This paper presents a novel control algorithm for electrically driven robot manipulators. The proposed control law is simple and model-free based on the voltage control strategy with the decentralized structure and only joint position feedback. It works for both repetitive and non-repetitive tasks. Recently, some control approaches based on the uncertainty estimation using the Fourier series have been presented. However, the proper value for the fundamental period duration has been left as an open problem. This paper addresses this issue and intuitively shows that in order to perform repetitive tasks; the least common multiple (LCM) of fundamental period durations of the desired trajectories of the joints is a proper value for the fundamental period duration of the Fourier series expansion. Selecting the LCM results in the least tracking error. Moreover, the truncation error is compensated by the proposed control law to make the tracking error as small as possible. Adaptation laws for determining the Fourier series coefficients are derived according to the stability analysis. The case study is an SCARA robot manipulator driven by permanent magnet DC motors. Simulation results and comparisons with a voltage-based controller using adaptive neuro-fuzzy systems show the effectiveness of the proposed control approach in tracking various periodic trajectories. Moreover, the experimental results on a real SCARA robot manipulator verify the successful practical implementation of the proposed controller.

Uncertainty estimation in robust tracking control of robot manipulators using the Fourier series expansion

Design of fuzzy system-fuzzy neural network-backstepping control for complex robot system

https://cronfa.swan.ac.uk/Record/cronfa31618/Download/0031618-02022017123053.pdf

Discrete-time optimal adaptive RBFNN control for robot manipulators with uncertain dynamics

Adaptive task-space control of robot manipulators using the Fourier series expansion without task-space velocity measurements

Direct Adaptive Function Approximation Techniques Based Control of Robot Manipulators

Purpose – Discrete control of robot manipulators with uncertain model is the purpose of this paper. Design/methodology/approach – The proposed control design is model-free by employing an adaptive fuzzy estimator in the controller for the estimation of uncertainty as unknown function. An adaptive mechanism is proposed in order to overcome uncertainties. Parameters of the fuzzy estimator are adapted to minimize the estimation error using a gradient descent algorithm. Findings – The proposed model-free discrete control is robust against all uncertainties associated with the model of robotic system including the robot manipulator and actuators, and external disturbances. Stability analysis verifies the proposed control approach. Simulation results show its efficiency in the tracking control. Originality/value – A novel model-free discrete control approach for electrically driven robot manipulators is proposed. An adaptive fuzzy estimator is used in the controller to overcome uncertainties. The parameters of ...

Model-free discrete control for robot manipulators using a fuzzy estimator

This paper presents a novel discrete adaptive fuzzy controller for electrically driven robot manipulators. It addresses how to overcome the nonlinearity, uncertainties, discretizing error and approximation error of the fuzzy system for asymptotic tracking control of robotic manipulators. The proposed controller is model-free in the form of discrete Mamdani fuzzy controller. The parameters of fuzzy controller are adaptively tuned using an adaptive mechanism derived by stability analysis. A robust control term is used to compensate the approximation error of the fuzzy system for asymptotic tracking of a desired trajectory. The controller is robust against all uncertainties associated with the robot manipulator and actuators. It is easy to implement since it requires only the joint position feedback. Compared with fuzzy controllers which employ all states to guarantee stability, the proposed controller is very simpler. Stability analysis and simulation results show its efficiency in the tracking control.

Discrete adaptive fuzzy control for asymptotic tracking of robotic manipulators

Purpose – The purpose of this paper is to design a discrete indirect adaptive fuzzy controller for a robotic manipulator. This paper addresses how to overcome the approximation error of the fuzzy system and uncertainties for asymptotic tracking control of robotic manipulators. The uncertainties include parametric uncertainty, un-modeled dynamics, discretization error and external disturbances. Design/methodology/approach – The proposed controller is model-free and voltage-based in the form of discrete-time Mamdani fuzzy controller. The parameters of fuzzy controller are adaptively tuned for asymptotic tracking of a desired trajectory. A robust control term is used to compensate the approximation error of the fuzzy system. An adaptive mechanism is derived based on the stability analysis. Findings – The proposed model-free discrete control is robust against all uncertainties associated with the robot manipulator and actuators. The approximation error of the fuzzy system is well compensated to achieve asympt...

Discrete-time indirect adaptive fuzzy control for robot manipulators

The objective of this paper is to design a finite-time controller for Lorenz system in discrete-time. First, a discrete model is derived through the Taylor series expansion. In the next step, a discrete-time terminal sliding mode controller (DTSMC) is developed to reach a finite-time and high precision control. The stability analysis of DTSMC is presented in the presence of external disturbance and model uncertainties. Numerical simulations of Lorenz system are shown and compared with a discrete-time sliding mode control (DSMC) to illustrate the effectiveness of the proposed control scheme.

Terminal sliding mode control of chaotic Lorenz system: A discrete-time case

This paper presents a discrete-time robust control for electrically driven robot manipulators in the task space. A novel discrete-time model-free control law is proposed by employing an adaptive fuzzy estimator for the compensation of the uncertainty including model uncertainty, external disturbances and discretization error. Parameters of the fuzzy estimator are adapted to minimize the estimation error using a gradient descent algorithm. The proposed discrete control is robust against all uncertainties as verified by stability analysis. The proposed robust control law is simulated on a SCARA robot driven by permanent magnet dc motors. Simulation results show the effectiveness of the control approach.

http://jad.shahroodut.ac.ir/article_333_0266cd24947152c098635e88f364f904.pdf

Siamak Azargoshasb

Papers

Model-free discrete control for robot manipulators using a fuzzy estimator

Discrete adaptive fuzzy control for asymptotic tracking of robotic manipulators

Discrete-time indirect adaptive fuzzy control for robot manipulators

Terminal sliding mode control of chaotic Lorenz system: A discrete-time case

Discrete time robust control of robot manipulators in the task space using adaptive fuzzy estimator