Robust Model-Free Control Framework with Safety Constraints for a Fully Electric Linear Actuator System

TL;DR

This paper presents a robust, model-free control framework with safety constraints for a complex electric linear actuator system, combining barrier Lyapunov functions and swarm intelligence for optimal control and stability.

Contribution

It introduces a novel dual robust barrier Lyapunov function control approach combined with a swarm intelligence-based parameter tuning for safety and robustness in electric actuator systems.

Findings

Successful experimental validation of the control framework

Effective safety constraint management in complex systems

Minimized tracking errors through optimized parameters

Abstract

This paper introduces a novel model-free control strategy for a complex multi-stage gearbox electromechanical linear actuator (EMLA) system, driven by a permanent magnet synchronous motor (PMSM) with non-ideal ball screw characteristics. The proposed control approach aims to (1) manage user-specified safety constraints, (2) identify optimal control parameters for minimizing tracking errors, (3) ensure robustness, and (4) guarantee uniformly exponential stability. First, this paper employs a trajectory-setting interpolation-based algorithm to specify the piecewise definition of a smooth and jerk-bounded reference trajectory. Then, a dual robust subsystem-based barrier Lyapunov function (DRS-BLF) control is proposed for the PMSM-powered EMLA system to track the reference motions, guaranteeing user-specified safety related to constraints on system characteristics and alleviating control signal efforts. This methodology guarantees robustness and uniform exponential convergence. Lastly, optimal control parameter values are determined by customizing a swarm intelligence technique known as the Jaya (a term derived from the Sanskrit word for `victory') algorithm to minimize tracking errors. Experimental results validate the performance of the DRS-BLF control.