Modeling and Design of ANFIS Dynamic Sliding Mode Controller for a Knee Orthosis of Hemiplegia

Abstract

The use of assistive devices to control the loss of strength and range of motion of hemiplegic patients is becoming common. It is difficult to develop a precise control approach for a knee orthosis system because of the unpredictability of the dynamics and the unwanted subject’s spasm, jerk, and vibration during gait assistance. In this study, an adaptive neuro-fuzzy inference system (ANFIS) control system based on a nonlinear disturbance observer (NDO) and dynamic sliding mode controller (DSMC) is presented to restore the natural gait of hemiplegic patients experiencing mobility disorder and strength loss as well as monitor patient-induced disturbances and parameter variations during semiactive assistance of both the stance and swing phases. The knee orthosis system’s nonlinear dynamic relations are first developed using the Euler–Lagrange formation. Using MATLAB/Simulink, the dynamic model and controller design for the knee orthosis system was created. The Lyapunov theory is then used to ensure the knee orthosis system is asymptotically stable in view of the proposed controller once the proposed control scheme has been designed. The proposed control scheme’s (ANFIS–NDO–DSMC) gait tracking performances are shown and contrasted with the conventional sliding mode controller (SMC). Furthermore, a comparative performance analysis for parametric uncertainties and disturbances is presented to look at the robustness of the proposed controller (ANFIS–NDO–DSMC). The coefficient of determination ( $R^2$ ) and root mean square error (RMSE) between the reference knee angle and ANFIS–NDO–DSMC for stance phase are 1 and 0.000516 rad, respectively. For swing phase, $R^2$ and RMSE are 0.9999 and 0.003202 rad, respectively. For SMC, RMSE is 0.000643 and 0.003252 rad for stance and swing phases, respectively. Stance and swing phase $R^2$ is 0.9997 and 0.9994, respectively. As seen from simulation results, the proposed controller exhibited excellent gait tracking performance for the knee orthosis control with high robustness and very fast convergence to a steady state compared to SMC.