Adaptive position control of pneumatic artificial muscles using an RBF-tuned single-neuron PID controller

Purpose This paper aims to improve the position regulation performance of pneumatic artificial muscle (PAM) systems by developing an adaptive outer-loop control strategy capable of handling nonlinear and time-varying dynamics. Design/methodology/approach An adaptive single-neuron proportional–integral–derivative (SN-PID) controller is proposed, which tunes its gains in real time using a radial basis function (RBF) neural network. The controller incorporates Hebbian learning and Jacobian-based fe