Particle Swarm Optimization-Based H∞ Tracking Fault Tolerant Control for Batch Processes

Abstract

This paper focuses on particle swarm optimization algorithm (PSOA)-based $\text{H}\infty $ tracking fault-tolerant control for batch processes to resist the influence of actuator faults and unknown disturbances. First, according to a given actual process model, by introducing output tracking error, state difference and new states including output tracking error, an extended equivalent model is constructed. Then, a linear-quadratic performance function is introduced. By using the PSOA to adjust those parameters in the function, a new state space $\text{H}\infty $ tracking fault-tolerant control law is proposed under optimal control theory. Actuator faults are regarded as uncertainties here. The Lyapunov stability theory is used to solve the allowable disturbances in a certain range. The greatest merit of this design is that it has better tracking performance and stronger anti-fault and interference ability. Finally, the injection molding process and nonlinear batch reactor are taken as examples to compare with the genetic algorithm method (GA) and the traditional control method (TC), which shows that the method proposed is more practical and effective.

Keywords

• Batch processes
• new extended state space model
• particle swarm optimization algorithm
• linear-quadratic H∞ tracking fault-tolerant control
• actuator fault