Intrinsic Plasticity-Driven Neuroadaptive Asymptotic Tracking Control for a Class of Uncertain Robotic Manipulators

This paper proposes a novel neuroadaptive asymptotic tracking control method for a class of uncertain multi-input multi-output (MIMO) robotic manipulators. Firstly, an intrinsic plasticity (IP)-driven cycle echo state network (ESN) is constructed. The intrinsic plasticity mechanism can adaptively adjust neuronal excitability, enhancing the network's ability to capture complex time-varying dynamics. Meanwhile, the cycle reservoir structure significantly reduces the number of neural connections, thus improving computational efficiency. Secondly, the proposed IP-driven cycle ESN is integrated with the robust integral of the sign of the error (RISE) framework to form a neuroadaptive controller. The IP-driven cycle ESN serves to dynamically approximate the unknown nonlinearities inherent in the robotic system, whereas the RISE term compensates for approximation errors and external disturbances to ensure satisfactory robust performance. Then, a rigorous stability analysis is given to demonstrate that the proposed controller can achieve asymptotic convergence of the tracking error. Finally, simulation experiments are conducted on two typical two-joint manipulators to evaluate the performance of the proposed method. Comparative results demonstrate that, in contrast to the Radial Basis Function Neural Network (RBFNN)-based PI control method, the proposed method achieves faster error convergence rate, higher tracking precision, and smoother control inputs. The results highlight the effectiveness of combining the approximation capability of the IP-driven cycle ESN with the robust compensation capability of the RISE framework for high-precision control of uncertain nonlinear robotic systems.