Model predictive position control of permanent magnet synchronous motor servo system with sliding mode observer

Abstract

In order to improve the control accuracy and robustness of permanent magnet synchronous motor (PMSM) servo system, a new non‐cascade predictive control strategy is proposed to replace the traditional three‐closed‐loop servo control in this paper. The design process is as follows. First, the state variables and a discrete‐time state space model of motor are defined; meanwhile, the lumped term of disturbance is included in the motor model. Second, the optimal cost function is chosen to design the model predictive controller (MPC). Then, according to the uncertain parameters and external disturbance, the sliding mode observer (SMO) is introduced to estimate and compensate the disturbance. Finally, the comparative experimental result proves that the proposed predictive control method not only has less regulation parameters but also has high position tracking accuracy and strong anti‐interference ability even under different conditions.

Research on EMB electro-mechanical brake system control algorithm

“…Furthermore, when building this controller, the selection of PID parameter values should be considered from multiple perspectives. The actual value of the parameter value is influenced by the mechanical dynamic characteristics of the system itself, which in this case is influenced by the physical characteristics of the EMB actuator components 17 . In actual engineering, the tuning methods of related parameter values generally include two types, calculation tuning and engineering tuning.…”

Production Change Optimization Model of Nonlinear Supply Chain System under Emergencies

“…In the primary stage of the development of the sliding mode control, most of the research focused on the design methods of sliding mode control [ 18 , 19 ], and in the middle stage, it mostly focused on the improvement of the sliding mode control convergence laws [ 20 , 21 , 22 ]. Nowadays, the research related to sliding mode control mainly extends its application area by combining it with other methods, such as adaptive control [ 23 , 24 , 25 ], neural network algorithms [ 26 , 27 ], and model predictive control [ 28 ]. Although sliding mode control has been widely used, there are fewer studies related to optimizing the overall supply chain system management using sliding mode control as known from the literature [ 29 ], which is one of the motivations for this paper.…”

Analysis and Robustness Improvement of Finite-Control-Set Model Predictive Current Control for IPMSM With Model Parameter Mismatches

“…Robust MPCC based equivalent input disturbance approach for PMSM drive is studied in [13]. The SMO is introduced in the non-cascade predictive control to estimate and compensate the disturbance caused by the uncertain parameters [14]. A continuous integral-type terminal SMO has been studied to deal with the mismatched disturbance [15].…”