Abstract
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.
Highlights
In recent decades, Euler-Lagrange systems (ELSs) have attracted more and more attention from researchers since they can model the dynamics of a large class of physical systems, like surface vehicles [1], [2], robotic manipulators [3], [4], aircrafts [5], underwater vehicles [6], [7], etc
The dynamics of ELSs are highly nonlinear and inevitably suffer from parametric and nonparametric uncertainties. Another challenge for the tracking control of ELSs is that these control schemes may fail their goal when the actuator cannot provide adequate power caused by its inherent physical limitations or the system output violates the operational space constraint, i.e., input saturation nonlinearities and output constraints
In this paper, we have addressed the neuroadaptive tracking control problem for ELSs in the simultaneous presence of uncertain dynamics, unknown disturbances and constraints both in input saturation
Summary
Euler-Lagrange systems (ELSs) have attracted more and more attention from researchers since they can model the dynamics of a large class of physical systems, like surface vehicles [1], [2], robotic manipulators [3], [4], aircrafts [5], underwater vehicles [6], [7], etc. The log-type BLF was applied to handle the time-varying output constraint of uncertain ELSs in [23], [24]. In the second scheme, a RBF NNs is applied to approximate the norm of unknown nonlinear function vector In this context, a indirect neuroadaptive control scheme is developed. The control objective of this paper is to design a robust holds adaptive NN tracking control law τc for the uncertain EL system (1) subject to input and output constraints under. CONTROL DESIGN FOR STATIC CONSTRAINT two adaptive neural tracking control laws are designed to solve the tracking control problem of uncertain ELSs subject to static output constraint and input saturation, in which one is the direct approximation method and the other one is the indirect approximation method. NN is applied to handle the unknown nonlinear function vector f (x)
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