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Exoskeletons are widely used in the field of medical rehabilitation, however imprecise exoskeleton control may lead to accidents during patient rehabilitation, so improving the control performance of exoskeletons has become crucial. Nevertheless, improving the control performance of exoskeletons is extremely difficult, the nonlinear nature of the exoskeleton model makes control particularly difficult, and external interference when the patient wears an exoskeleton can also affect the control effect. In order to solve the above problems, a method based on particle swarm optimization (PSO) and RBF neural network to optimize exoskeleton torque control is proposed to study the motion trajectory of nonlinear exoskeleton joints in this paper, and it is found that exoskeleton torque control optimized by PSO-RBFNN has faster control speed, better stability, more accurate control results and stronger anti-interference, and the optimized exoskeleton can effectively solve the problem of difficult control of nonlinear exoskeleton and the interference problem when the patient wears the exoskeleton.</div

PSO optimization RBFNN weight flowchart.

Exoskeletons are widely used in the field of medical rehabilitation, however imprecise exoskeleton control may lead to accidents during patient rehabilitation, so improving the control performance of exoskeletons has become crucial. Nevertheless, improving the control performance of exoskeletons is extremely difficult, the nonlinear nature of the exoskeleton model makes control particularly difficult, and external interference when the patient wears an exoskeleton can also affect the control effect. In order to solve the above problems, a method based on particle swarm optimization (PSO) and RBF neural network to optimize exoskeleton torque control is proposed to study the motion trajectory of nonlinear exoskeleton joints in this paper, and it is found that exoskeleton torque control optimized by PSO-RBFNN has faster control speed, better stability, more accurate control results and stronger anti-interference, and the optimized exoskeleton can effectively solve the problem of difficult control of nonlinear exoskeleton and the interference problem when the patient wears the exoskeleton.</div

RBF neural network diagram.

Exoskeletons are widely used in the field of medical rehabilitation, however imprecise exoskeleton control may lead to accidents during patient rehabilitation, so improving the control performance of exoskeletons has become crucial. Nevertheless, improving the control performance of exoskeletons is extremely difficult, the nonlinear nature of the exoskeleton model makes control particularly difficult, and external interference when the patient wears an exoskeleton can also affect the control effect. In order to solve the above problems, a method based on particle swarm optimization (PSO) and RBF neural network to optimize exoskeleton torque control is proposed to study the motion trajectory of nonlinear exoskeleton joints in this paper, and it is found that exoskeleton torque control optimized by PSO-RBFNN has faster control speed, better stability, more accurate control results and stronger anti-interference, and the optimized exoskeleton can effectively solve the problem of difficult control of nonlinear exoskeleton and the interference problem when the patient wears the exoskeleton.</div

PSO-RBFNN optimal control.

Exoskeletons are widely used in the field of medical rehabilitation, however imprecise exoskeleton control may lead to accidents during patient rehabilitation, so improving the control performance of exoskeletons has become crucial. Nevertheless, improving the control performance of exoskeletons is extremely difficult, the nonlinear nature of the exoskeleton model makes control particularly difficult, and external interference when the patient wears an exoskeleton can also affect the control effect. In order to solve the above problems, a method based on particle swarm optimization (PSO) and RBF neural network to optimize exoskeleton torque control is proposed to study the motion trajectory of nonlinear exoskeleton joints in this paper, and it is found that exoskeleton torque control optimized by PSO-RBFNN has faster control speed, better stability, more accurate control results and stronger anti-interference, and the optimized exoskeleton can effectively solve the problem of difficult control of nonlinear exoskeleton and the interference problem when the patient wears the exoskeleton.</div

Quantitative analysis chart of error.

Exoskeletons are widely used in the field of medical rehabilitation, however imprecise exoskeleton control may lead to accidents during patient rehabilitation, so improving the control performance of exoskeletons has become crucial. Nevertheless, improving the control performance of exoskeletons is extremely difficult, the nonlinear nature of the exoskeleton model makes control particularly difficult, and external interference when the patient wears an exoskeleton can also affect the control effect. In order to solve the above problems, a method based on particle swarm optimization (PSO) and RBF neural network to optimize exoskeleton torque control is proposed to study the motion trajectory of nonlinear exoskeleton joints in this paper, and it is found that exoskeleton torque control optimized by PSO-RBFNN has faster control speed, better stability, more accurate control results and stronger anti-interference, and the optimized exoskeleton can effectively solve the problem of difficult control of nonlinear exoskeleton and the interference problem when the patient wears the exoskeleton.</div

Comparison of error benchmarks.

