An Advanced Angular Velocity Error Prediction Horizon Self-Tuning Nonlinear Model Predictive Speed Control Strategy for PMSM System

Abstract

In nonlinear model predictive control (NMPC), higher accuracy can be obtained with a shorter prediction horizon in steady-state, better dynamics can be obtained with a longer prediction horizon in a transient state, and calculation burden is proportional to the prediction horizon which is usually pre-selected as a constant according to dynamics of the system with NMPC. The minimum calculation and prediction accuracy are hard to ensure for all operating states. This can be improved by an online changing prediction horizon. A nonlinear model predictive speed control (NMPSC) with advanced angular velocity error (AAVE) prediction horizon self-tuning method has been proposed in which the prediction horizon is improved as a discrete-time integer variable and can be adjusted during each sampling period. A permanent magnet synchronous motor (PMSM) rotor position control system with the proposed strategy is accomplished. Tracking performances including rotor position Integral of Time-weighted Absolute value of the Error (ITAE), the maximal delay time, and static error are improved about 15.033%, 23.077%, and 10.294% respectively comparing with the conventional NMPSC strategy with a certain prediction horizon. Better disturbance resisting performance, lower weighting factor sensitivities, and higher servo stiffness are achieved. Simulation and experimental results are given to demonstrate the effectiveness and correctness.

Based on Fast Non-singular Terminal Sliding of PMSM Model-free Control

“…In order to solve the limitations brought by traditional PI control, researchers have proposed a new control theory, which introduced nonlinear control into PMSM system variables, such as adaptive control [6][7][8], model prediction speed control [9][10][11], robust control [12][13][14], and sliding mode control [15][16][17]. Because sliding mode control is not easy to be interfered with by the outside world, it is widely used in PMSM control, but because the opening function is discontinuous, there will be serious oscillations at the critical point of the synovial surface, resulting in unstable system status.…”

Speed control of PMSM based on neural network model predictive control

“…Therefore, most of the current cases of successful application of the MPC to control PMSM are the MPC improved by scholars. For example, to reduce computational costs while maintaining prediction accuracy, Wei et al (2021) proposed a nonlinear model predictive speed control (NMPSC) with advanced angular velocity error (AAVE) prediction horizon self-tuning method and applied it to implement rotor position control for PMSM. In order to reduce the computational complexity of MPC, Bemporad et al (2002) proposed an explicit solution to the MPC optimization problem, which is known as explicit MPC.…”

Nonlinear Model Predictive Control of Single-Link Flexible-Joint Robot Using Recurrent Neural Network and Differential Evolution Optimization

“…The DEO algorithm has the benefit of being a global optimization technique that is simple to understand and implement, and has strong robustness and fewer parameters to be adjusted. Due to its advantages, DEO has been extensively investigated [49] and successfully applied in diverse fields, including robot manipulator systems [50], mobile robots [51][52][53], autonomous cars [54], spectrum sensing systems [55], and permanent magnet synchronous motor systems [56]. Although the integrating MPC and NN methods produced good results in robot applications, few researches are focusing on the position control of the FJ robot.…”