Adaptable fuzzy control solutions for driving systems working under continuously variable conditions

Abstract

Based on our previous research results partially published in [1], [2], [3] and [4], the paper presents a survey on dedicated control solutions for driving systems working under continuously variable conditions: variable reference input (speed), variable moment of inertia and variable load disturbance. The solutions were validated using numerical simulation and tested on a laboratory equipment [5]. The structures employ the switching between different ModelBased (MB) control algorithms; due on the simplicity in adaptation, different fuzzified Takagi-Sugeno control solutions are offered. A hybrid Takagi-Sugeno PI-neurofuzzy controller is presented. The solutions are based on a classical cascade control structure with an inner current controller and an external speed control loop with bumpless switching between the control algorithms. Our solutions are representative for mechatronics applications. I. INTRODUCTIONThe paper synthesizes results concerning the development of control solutions with variable parameters dedicated for driving systems working in continuously variable conditions (c.v.c.) variable reference, variable parameters (mainly variable moment of inertia, VMI) and variable load disturbance, which depends on the plant evolution, reported by the authors in [1]- [4]. Similar applications are also treated in other papers in the literature [6]- [9]. For such applications, the variable control solution offers better control system performance: better variable reference control and better operating in extreme regimes.Based on the fact that a relatively good model of the plant can be available, Section II is focused on the plant modeling: the nonlinear model and, derived from it, the linearized benchmark mathematical models (MMs) which are efficiently usable in the design step [10]. Section III presents the basic control structures: the PI case, the T-S PI-fuzzy controller, the PI quasi-relay sliding mode controller and finally, the neuro-fuzzy controller. Case studies related to the tuning are treated in Section IV. The main conclusions are discussed in Section V.

Low speed control and implementation of permanent magnet synchronous motor for mechanical elastic energy storage device with simultaneous variations of inertia and torque

“…However, PI control cannot accommodate complex non-linearity of PMSM and multivariable load variations. Therefore, various improved PI control methods are proposed such as fuzzy PI control [16], neural network PI control [20]. Nevertheless, implementation of these intelligent algorithms is relatively difficult, and these control processes are more complicated.…”

“…In contrast with the former two cases, the third case is more complicated and relatively fewer literatures have been retrieved. A proportional–integral (PI) controller [15] and Takagi‐Sugeno PI fuzzy controller [16] are presented for a DC electric drive system. In [17], load torque observer and inertia identification are, respectively, designed to observe parameters and suppress perturbation simultaneously.…”