Iterative Learning Fault-Tolerant Control for Batch Processes

Abstract

This paper develops an iterative learning reliable control (ILRC) scheme for batch processes with unknown disturbances and actuator faults. To develop the iterative learning control design, the batch process is transformed to, and treated as, a two-dimensional Fornasini-Marchsini (2D-FM) model. The relevant concepts of fault-tolerance along two-dimensional (2D) axes are introduced. The proposed control law can guarantee the closed-loop convergency along both the time and the cycle directions to satisfy H ∞ performance, even with unknown disturbances and actuator faults. For performance comparison, a traditional fault-tolerant control law is also developed by considering the batch process in each cycle as a continuous process. Conditions for the existence of the proposed ILRC scheme are given as linear matrix inequalities. Applications to injection velocity control show that the proposed ILRC achieve the design objectives well, with performance improvement along both the time and cycle directions.

Modelling of non-minimum phase effects in discrete-time norm optimal iterative learning control

“…Applications of ILC can be widely found in areas including industrial robot manipulators [1], [2], chemical batch processes [3], [4], some medical applications [? ] and manufacturing [5], [6].…”

Parameter‐dependent Lyapunov function‐based robust iterative learning control for discrete systems with actuator faults

“…Let u k,i (p), 1 ≤ i ≤ r, denote the entries in u k (p) and let u F k,i (p) denote an input, or actuator signal, with possible failures. Then the failure model used in this paper is [22,26] …”

“…Fault-tolerant control is a critical issue for ILC and there has been some research reported, such as [22,23,24]. This paper gives new results on fault tolerance for ILC design using repetitive process stability theory where a parameter dependent Lyapunov function was used in an attempt to admit a larger uncertainty range than its constant counterpart.…”

Robust fault-tolerant iterative learning control for discrete systems via linear repetitive processes theory

“…Also let u k,i (p), 1 ≤ i ≤ r, denote the entries in u k (p) and let u F k,i (p) denote an input and actuator signal with possible failures. Then, the failure model used in this paper is [24] …”

“…This uses the theory of differential repetitive processes and again there is no 2D systems model counterpart or lifted model. In comparison to [24], the major advance in this paper is the robustness analysis and control law design.…”