2D Terminal Constrained Model Predictive Iterative Learning Control of Batch Processes With Time Delay

Abstract

In this paper, a 2D terminal constrained model predictive iterative learning control method of batch processes with time delay is proposed to deal with time delay, input and output constraints, and disturbances in batch processes. Firstly, an iterative learning control law is designed for the given batch process; then the state error and output tracking error are introduced, and the original state-space model is converted to an equivalent 2D-FM model. In the meantime, an optimal performance index with terminal constraints is introduced, and an update law is designed to minimize the objective function under input and output constraints. The robust constraint set is adopted that the system state is converged to this set round the desired point. Then, based on the designed optimal performance index and Lyapunov stability theory, the MPC problem is transformed into a linear matrix inequality problem and a sufficient condition is given to ensure the robust asymptotically stability of the closed-loop system. Finally, the validity of the proposed method is proved by the simulation on a stirred tank. INDEX TERMS Batch processes, time delay, terminal constraints, 2D terminal constrained model predictive iterative learning control.

Probability Based Stochastic Iterative Learning Control for Batch Processes With Actuator Faults

“…where, γ (t, k) = 0 , normal system 1 , fault system represents whether there exists the failure or not, we suppose that each batch runs n steps and the possibility is shown as formula (9). Since formula ( 10) and ( 9) are opposite events, the possibility of formula ( 10) is 1 − (1 − α) n ; if the failure occurs in the current batch, probabilities are the same as the time direction, as can be seen in formula ( 11) and (12).…”

Genetic-Algorithm-Optimization-Based Predictive Functional Control for Chemical Industry Processes Against Partial Actuator Faults

“…In this complex situation, it is necessary to seek new control methods. As a current control algorithm, model predictive control (MPC) is widely used because of its ability to improve control performance [28][29][30][31][32][33][34][35]. Especially for a class of systems whose process is nonlinear and its exact model is difficult to obtain or whose process time delay is large, this method is more popular.…”

“…Especially for a class of systems whose process is nonlinear and its exact model is difficult to obtain or whose process time delay is large, this method is more popular. In order to solve the problem of disturbances and faults, a model predictive fault-tolerant control (MPFTC) strategy based on genetic algorithm (GA) is proposed [33]. The nonlinear model predictive control (NMPC) method is constructed in [34], which solves the constraints and nonlinear problems.…”