Neuroadaptive Control for Uncertain Euler-Lagrange Systems With Input and Output Constraints

This paper investigates the control issue of Euler-Lagrange systems (ELSs) subject to dynamic uncertainties and external disturbances under input and output constraints, and develops two neuroadaptive control schemes, i.e., direct neuroadaptive approximation method and indirect neuroadaptive approximation method. In the control design, a smooth saturation function called Gaussian error function is used to replace the saturation model, which is applied to solve the input saturation issue. Moreover, a new function is used to guarantee that the output does not violate the restricted boundary. The uncertain dynamic of the ELSs is reconstructed by the direct or indirect neuroadaptive method, and then a virtual-parameter learning method is proposed to reduce the computational load of control schemes. With the aid of Lyapunov stability theory, it is proven that all signals in the closed-loop control system are bounded and the tracking error of ELSs converges to zero under the proposed neuroadaptive control schemes. The simulations on a robotic manipulator illuminate the effectiveness and preponderance of the developed neuroadaptive control schemes.