Particle Swarm Optimization-Based Model-Free Adaptive Control for Time-Varying Batch Processes

Abstract

The batch process is a production process with strong nonlinearity, which usually suffers from time-varying parameters and uncertainty of disturbances. Concerning the mentioned problems, this study proposes to investigate the application of the particle swarm optimization-based model-free adaptive control (PSO-MFAC) method for time-varying batch processes. Model-Free Adaptive Control (MFAC) is a data-driven control method, which is one of the promising methods to solve the nonlinear process. Firstly, a Full Form Dynamic Linearization Model-Free Adaptive Control method has been adopted for the control of batch processes. Further, considering that the adopted model-free adaptive control involves seven control parameters, such as cognitive scaling factor (φ₁), social scaling factor (φ₂), inertia weight (φ₃), learning rate (η), control parameter update rate, exploration rate and learning rate for MFAC obtained by a particle swarm optimization (PSO) algorithm in combination with a criterion function performance index. Finally, by comparing it with the existing methods, a typical batch fermentation was applied to verify that PSO-MFAC had a good control effect. The findings indicate that the PSO-MFAC controller exhibits a preference for exploiting the optimal option due to its φ₃ value less than 0.1. The efficacy and feasibility of the PSO-MFAC control effect have been proven by obtaining the lowest integral square error (ISE) value of 1.1192 regarding the nonlinearity of the batch process due to time-varying challenges.