Experimental evaluation of a model reference adaptive control for a hydraulic robot: a case study

Abstract

This paper presents the implementation of an explicit model reference adaptive control (MRAC) for position tracking of a dynamically unknown robot. An auto regressive exogenous (ARX) model is chosen to define the plant model and the control input is optimised in a H2 norm to reduce computational time and to simplify the algorithm. The theory of MRAC falls into a description of the various forms of controllers and parameter estimation techniques, therefore, applications may require very complicated solution methods depending on the selected laws. However, in this study, the proposed MRAC shows that applications may be as easy as classical control methods, such as PID, by guaranteeing the stability and achieving the convergency of the plant parameters. Despite the selected simple control model, simple optimisation method and drawbacks of the robot the experimental results show that MRAC provides an excellent position tracking compared with conventional control (PID). Many experimental implementations have been done on the robot and one of them is included in the paper.

Simulation and Experimental Studies on Perfect Tracking Optimal Control of an Electrohydraulic Actuator System

“…There are numerous researchers who used that linear model in either continuous time or discrete time in their proposed control strategy [12,[16][17][18]. Most of the modeling approaches for discrete-time model that have been implemented in previous researches are developed from first principle or physical laws.…”

“…The increasing numbers of works dealing with EHA system over the past decades involved a linear control, intelligent control, and nonlinear control approaches such as neural network (NN) [12], self-tuning fuzzy PID [13,14], model reference adaptive control (MRAC) [15,16], generalized predictive control (GPC) [17], and sliding mode control [18]. Most of tracking control system is typically necessary in minimizing gain and phase error from the output to the desired trajectory.…”

Study on Control Method for a Robotic Rotary Hydraulic Actuator

“…Liu et al proposed a hybrid fuzzy PID control of hydraulic robot, where the fuzzy controller controls the piston when the piston was near the desired position, and PID controller was applied when the piston is far away the target position [3]. The explicit model reference adaptive control was also proposed to track the position of a dynamically unknown hydraulic robot [4]. A minimal control synthesis algorithm was derived to improve the servo accuracy of hydraulic quadruped robot electro-hydraulic serve actuators [5].…”

Point-to-point trajectory tracking with two-degree-of-freedom robust control for a non-minimum phase electro-hydraulic system

“…The increasing numbers of works dealing with EHA system over the past decades involved a linear control, intelligent control and nonlinear control approaches such as neural network (NN) [10], self-tuning Fuzzy-PID [13,14], model reference adaptive control (MRAC) [15,16], generalized predictive control (GPC) [17] and sliding mode control (SMC) [18]. There are much works in designing SMC for EHA system previously based on continuous-time [19][20][21][22].…”