Abstract
In accordance with the movement coordination principle of both lower limbs, a complete radial basis functions neural network based adaptive sliding mode control strategy (RBFVSMC) is proposed. The movement information on the non-affected side of patients is detected to drive the rehabilitation training. The nonlinear mathematical model of the rehabilitation robot system is firstly described. Based on the robotic dynamic model, a variable sliding mode control (VSMC) is proposed to stabilize the system. To reduce the buffeting problem caused by VSMC, the universal approximation of RBFNN is used to approach and compensate external disturbances and uncertainties. Besides, the buffeting phenomenon of sliding mode control is alleviated by replacing the sign function with a saturation function. The final asymptotic stability is guaranteed with Lyapunov criteria. Compared to proportional-integral-derivative (PID), radial basis functions neural network (RBFNN), continuous terminal SMC (CNTSMC), and decentralized adaptive robust controller (NDOBCTC), the effectiveness of the overall control scheme is demonstrated by co-simulation and human experiment in accordance to track following performance and disturbances rejection ability.
Highlights
The incidence of stroke is increasing with aging
B RADIAL BASIS FUNCTION NEURAL NETWORK (RBFNN) Radial Basis Function Neural Network was proposed by Powell in 1988, a method was used for prediction
The experimental results show that the tracking performance of the RBFNNVSMC controller designed in this paper is better, and the overall average error of hip joint position, hip joint speed, knee joint position, and knee joint speed is 1.123 rad, 0.179 rad,1.103 rad, and 0.307 rad respectively
Summary
The incidence of stroke is increasing with aging. Stroke has become one of the most serious diseases for humans in the world, and its incidence is significantly higher in developed countries than in developing countries. Chenghu Jing et al proposed a continuous nonsingular terminal SMC (CNTSMC) [15] This method could suppress buffeting effectively and achieve finite-time convergence, but the convergence of the tracking error was not good enough in the case of bounded disturbance. In [19], a decentralized adaptive robust controller (NDOBCTC) based on NDO was proposed for robot manipulators These control methods based on disturbance observer only achieve asymptotic stability, which means that the tracking error cannot be converged to an equilibrium state within a finite time. 4) Both simulation results and human experiment results on the proposed rehabilitation robot demonstrated the effectiveness and superiority of the proposed control scheme for achieving desired performance of track following in terms of accuracy, and robustness against disturbances.
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