A Non-Linear Continuous-Time Generalized Predictive Control for a Planar Cable-Driven Parallel Robot

Fouad Inel,Ali Medjbouri,Giuseppe Carbone

doi:10.3390/act10050097

Fouad Inel, Ali Medjbouri + Show 1 more

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https://doi.org/10.3390/act10050097

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Journal: Actuators	Publication Date: May 4, 2021
Citations: 5	License type: CC BY 4.0

Affiliation: University of Skikda, University of Calabria

Abstract

This paper addresses a novel nonlinear algorithm for the trajectory tracking of a planar cable-driven parallel robot. In particular, we outline a nonlinear continuous-time generalized predictive control (NCGPC). The proposed controller design is based on the finite horizon continuous-time minimization of a quadratic predicted cost function. The tracking error in the receding horizon is approximated using a Taylor-series expansion. The main advantage of the proposed NCGPC is based on using an analytic solution, which can be truncated to a desired degree of order of the Taylor-series. This allows us to achieve a prediction horizon of NCGPC tracking performance. The description of the proposed NCGPC method is followed by a comparison between NCGPC and a conventional computed torque control (CTC) method. Robustness tests are performed by considering payload and parameter uncertainties for both controllers. Simulation results of NCGPC compared to the commonly used CTC prove the effectiveness and advantages of the proposed approach.

Highlights

This paper focuses at a two-degree of freedom (DOF) planar cable-driven parallel
Simulation results obtained with nonlinear continuous-time generalized predictive control (NCGPC) are presented and compared to those obtained using computed torque control (CTC), which is widely used for robotic systems
This paper describes a novel continuous time predictive controller design method for a planar cable-driven parallel robot

Summary

Introduction

The proposed controller design is based on the finite horizon continuous-time minimization of a quadratic predicted cost function. The main advantage of the proposed NCGPC is based on using an analytic solution, which can be truncated to a desired degree of order of the Taylor-series This allows us to achieve a prediction horizon of NCGPC tracking performance. Model predictive control (MPC), known as receding horizon control, is generally based on a priori knowledge of the process via a prediction model ensuring the estimation of the future outputs This can be achieved by means of frequency analyses, as proposed, for example, in [11], or by establishing proper dynamic models with cable elasticity, as reported, for example, in [12].

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