Exoskeletons are widely used in the field of medical rehabilitation, however imprecise exoskeleton control may lead to accidents during patient rehabilitation, so improving the control performance of exoskeletons has become crucial. Nevertheless, improving the control performance of exoskeletons is extremely difficult, the nonlinear nature of the exoskeleton model makes control particularly difficult, and external interference when the patient wears an exoskeleton can also affect the control effect. In order to solve the above problems, a method based on particle swarm optimization (PSO) and RBF neural network to optimize exoskeleton torque control is proposed to study the motion trajectory of nonlinear exoskeleton joints in this paper, and it is found that exoskeleton torque control optimized by PSO-RBFNN has faster control speed, better stability, more accurate control results and stronger anti-interference, and the optimized exoskeleton can effectively solve the problem of difficult control of nonlinear exoskeleton and the interference problem when the patient wears the exoskeleton.</div

Exoskeleton specific parameters.

Exoskeletons are widely used in the field of medical rehabilitation, however imprecise exoskeleton control may lead to accidents during patient rehabilitation, so improving the control performance of exoskeletons has become crucial. Nevertheless, improving the control performance of exoskeletons is extremely difficult, the nonlinear nature of the exoskeleton model makes control particularly difficult, and external interference when the patient wears an exoskeleton can also affect the control effect. In order to solve the above problems, a method based on particle swarm optimization (PSO) and RBF neural network to optimize exoskeleton torque control is proposed to study the motion trajectory of nonlinear exoskeleton joints in this paper, and it is found that exoskeleton torque control optimized by PSO-RBFNN has faster control speed, better stability, more accurate control results and stronger anti-interference, and the optimized exoskeleton can effectively solve the problem of difficult control of nonlinear exoskeleton and the interference problem when the patient wears the exoskeleton.</div

Joint trajectory and error.

Exoskeletons are widely used in the field of medical rehabilitation, however imprecise exoskeleton control may lead to accidents during patient rehabilitation, so improving the control performance of exoskeletons has become crucial. Nevertheless, improving the control performance of exoskeletons is extremely difficult, the nonlinear nature of the exoskeleton model makes control particularly difficult, and external interference when the patient wears an exoskeleton can also affect the control effect. In order to solve the above problems, a method based on particle swarm optimization (PSO) and RBF neural network to optimize exoskeleton torque control is proposed to study the motion trajectory of nonlinear exoskeleton joints in this paper, and it is found that exoskeleton torque control optimized by PSO-RBFNN has faster control speed, better stability, more accurate control results and stronger anti-interference, and the optimized exoskeleton can effectively solve the problem of difficult control of nonlinear exoskeleton and the interference problem when the patient wears the exoskeleton.</div

Exoskeleton shape structure.

Exoskeletons are widely used in the field of medical rehabilitation, however imprecise exoskeleton control may lead to accidents during patient rehabilitation, so improving the control performance of exoskeletons has become crucial. Nevertheless, improving the control performance of exoskeletons is extremely difficult, the nonlinear nature of the exoskeleton model makes control particularly difficult, and external interference when the patient wears an exoskeleton can also affect the control effect. In order to solve the above problems, a method based on particle swarm optimization (PSO) and RBF neural network to optimize exoskeleton torque control is proposed to study the motion trajectory of nonlinear exoskeleton joints in this paper, and it is found that exoskeleton torque control optimized by PSO-RBFNN has faster control speed, better stability, more accurate control results and stronger anti-interference, and the optimized exoskeleton can effectively solve the problem of difficult control of nonlinear exoskeleton and the interference problem when the patient wears the exoskeleton.</div

Single-leg simplified diagram.

Exoskeletons are widely used in the field of medical rehabilitation, however imprecise exoskeleton control may lead to accidents during patient rehabilitation, so improving the control performance of exoskeletons has become crucial. Nevertheless, improving the control performance of exoskeletons is extremely difficult, the nonlinear nature of the exoskeleton model makes control particularly difficult, and external interference when the patient wears an exoskeleton can also affect the control effect. In order to solve the above problems, a method based on particle swarm optimization (PSO) and RBF neural network to optimize exoskeleton torque control is proposed to study the motion trajectory of nonlinear exoskeleton joints in this paper, and it is found that exoskeleton torque control optimized by PSO-RBFNN has faster control speed, better stability, more accurate control results and stronger anti-interference, and the optimized exoskeleton can effectively solve the problem of difficult control of nonlinear exoskeleton and the interference problem when the patient wears the exoskeleton.</div