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Enlarging the Terminal Region of Quasi-infinite Horizon NMPC Based on T-S Fuzzy Model
Shuyou Yu,Hong Chen,Xuejun Li 제어·로봇·시스템학회 2009 International Journal of Control, Automation, and Vol.7 No.3
The paper presents a method for enlarging the terminal region of quasi-infinity horizon nonlinear model predictive control (NMPC) for nonlinear systems with constraints. The main technique builds on the fact that terminal controllers are fictitious and never applied to the system in the quasi-infinite horizon NMPC [1]. Based on T-S fuzzy models of nonlinear systems, we show that a parameter-dependent state feedback law exists such that the corresponding value function and its level set can be served as terminal cost and terminal region. The problem of maximizing the terminal region is formulated as a convex optimization problem based on linear matrix inequalities (LMIs). A numerical example is given to illustrate the effectiveness of the proposed method.
Control Invariant Sets of Linear Systems with Bounded Disturbances
Shuyou Yu,Yu Zhou,Ting Qu,Fang Xu,Yan Ma 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.2
In this paper, algorithms to compute robust control invariant sets are proposed for linear continuous-time systems subject to additive but bounded disturbances. Robust control invariant sets of linear time invariant systems are achieved by logarithmic norm. Robust control invariant sets of linear uncertain systems, which are level sets of the storage functions, are obtained by solving functional differential inequality. Simulation shows that the proposed algorithms can yield improved minimal volume robust control invariant sets approximations in comparison with the schemes in the existing literature.
Liquid Level Tracking Control of Three-tank Systems
Shuyou Yu,Xinghao Lu,Yu Zhou,Yangyang Feng,Ting Qu,Hong Chen 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.10
In this paper, a liquid level tracking controller composed of a feedforward controller and a feedback controller is proposed for three-tank systems. Firstly, the flat property of three-tank systems is verified and a feedforward controller is designed accordingly to track the ideal trajectories. Secondly, in order to eliminate the tracking errors introduced by model uncertainties or unknown disturbances, a nonlinear model predictive controller is designed in which a terminal equality constraint is added for ensuring asymptotic convergence. In addition, an improved cuckoo search algorithm is adopted to solve the optimization problem involved in the nonlinear model predictive control. Finally, the control performance is confirmed by both simulation and experiment results.
Nonlinear Predictive Control of Active Four-wheel Steering Vehicles
Shuyou Yu,Wenbo Li,Baojun Lin,Yongfu Li,Hong Chen,Hongqing Chu,Jianhua Yu 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.10
In order to improve the handling stability of active four-wheel steering vehicles, a nonlinear model predictive controller is presented, which can guarantee that the actual sideslip angle and yaw rate can track the ideal sideslip angle and the ideal yaw rate through control of the front and rear wheel angles. A nonlinear static tyre model connected with a linear dynamic model is adopted to describe the vehicle dynamics. Furthermore, the tyre model is replaced by a map in the optimization problem of nonlinear model predictive control. The introduction of maps can reduce the online computational time by a trade-off between the computational burden of CPU and the storage burden of ROM. Simulation results in CarSim indicate that the proposed controller can follow the outputs of the ideal reference model, reduce the computational burden, and improve the handling stability of the active four-wheel steering vehicles effectively.
Computation of Feasible and Invariant Sets for Interpolation-based MPC
Ismi Rosyiana Fitri,Jung-Su Kim,Shuyou Yu,Young IL Lee 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.10
The terminal invariant set plays a key role in the stabilizing MPC (Model Predictive Control) formulation. When control gains of the terminal local control laws and corresponding feasible and invariant sets are given, the existing interpolation methods unite them to enlarge the stabilizable region and enhance performance. In this paper, when an invariant set is given, an algorithm is proposed to find another invariant set such that their convex hull is maximized and also invariant. Numerical examples show that the set of the stabilizable initial state of the MPC is enlarged by the terminal constraint set computed by an interpolation-based approach.