Modeling and Design of ANFIS Dynamic Sliding Mode Controller for a Knee Orthosis of Hemiplegia
Belay Eshetu,
Solomon Seid
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 t… Show more
The lower extremity exoskeleton can enhance the ability of human limbs, which has been used in many fields. It is difficult to develop a precise force tracking control approach for the exoskeleton because of the dynamics model uncertainty, external disturbances, and unknown human–robot interactive force lied in the system. In this paper, a control method based on a novel recurrent neural network, namely zeroing neural network (ZNN), is proposed to obtain the accurate force tracking. In the framework of ZNN, an adaptive RBF neural network (ARBFNN) is employed to deal with the system uncertainty, and a fixed-time convergence disturbance observer is designed to estimate the external disturbance of the exoskeleton electrohydraulic system. The Lyapunov stability method is utilized to prove the convergence of all the closed-loop signals and the force tracking is guaranteed. The proposed control scheme’s (ARBFNN-FDO-ZNN) force tracking performances are presented and contrasted with the exponential reaching law-based sliding mode controller (ERL-SMC). The proposed scheme is superior to ERL-SMC with fast convergence speed and lower tracking error peak. Finally, experimental tests are conducted to verify the efficacy of the proposed controller for solving accurate force tracking control issues.
The lower extremity exoskeleton can enhance the ability of human limbs, which has been used in many fields. It is difficult to develop a precise force tracking control approach for the exoskeleton because of the dynamics model uncertainty, external disturbances, and unknown human–robot interactive force lied in the system. In this paper, a control method based on a novel recurrent neural network, namely zeroing neural network (ZNN), is proposed to obtain the accurate force tracking. In the framework of ZNN, an adaptive RBF neural network (ARBFNN) is employed to deal with the system uncertainty, and a fixed-time convergence disturbance observer is designed to estimate the external disturbance of the exoskeleton electrohydraulic system. The Lyapunov stability method is utilized to prove the convergence of all the closed-loop signals and the force tracking is guaranteed. The proposed control scheme’s (ARBFNN-FDO-ZNN) force tracking performances are presented and contrasted with the exponential reaching law-based sliding mode controller (ERL-SMC). The proposed scheme is superior to ERL-SMC with fast convergence speed and lower tracking error peak. Finally, experimental tests are conducted to verify the efficacy of the proposed controller for solving accurate force tracking control issues.
Robots are increasingly being used in rehabilitation to assist patients with physical disabilities, particularly the knee joint. Physiotherapists often practice the patient’s leg around the knee joint to strengthen the muscles. However, Continuous Passive Motion (CPM) devices face challenges such as lack of feedback and resistance. This thesis aims to address these issues by using admittance and impedance concepts to control the robot’s flexible behavior against individual foot forces. The research uses a combination of backstepping and admittance algorithms, Model Reference Adaptive Control (MRAC), Sliding-Backstepping and Admittance Control to create a soft interactive patient-robot interaction. The device is designed for both left and right legs.
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