MTN-based recursive d-step-ahead predictive control of MIMO nonlinear systems with unknown input time-delay in industrial process

Abstract

Purpose This paper aims to provide a precise tracking control scheme for multi-input multi-output “MIMO” nonlinear systems with unknown input time-delay in industrial process. Design/methodology/approach The predictive control scheme based on multi-dimensional Taylor network (MTN) model is proposed. First, for the unknown input time-delay, the cross-correlation function is used to identify the input time-delay through just the input and output data. And then, the scheme of predictive control is designed based on the MTN model. It goes as follows: a recursive d-step-ahead MTN predictive model is developed to compensate the influence of time-delay, and the extended Kalman filter (EKF) algorithm is applied for its learning; the multistep predictive objective function is designed, and the optimal controlled output is determined by iterative refinement; and the convergence of MTN predictive model and the stability of closed-loop system are proved. Findings Simulation results show that the proposed scheme is of desirable generality and capable of performing the tracking control for MIMO nonlinear systems with unknown input time-delay in industrial process effectively, such as the continuous stirred tank reactor (CSTR) process, which provides a considerably improved performance and effectiveness. The proposed scheme promises strong robustness, low complexity and easy implementation. Research limitations/implications For the limitations of proposed scheme, the time-invariant time-delay is only considered in time-delay identification and control schemes. And the CSTR process is only introduced to prove that the proposed scheme can adapt to practical industrial scenario. Originality/value The originality of the paper is that the proposed MTN control scheme has good tracking performance, which solves the influence of time-delay, coupling and nonlinearity and the real-time performance for MIMO nonlinear systems with unknown input time-delay.

Output reachable set synthesis for singular Markovian jump systems with time-varying delay: a PD bumpless transfer control scheme

“…This phenomenon is called time delay. Time delay exists widely in various practical systems, such as mechanical drive systems (Liu et al , 2020), industrial process systems (Yan and Li, 2022), warehouse management systems (Tai et al , 2019). Systems with time delay are also referred to as time delay systems (Qi et al , 2023; Yang et al , 2023).…”

“…A time window based on prioritized path planning algorithm was proposed by Tai et al (2019) to address the delay problem of multiple automated guided vehicles. Yan and Li (2022) proposed an improved recursive d-step multi-dimensional Taylor network prediction model that compensates for the effect of time delay. Along with the intensive investigation of time delay, it has been generalized to other complex systems.…”

Recursive d-step-ahead predictive control of MIMO nonlinear systems with input time-delay via multi-dimensional Taylor network extended from PID

“…In addition, its parameters are capable of selftuning, not relying on the experience. MTN has been found to be particularly useful for the system identification and prediction (Li and Yan, 2018;Lin et al, 2014) and nonlinear systems control (Li et al, 2019(Li et al, , 2022Yan and Duan, 2021;Yan and Kang, 2017;Yan and Li, 2022;Zhang and Yan, 2020). Since the MIMO system with input time-delays have not been considered in the above-mentioned research works, this study is just focused on the MIMO nonlinear time-delay system with complex situations.…”