Predictive Control Strategy Based on Extreme Learning Machine for Path-Tracking of Autonomous Mobile Robot

Abstract

In this paper, we propose a novel nonlinear predictive control strategy based on an extreme learning machine to address the path-tracking control problem of wheeled mobile robots in the presence external disturbances. The hybrid chaotic optimization algorithm (HCOA), which can avoid being trapped in local minima and improve convergence in dealing with the large space and highdimension optimization problems, is used to perform real-time nonlinear minimization of the cost function of a mobile robot to enhance the control accuracy. The proposed improved bidirectional extreme learning machine is employed to model the mobile robot plant and estimate future plant output. The experimental results of tracking the automation mobile robot indicate that the proposed controller can provide more accuracy and faster tracking performance than traditional neural network predictive controllers.

Systematic Procedure for Optimal Controller Implementation Using Metaheuristic Algorithms

“…A particular case of the RHC is the well-known Model Predictive Control (MPC) that makes at each step a specific action: The minimization of the prediction errors. There are many works dealing with the MPC from different points of view such as; Theoretical works - Clarke (1994); Hiskens and Gong (2006); Zheng (2010), tutorial reviews - Christofides et al (2013), or surveys of industrial applications - Qin and Badgwell (2003), Yang et al (2014), Lopez Francol (2018.…”

State of the Art in Predictive Control of Wheeled Mobile Robots

“…Neural Network Predictive Control. NNPC method uses algorithms with optimization based on arti-ficial neural networks, and relevant examples are in works [11,43].…”

“…It was noticed that, in the works concerning predictive control, differentially driven robots and three-wheeled omnidirectional robots are predominant. The works [6,7,12,22,24,28,31,32,43] concern two-wheeled robots, and work [6] concerns inverted pendulum robot. In turn, works [2,5,10,20,21,29,38] describe three-wheeled omnidirectional robots, whereas [1,4,18,35], three-wheeled robots with two fixed driven wheels and castor.…”

A hybrid switching predictive controller based on bi-level kernel-based ELM and online trajectory builder for automotive coldstart emissions reduction

“…The simulations demonstrated the effectiveness of the proposed controller for the considered problem. Yang et al [22] proposed a predictive control strategy based on ELM for the path tracking of autonomous mobile robots. A chaos-based optimization algorithm was employed to calculate the controlling commands.…”