examining the closed loop transfer function. The closed loop transfer function for the control structure  50  is given by: 
   
     
       
         
           R 
           = 
           
             
               
                 [ 
                 
                   
                     
                       
                         G 
                         C2 
                       
                       ⁢ 
                       
                         G 
                         P 
                       
                     
                     
                       1 
                       + 
                       
                         
                           G 
                           C2 
                         
                         ⁢ 
                         
                           G 
                           P 
                         
                       
                     
                   
                   + 
                   
                     
                       
                         ( 
                         
                           
                             G 
                             C1 
                           
                           - 
                           
                             G 
                             C2 
                           
                         
                         ) 
                       
                       ⁢ 
                       
                         G 
                         P 
                       
                     
                     
                       
                         ( 
                         
                           1 
                           + 
                           
                             
                               G 
                               C2 
                             
                             ⁢ 
                             
                               G 
                               P 
                             
                           
                         
                         ) 
                       
                       ⁢ 
                       
                         ( 
                         
                           1 
                           + 
                           
                             
                               G 
                               C1 
                             
                             ⁢ 
                             
                               G 
                               P 
                               * 
                             
                           
                         
                         ) 
                       
                     
                   
                 
                 ] 
               
               ⁢ 
               
                 ( 
                 C 
                 ) 
               
             
             + 
             
               
                 
                   G 
                   L 
                 
                 
                   1 
                   + 
                   
                     
                       G 
                       C2 
                     
                     ⁢ 
                     
                       G 
                       P 
                     
                   
                 
               
               ⁢ 
               
                 ( 
                 L 
                 ) 
               
             
           
         
       
     
   
   From this closed loop transfer function, it can again be shown that when the process model  56  matches the process  58 , or G P =G P *, the closed loop transfer function reduces to: 
   
     
       
         
           R 
           = 
           
             
               
                 
                   
                     G 
                     C1 
                   
                   ⁢ 
                   
                     G 
                     P 
                   
                 
                 
                   1 
                   + 
                   
                     
                       G 
                       C1 
                     
                     ⁢ 
                     
                       G 
                       P 
                     
                   
                 
               
               ⁢ 
               
                 ( 
                 C 
                 ) 
               
             
             + 
             
               
                 
                   G 
                   L 
                 
                 
                   1 
                   + 
                   
                     
                       G 
                       C2 
                     
                     ⁢ 
                     
                       G 
                       P 
                     
                   
                 
               
               ⁢ 
               
                 ( 
                 L 
                 ) 
               
             
           
         
       
     
   
   wherein each controller  52  and  54  acts upon only one of the input variables  62  and  68 . The first controller  52  shapes a response to the first variable  62  and the second controller  54  shapes a response to the second variable  64 . 
   Since each of the controllers  52  and  54  in the control structure  50  is individually set to a variable, the control structure  50  can use high performance controllers to shape the response to the input variables  62  and  64 . One such use of this control structure  50  is in a system where the variables are a set point and a load disturbance. The set point variable is a variable which is the desired value of the process output  68 . A load disturbance is an unwanted input to the system that may or may not be measured but is undesirable. 
   The object of the control structure  50  would then be to match the set point and reject the load disturbance. The controller  52  associated with the set point variable would be tuned to adjust the process output  68  to the new value of the set point based on specific performance criteria for the system. For instance, it may be important to avoid overshoot and to have a rise time that is prescribed to be relatively fast for this set point change. The load rejection performed by the other controller  54  can be tuned to a different set of performance criteria. The transfer function of the second controller  54  can be chosen based on properties of the load and the desired performance criteria of the load rejection. For instance, overshoot is a particularly undesirable response to a disturbance in many systems. These distinct performance measures may not be attainable in a control system where both set point and load disturbances are routed through a single controller. 
   In the control structure  50 , the controllers  52  and  54  are initially tuned for performance based on the modeled properties of the process  58  and the load process  128 . The parameters of the transfer functions G C1  and G C2  as well as the order of these transfer functions are chosen to make the control signals  80  and  110  sum to the desired process control signal  60  to produce a desired process output  68 . More robust designs for the control system would allow the transfer functions G C1  and G C2  of the controllers  52  and  54  to be self-tuned by techniques incorporated in controllers such as a model referenced adaptive controller or a self-tuning adaptive controller. 
   A control structure  150  of  FIG. 3  incorporates the control structure  50  in a model referenced adaptive controller. In this control structure  150 , the difference between the predicted process output  72  and the process output  68  is taken in a difference junction  154 . The difference junction  154  passes the difference to a parameter adjustment algorithm  160 . The parameter adjustment algorithm  160  adjusts the parameters of the transfer function G C2  of the second controller  54 . The magnitude of the adjustment is based on the difference between the predicted process output  72  and the process output  68 . In this control structure  150  the second controller  54  is tuned while the system is operating. 
   A control structure  200  of  FIG. 4  incorporates the control structure  50  in a self-tuning adaptive controller. The control structure  200  comprises a parameter estimation block  204  and a controller design block  208 . The parameter estimation  204  receives input from the measured variable  68  and the process control signal  60 . The parameter estimation block  204  adjusts the parameters for the process model  56  and a set of parameters that are passed to the controller design block  208 . The controller design block  208  takes the input from the parameter estimation block  204  to adjust the parameters of the transfer functions G C1  and G C2  of the first and second controllers  52  and  54 . In this control structure  200  both controllers  52  and  54  and the process model  56  are tuned while the system is operating. 
   Partitioned control structure can also be implemented in a multiple input/multiple output (MIMO) system. In such a system, inputs such as the first and second variables  62  and  64  would be introduced as a vector to the control structure. The output  68  would also be a vector. Within the control structure, the transfer functions could be a matrix of functions. The process model  56  would include a model for how the process  58  would react to each input in the input vector. 
   The invention has been described with reference to a preferred embodiment. Those skilled in the art will perceive improvements, changes, and modifications. Such improvements, changes, and modifications are intended to be within the scope of the claims.