Linear Quadratic Predictive Fault-Tolerant Control for Multi-Phase Batch Processes

Self Cite

Concerning two dimensional (2D) multi-phase batch processes, a delay-range-dependent optimal hybrid iterative learning control (2D-OILC) scheme is presented. Firstly, the process is converted into a 2D-FM time delay switched system in different dimensions by introduction of state errors and system output tracking errors among batches. According to this model, hybrid iterative learning control is combined with feedback control and a 2D optimal hybrid controller is designed. Secondly, the average dwell time method is adopted to give sufficient conditions for system robust stability in terms of linear matrix inequalities and the upper bound for system's optimal performance index is provided. Besides, the issue of optimal controller design is presented to draw the minimum upper limit of the closed-loop system performance index. The presented control law not only can make the closed-loop system preserving good tracking performance but also ensures its H ∞ performance. Last but not least, the validity of the above strategies is proved by the simulation on an injection molding process. INDEX TERMS Multi-phase batch processes, optimal hybrid iterative learning control, interval time varying delay, average dwell time.

Section: Imentioning

confidence: 99%

“…The so-called two dimensional (2D) system means that the state of the system changes along two different directions: the time and batch directions [6] or the time and space directions [7]. At the moment, results are fruitful [17]- [25]. In a survey of research findings, most of them are concerned with one-phase cases.…”

Section: Introductionmentioning

confidence: 99%

Design of Optimal Hybrid Controller for Multi-Phase Batch Processes With Interval Time Varying Delay

Zhang

Wang

2019

Self Cite

“…Multi-phase batch process control has become a hot topic in research [29], [33]- [36]. In [34], a switching system model was used to study a multi-phase batch production process, and a related predictive control strategy was proposed. In [35], the average dwell time method was used to study a multiphase system, and an optimal control strategy is proposed.…”

Section: Introductionmentioning

confidence: 99%

Improved H-Infinity Hybrid Model Predictive Fault-Tolerant Control for Time-Delayed Batch Processes Against Disturbances

Chen

2020

An H-infinity model predictive fault-tolerant control strategy is proposed for multi-phase batch processes with interval delay and actuator failures. First, state variables, state errors and output tracking errors are introduced to establish an extended-state-space switched system model. Then, based on this model, a predictive fault-tolerant control law is designed for tracking the set point by the output that satisfies the requirements of the optimal performance index under input and output constraints. The feasibility conditions for the solvability of the control law are presented in the form of linear matrix inequalities. In addition, the designed switching law is constructed, and the gain of the control law is obtained via the optimization algorithm. This design has several advantages: the output tracking is faster, the tracking performance is superior, and the trace is smoother at the switching time. Finally, through a comparison with traditional methods, the effectiveness and feasibility of this method are demonstrated via injection molding simulation. INDEX TERMS Batch production systems, added delay, fault tolerant control, linear matrix inequalities, Lyapunov methods, predictive models, switching systems, predictive control.

“…The ILMPC strategy proposed in [45] has dynamic R parameters, integrating ILC and MPC into one whole, and achieving zero error tracking. So as to solve the multi-phase batch processing problem of input delay and actuator faults, [46] proposed a linear quadratic prediction FTC scheme. However, these are all studied under 2D linear systems.…”

Section: Introductionmentioning

confidence: 99%

2D Fuzzy Constrained Fault-Tolerant Predictive Control of Nonlinear Batch Processes

Luo

Wang

2019

Self Cite

Concerning actuator gain faults and strong system nonlinearity in batch processes, a design method of a kind of 2D fuzzy constrained model fault-tolerant predictive controller is proposed. Firstly, introduce two errors: state error and output error, after that the original system model is converted into a 2D Roesser fault model. Meanwhile, the design of iterative learning fault tolerant control under constraints has been transformed into the determination of the constrained update law. Subsequently, real-time on-line design of the fuzzy fault-tolerant update law that ensures the closed-loop system robustly asymptotic stability is presented by taking the appearance of linear matrix inequalities (LMIs) with constraint subject to the designed infinite optimization performance index and Lyapunov stability theory. Finally, taking a three-tank case as an example to compare with the 1D of the method proposed in this paper, the comparison results illustrated the 2D method of this paper has better control effects. INDEX TERMS Actuator faults, fuzzy constrained iterative learning predictive fault-tolerant control, nonlinear batch processes, 2D T-S fuzzy model.