Robust Optimal PID type ILC for Linear Batch Process
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ISSN:1598-6446 eISSN:2005-4092 http://www.springer.com/12555
Robust Optimal PID type ILC for Linear Batch Process Furqan Memon and Cheng Shao* Abstract: The proportional-integral-derivative (PID) controller is standard technique for controlling the industrial batch process. However, the parameter tuning and updating of PID controller has been a challenging topic for control engineers especially for the real system having initial state error, state and output disturbances. In this paper, a kind of robust optimal iterative learning control (ILC) scheme is suggested to update the PID gains for the linear system with initial state error, state and output disturbances. The quadratic performance criteria is considered, which constitute of the error and input slew rate, and the robust BIBO stability is investigated theoretically for the proposed PID type ILC scheme. In addition, the bound of the tracking error has been calculated by using Lyapunov composite energy function. Simulation examples are also given to demonstrate the effectiveness of the proposed scheme in term of its ability to deal with linear as well as nonlinear system Keywords: Discrete-time system, iterative learning control (ILC), parameter optimal ILC, PID iterative learning control.
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INTRODUCTION
Due to the PID controller’s better good performance, simple structure, and robustness, it is still considered as a standard tool to solve the automatic control problem of industrial processes. However, the better performance of the PID controller depends strongly on the proper choice of the PID parameters. Some efforts are made to update PID parameters, such as fuzzy logic [1], neural network [2], adaptive [3] and etc. For batch/repetitive processes, iterative learning control (ILC) is known as an effective control strategy to optimize control performance from iteration to iteration. Hence, it is motivated to apply ILC schemes to update PID parameters for the batch processes. ILC is an effective control technique to realize perfect tracking for the batch process. The main principle of ILC is to use the error information from the previous iteration to enhance the input or learning gains for the subsequent trial, such that perfect or bounded tracking of the reference trajectory can be attained. ILC method for computing such input was first addressed in 1978 by Uchiyama [4] and later mathematically formulated by Arimoto et al. in 1984 [5]. Afterward, significant research efforts have been devoted to the ILC design to deal with actual batch process, for the improvement of tracking performance and convergence rate. In order to deal with linear and non-linear batch processes, re-
searchers have recommended many techniques for attaining perfect tracking in both continuous- and discrete-time domains [6,7]. The efficacy of ILC has been demonstrated in literature with the applications in the field of robotics, mechatronics, manufacturing, building control, nuclear fusion, rehabilitation, and in network control [8–16]. ILC consists of many varieties, which have bee
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