Robust nonlinear model predictive sliding mode control algorithm for saturated uncertain multivariable mechanical systems

Abstract

In this paper, robust stabilization and predictive tracking control for a class of nonlinear uncertain multivariable systems is presented. The control scheme is built by incorporating nonlinear model predictive control in discrete sliding mode control to obtain optimal results while satisfying hard constraints and closed-loop robustness in the presence of external disturbance (matched or unmatched) and parametric uncertainty. Additionally, the control input domain limitation is involved in construction of the model predictive control scheme by employing sliding functions admissible bounds. The problem of cost minimization is solved through optimal selection of terminal cost gain and allocation of system dynamics with respect to sliding surfaces while robust stabilization and feasibility are ensured through the prediction horizon.

Relaxed nonquadratic H∞ robust constrained event-triggered fuzzy controller design for discrete-time nonlinear systems

“…To do this, based on the TSFM of the actuator saturation and the new offline LMI condition, the rate and amplitude limitations of the control signal were considered. This means, compared with Chen et al (2023), Farbood et al (2021, Homaeinezhad andFotoohiNia (2023), andTeng et al (2018), the constrained performance was achieved without any online computation. Furthermore, the eventtriggered mechanism decreases the number of controller updates which results in system performance improvement.…”

“…In some of the existing studies that are considered the limitations of the controller, online computation is needed. For example, in the model predictive control (MPC)-based methods (Chen et al, 2023;Farbood et al, 2021Farbood et al, , 2022Homaeinezhad and FotoohiNia, 2023) to calculate the constrained control signal, an online-based optimization problem must be solved. Therefore, the computational burden and the conservatism are increased.…”

“…In the existing studies (Cai et al, 2020;Chaibi et al, 2020;Du et al, 2019;Lian et al, 2019;Zheng et al, 2020), the limitations of the amplitude and rate of the control signals are not considered. To achieve a constrained performance in the onlinebased solving optimization problem methods (Chen et al, 2023;Farbood et al, 2021Farbood et al, , 2022Homaeinezhad and FotoohiNia, 2023;Teng et al, 2018) the computational burden is very high which leads to difficulty in the real-time implementation of the control system. In Echreshavi et al (2021) and , the controller gains to guarantee the closed-loop stability of the system are calculated based on the quadratic Lyapunov function which is a highly conservative approach.…”

“…Despite Cai et al (2020), Chaibi et al (2020), Du et al (2019), Lian et al (2019), Zhang and Dong (2021), and Zheng et al (2020), the amplitude and rate constraints of the control signal are investigated in the procedure of the proposed method in an offline scheme. Consequently, the computational burden of the proposed approach is less than the constrained online-based control methods (Chen et al, 2023;Farbood et al, 2021Farbood et al, , 2022Homaeinezhad and FotoohiNia, 2023). Compared with Cai et al (2020), Chaibi et al (2020), Du et al (2019), Lian et al (2019), andZheng et al (2020) to reduce the communication burden; an ETC is designed to consider the amplitude and rate limitations of the controller, simultaneously for the TSFMbased nonlinear system.…”

Tracking control of moving flexible system by incorporation of feedback-based vibrational energy sink in model predictive control algorithm

“…The MPC incorporation with SMC is generally employed to benefit from both SMC inherent robustness in dealing with uncertainties associated with nonlinear problems and MPC capability for addressing system constraints. 29–31 The main issue emerges when optimal solution is not available due to necessary satisfaction of hard constraints such as actuator limitations. In this study, a new technique is utilized such that tracking performance is considered as soft constraints.…”