Performance Investigations of an Improved Backstepping Operational space Position Tracking Control of a Mobile Manipulator

This article implies an improved backstepping control technique for the operational-space position tracking of a kinematically redundant mobile manipulator. The mobile manipulator thought-out for the analysis has a vehicle base with four mecanum wheels and a serial manipulator arm with three rotary actuated joints. The recommended motion controller provides a safeguard against the system dynamic variations owing to the parameter uncertainties, unmodelled system dynamics and unknown exterior disturbances. The Lyapunov’s direct method assists in designing and authenticating the system’s closed-loop stability and tracking ability of the suggested control strategy. The feasibility, effectiveness and robustness of the recommended controller are demonstrated and investigated numerically with the help of computer based simulations. The mathematical model used for the computer-based simulations is derived based on a real-time mobile manipulator and the derived model is further verified with an inbuilt gazebo model in a robot operating system (ROS) environment. In addition, the proposed scheme is verified on an in-house fabricated mobile manipulator system. Further, the recommended controller performance is correlated with the conventional backstepping control design in both computer-based simulations and in real-time experiments

Intelligent Adaptive Motion Control for Ground Wheeled Vehicles

In this paper a new intelligent adaptive control is applied to solve a problem of motion control of ground vehicles with two independent wheels actuated by a differential drive. The major objective of this work is to obtain a motion control system by using a new fuzzy inference mechanism where the Lyapunov’s stability can be assured. In particular the parameters of the kinematical control law are obtained using an intelligent Fuzzy mechanism, where the properties of the Fuzzy maps have been established to have the stability above. Due to the nonlinear map of the intelligent fuzzy inference mechanism (i.e. fuzzy rules and value of the rule), the parameters above are not constant, but, time after time, based on empirical fuzzy rules, they are updated in function of the values of the tracking errors. Since the fuzzy maps are adjusted based on the control performances, the parameters updating assures a robustness and fast convergence of the tracking errors. Also, since the vehicle dynamics and kinematics can be completely unknown, a dynamical and kinematical adaptive control is added. The proposed fuzzy controller has been implemented for a real nonholonomic electrical vehicle. Therefore system robustness and stability performance are verified through simulations and experimental studies

Robust Backstepping Tracking Control of Mobile Robot Based on Nonlinear Disturbance Observer

This paper presents a robust backstepping control (BC) method based on nonlinear disturbance observer (NDOB) for trajectory tracking of the nonholonomic wheeled mobile robot (WMR) in the presence of external disturbances and parameters uncertainties. At first, a bounded Fuzzy logic based backstepping controller (BFLBC) is designed to control the WMR without considering the effects of the external disturbances and the parameters uncertainties. Typically, the conventional BC controller depends upon the state tracking errors analysis, where unbounded velocity signal is produced for the applications that have huge tracking errors. Therefore, a fuzzy logic controller (FLC) is introduced in this research in order to normalize the state tracking errors, so that the input errors to the BC are bounded to a finite interval. Finally, the designed BFLBC is integrated with the nonlinear disturbance observer in order to attenuate the external disturbances and model uncertainties. The simulation results show the effectiveness of the proposed controller to generate a bounded velocity signal as well as to stabilize the tracking errors to zero. In addition, the results prove that the proposed controller provide an excellent disturbance attenuation as well as robustness against the parameters uncertainties

Trajectory Tracking Control Design for Dual-Arm Robots Using Dynamic Surface Controller

This paper presents a dynamic surface controller (DSC) for dual-arm robots (DAR) tracking desired trajectories. The DSC algorithm is based on backstepping technique and multiple sliding surface control principle, but with an important addition. In the design of DSC, low-pass filters are included which prevent the complexity in computing due to the “explosion of terms”, i.e. the number of terms in the control law rapidly gets out of hand. Therefore, a controller constructed from this algorithm is simulated on a four degrees of freedom (DOF) dual-arm robot with a complex kinetic dynamic model. Moreover, the stability of the control system is proved by using Lyapunov theory. The simulation results show the effectiveness of the controller which provide precise tracking performance of the manipulator

Comprehensive review on controller for leader-follower robotic system

985-1007This paper presents a comprehensive review of the leader-follower robotics system. The aim of this paper is to find and elaborate on the current trends in the swarm robotic system, leader-follower, and multi-agent system. Another part of this review will focus on finding the trend of controller utilized by previous researchers in the leader-follower system. The controller that is commonly applied by the researchers is mostly adaptive and non-linear controllers. The paper also explores the subject of study or system used during the research which normally employs multi-robot, multi-agent, space flying, reconfigurable system, multi-legs system or unmanned system. Another aspect of this paper concentrates on the topology employed by the researchers when they conducted simulation or experimental studies

Adaptive Predictive Control Using Neural Network for a Class of Pure-feedback Systems in Discrete-time

10.1109/TNN.2008.2000446IEEE Transactions on Neural Networks1991599-1614ITNN

Disturbance Rejection Control for Autonomous Trolley Collection Robots with Prescribed Performance

Trajectory tracking control of autonomous trolley collection robots (ATCR) is an ambitious work due to the complex environment, serious noise and external disturbances. This work investigates a control scheme for ATCR subjecting to severe environmental interference. A kinematics model based adaptive sliding mode disturbance observer with fast convergence is first proposed to estimate the lumped disturbances. On this basis, a robust controller with prescribed performance is proposed using a backstepping technique, which improves the transient performance and guarantees fast convergence. Simulation outcomes have been provided to illustrate the effectiveness of the proposed control scheme