Sequential Optimization of Industrial Control Systems Using Adaptive Optimal Control
Date Issued
2011
Date
2011
Author(s)
CHANG, CHIN-TANG
Abstract
The PID controller, which consists of proportional, integral and derivative elements, is commonly used in closed-loop control of industrial processes. A nonlinear system can be satisfactory controlled by a PID controller without the need for accurate mathematical model of controlled object. However, the parameters of the conventional PID controller are not often properly tuned for highly nonlinear systems with uncertain parameters. In this paper, a sequential optimal tuning method using adaptive optimal control (AOC) for PID controllers is proposed. With reinforcement learning architecture in the method, the adaptive critic is trained to predict the future system performance and the actor is optimized for the control. The control performance objective can be described in terms of cost function. By defining the cost function to specify desired response and stability of closed-loop system, the demand control performance can be achieved. The effectiveness of the sequential optimal control is verified by several simulation results, where a PID controller and a Takagi–Sugeno (T-S) fuzzy PID controller based on AOC algorithm are used to control a linear and a nonlinear system correspondently. The results show that the proposed method can sequentially optimize the controller behavior by the learning procedure which can automatically adjust the controller parameters and the cost function is explicitly minimized.
Subjects
PID controller
Adaptive optimal control
T-S fuzzy control
Sequential optimization
Industrial control
Type
thesis
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