Proportional Integral and Derivative (PID) controller is by far the most popular feedback controller in the automatic control field. In industrial processes the. A digital adaptive PID controller algorithm which contains recursive identification is presented in this paper. It is performed for three types of discrete model. How can I link this model with the adaptive PID control law, considering that neither pole placement technique nor Model Reference Adaptive control can be.
|Author:||Dr. Kristopher Grady|
|Published:||25 February 2014|
|PDF File Size:||26.80 Mb|
|ePub File Size:||32.60 Mb|
|Uploader:||Dr. Kristopher Grady|
However, the control of the output voltage of SOFCs is quite challenging because of the strong nonlinearity, limited fuel flow, and rapid variation of the load disturbance. Nowadays, proportional-integral-derivative PID controllers are commonly utilized in industrial control systems for their high reliability adaptive pid controller simplicity.
adaptive pid controller However, it will lead to overshoot and windup issues when used in the wide-range operation of SOFCs. In most of the real applications, the controlled system has parameters which slowly vary or are uncertain. Thus, PID gains must be adapted to cope with such changes.
RLS algorithm is used to update the PID gains in real time as system operates to force the actual system to behave like a desired reference model. Computer simulations are given to demonstrate the effectiveness of the proposed APID controller on SISO stable and unstable systems considering the adaptive pid controller of changes in the systems parameters.
Introduction A challenging problem in designing a PID controller is to find its appropriate gain values i. Moreover, in case where some of the system parameters or operating conditions are uncertain, unknown, adaptive pid controller varying during operation, a conventional PID controller would not change its gains to cope with the system changes.
Therefore a tuning method is needed.
Various PID controller tuning techniques have been reported in the adaptive pid controller. It is classified into two groups, offline tuning methods as Zeigler-Nichols method and online tuning methods or adaptive PID. APID can tune the PID gains to force the system to follow adaptive pid controller desired performance even with the existence of some changes in system characteristics [ 2 ].
Adaptive control has been commonly used during the past decades specially the model reference adaptive control MRAC. Its objective is to adapt the parameters of the control system to force the actual process to behave like some given ideal model which is demonstrated in [ 34 ].
Adaptive PID Control of Nonlinear Systems
There are two main categories of adaptive control. It starts with controlled adaptive pid controller identification and then uses those estimated parameters to design the controller as presented in [ 5 — 7 ]. This is more practical than indirect method.
It uses a parameter estimation method to get the controller parameters directly the same as introduced in [ 89 ]. An adaptive PID controller is presented in [ 10 ] using least square adaptive pid controller which is an offline parameter estimation method.