T–S fuzzy model predictive speed control of electrical vehicles. (September 2016)

• Record Type: Journal Article
• Authors: Khooban, Mohammad Hassan; Vafamand, Navid; Niknam, Taher
• Is Part Of: ISA transactions. Volume 64(2016:Sep.)
• Page Start: 231
• Page End: 240
• Publication Date: 2016-09
• Subjects: Nonlinear model predictive control (MPC) -- Linear matrix inequality (LMI) -- Electrical vehicles (EVs) -- Takagi–Sugeno (TS) fuzzy systems -- Speed control; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2016.04.019
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: This paper proposes a novel nonlinear model predictive controller (MPC) in terms of linear matrix inequalities (LMIs). The proposed MPC is based on Takagi–Sugeno (TS) fuzzy model, a non-parallel distributed compensation (non-PDC) fuzzy controller and a non-quadratic Lyapunov function (NQLF). Utilizing the non-PDC controller together with the Lyapunov theorem guarantees the stabilization issue of this MPC. In this approach, at each sampling time a quadratic cost function with an infinite prediction and control horizon is minimized such that constraints on the control input Euclidean norm are satisfied. To show the merits of the proposed approach, a nonlinear electric vehicle (EV) system with parameter uncertainty is considered as a case study. Indeed, the main goal of this study is to force the speed of EV to track a desired value. The experimental data, a new European driving cycle (NEDC), is used in order to examine the performance of the proposed controller. First, the equivalent TS model of the original nonlinear system is derived. After that, in order to evaluate the proficiency of the proposed controller, the achieved results of the proposed approach are compared with those of the conventional MPC controller and the optimal Fuzzy PI controller (OFPI), which are the latest research on the problem in hand. Highlights: This paper introduces a new MPC based on T–S Fuzzy Model. The proposed framework is simple and does not have any complexities. A new LMI is applied to approve the stability of proposed method. The experimental data, NEDC, is used in order to examine the performance of proposed controller.