Abstract
As the actuator faults in an industrial process cause damage or performance deterioration, the design issue of an optimal controller against these failures is of great importance. In this paper, a fractional-order predictive functional control method based on population extremal optimization is proposed to maintain the control performance against partial actuator failures. The proposed control strategy consists of two key ideas. The first one is the application of fractional-order calculus into the cost function of predictive functional control. Since the knowledge of analytical parameters including the prediction horizon, fractional-order parameter, and smoothing factor in fractional-order predictive functional control is not known, population extremal optimization is employed as the second key technique to search for these parameters. The effectiveness of the proposed controller is examined on two industrial processes, e.g., injection modeling batch process and process flow of coke furnace under constant faults, time-varying faults, and nonrepetitive unknown disturbance. The comprehensive simulation results demonstrate the performance of the proposed control method by comparing with a recently developed predictive functional control, genetic algorithm, and particle swarm optimization-based versions in terms of four performance indices.
Highlights
In industrial processes, actuators play an important role in the industrial control system because an actuator links the controller output to the physical actions and determines the quality of products [1, 2]
We firstly introduce a typical industrial process, i.e., injection molding process in Section 4.1. en, in order to demonstrate the effectiveness of the proposed population-based extremal optimization (PEO)-fractional-order predictive functional control (FOPFC), we do some experiments on control of the injection velocity under different partial faults and unknown disturbance
We have proposed the PEO-FOPFC strategy for industrial processes under partial actuator failures. ere are two key operators in the PEO-FOPFC
Summary
Actuators play an important role in the industrial control system because an actuator links the controller output to the physical actions and determines the quality of products [1, 2]. Wang et al [4] put forward an iterative learning control (ILC) scheme for batch processes under partial actuator faults according to a particular 2D Fornasini–Marchsini model. Jin et al [6] proposed an improved ILC scheme to control the nonlinear constrained system with actuator failures. Ding et al [7] proposed a novel ILC scheme to control the uncertain multiple-input multiple-output discrete system under actuator faults. Due to the uncertainties of actuator fault, the control system design is often mismatched [1, 9] To deal with this challenge problem, ILC [10,11,12] has been developed as one of the most popular strategies for different industrial processes. As discussed in [3], the ILC is largely dependent on the repetitive nature of such processes, whose performance improvement is confined by this unsuitable assumption because many real-world processes are time-varying and nonrepetitive and suffering from persistent disturbance
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