An adaptive finite‐time stable control law for manipulator robots with unknown parameters

Abstract

This article develops an adaptive finite-time stable control algorithm for an accurate manipulation of robot arms considering unknown model parameters and external disturbances. First, a state-dependent twisting control algorithm is developed for the robust manipulation of robot arms under the consideration of bounded unknown uncertainties conditions. The more challenging problem of robust control design under consideration of totally unknown dynamical parameters is investigated in a second stage by supporting the control law with a suitable online parameters estimator. The objective of the parameters estimator is to drive the vector of estimated parameters to some level that ensures the establishment of real second-order sliding mode without overloading the discontinuous controller gains. This approach based on combining both techniques of robust and adaptive algorithms allows overcoming large levels of uncertainties while preserving a high level of tracking accuracy without exciting the unwanted phenomena of chattering. The system finite-time stability is proven by using Lyapunov theory and the weighted homogeneity principle. Practical tests are reported to support theoretical study results. K E Y W O R D Sadaptive and robust control, finite-time stability, manipulator robots, second-order sliding mode control, twisting control algorithm 5218

A model free adaptive‐robust design for control of robot manipulators: Time delay estimation approach

“…1 Among the control schemes, model-based control algorithms require the nonlinear robot dynamic information and thus, their implementation is not achievable due to the lack of exact information and computational complexity. 2 Based on this fact, remarkably control schemes have been proposed to overcome the mentioned problem such as PID or PD control, 3,4 fuzzy control, 5,6 neural networks, 7,8 sliding mode control (SMC) 9,10 and adaptive SMC. 11,12 The conventional robust control methods such as SMC can achieve high performance in the position control of the robots.…”

“…Generally, control of the robot manipulators is a challenging problem caused by the inherent nonlinearities, unspecified disturbances, time‐varying parameters, and robot dynamic uncertainties 1 . Among the control schemes, model‐based control algorithms require the nonlinear robot dynamic information and thus, their implementation is not achievable due to the lack of exact information and computational complexity 2 . Based on this fact, remarkably control schemes have been proposed to overcome the mentioned problem such as PID or PD control, 3,4 fuzzy control, 5,6 neural networks, 7,8 sliding mode control (SMC) 9,10 and adaptive SMC 11,12 …”

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“…Many methods have now been developed to control nonlinear dynamical systems. Some examples of the robust control techniques that have been proposed include sliding mode control (SMC) [1], adaptive control [2][3][4][5], fuzzy control [6][7][8], backstepping [9], model predictive control [10], integral-type saturated control [11], proportional-derivative control [12], and observer-based quantized output feedback control [13].…”

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