Generalized Dynamic Predictive Control for Nonlinear Systems Subject to Mismatched Disturbances With Application to PMSM Drives

Abstract

This paper investigates a generalized dynamic predictive control (GDPC) strategy with a novel autonomous tuning mechanism of the horizon for a class of nonlinear systems subject to mismatched disturbances. As a new incremental function for the predictive control method, the horizon can be determined autonomously with respect to the system working conditions, instead of selecting a fixed value via experience before, which is able to effectively improve the control performance optimization ability to a certain extent considering different system perturbation levels. To this aim, firstly, a non-recursive composite control framework is constructed based on a series of disturbance observations via higher-order sliding modes. Secondly, by designing a simple one-step scaling gain update mechanism into the receding horizon optimization, the horizon can be therefore adaptively tuned according to its real-time practical operating conditions. A three-order numerical simulation and a typical engineering application of permanent magnet synchronous motor (PMSM) drive system are carried out to demonstrate the effectiveness and conciseness of the proposed GDPC method.

Barrier-Function-Based Adaptive Fast-Terminal Sliding-Mode Control for a PMSM Speed-Regulation System

“…The traditional TSMC can be expressed as [33] s = ė + β⌊e⌉ λ (8) where β > 0 is a constant and λ is a positive constant satisfying 0 < λ < 1.…”

“…However, it is important to note that PMSMs are complex, strongly coupled nonlinear systems [7]. In practical scenarios, PMSMs are often subjected to diverse disturbances, such as external load disturbances and internal parameter variations [8], which pose challenges in achieving optimal control performance using the traditional linear control strategy.…”

Optimized Trajectory Tracking for Robot Manipulators with Uncertain Dynamics: A Composite Position Predictive Control Approach

“…In contrast, continuous-time predictive control circumvents the need for model discretization and directly utilizes the Taylor approximation of the system dynamics model to compute optimization performance indicators. This approach yields explicit analytical solutions, offering advantages such as simplicity in design, reduced computational load, and a clear parameter-tuning mechanism [18,19]. By advancing research on the continuous-time MPC, we can further enhance the effectiveness and efficiency of robot manipulators in challenging environments, benefiting various applications in industrial automation, medical robotics, and autonomous systems.…”

“…Based on the above analysis and inspired by reference [18], this paper investigates a composite position predictive control (PPC) approach for the trajectory tracking of robot manipulators. The proposed PPC scheme integrates motion profile and disturbance preview techniques to effectively address the uncertainties present in the system.…”