School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, China.
PLoS One. 2023 Aug 8;18(8):e0285453. doi: 10.1371/journal.pone.0285453. eCollection 2023.
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.
外骨骼在医疗康复领域得到了广泛应用，然而外骨骼控制不精确可能会导致患者康复过程中的事故，因此提高外骨骼的控制性能变得至关重要。然而，提高外骨骼的控制性能极其困难，外骨骼模型的非线性性质使得控制变得特别困难，而患者佩戴外骨骼时的外部干扰也会影响控制效果。为了解决上述问题，本文提出了一种基于粒子群优化（PSO）和 RBF 神经网络的方法来优化外骨骼扭矩控制，以研究非线性外骨骼关节的运动轨迹，结果表明，PSO-RBFNN 优化后的外骨骼扭矩控制具有更快的控制速度、更好的稳定性、更精确的控制结果和更强的抗干扰能力，优化后的外骨骼可以有效地解决非线性外骨骼控制困难和患者佩戴外骨骼时的干扰问题。
