In this paper, adaptive impedance control is developed for an ${n}$ -link robotic manipulator with input saturation by employing neural networks. Both uncertainties and input saturation are considered in the tracking control design. In order to approximate the system uncertainties, we introduce a radial basis function neural network controller, and the input saturation is handled by designing an auxiliary system. By using Lyapunov’s method, we design adaptive neural impedance controllers. Both state and output feedbacks are constructed. To verify the proposed control, extensive simulations are conducted.

Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation

Barrier Lyapunov functions for Nussbaum gain adaptive control of full state constrained nonlinear systems

This paper investigates adaptive fuzzy neural network (NN) control using impedance learning for a constrained robot, subject to unknown system dynamics, the effect of state constraints, and the uncertain compliant environment with which the robot comes into contact. A fuzzy NN learning algorithm is developed to identify the uncertain plant model. The prominent feature of the fuzzy NN is that there is no need to get the prior knowledge about the uncertainty and a sufficient amount of observed data. Also, impedance learning is introduced to tackle the interaction between the robot and its environment, so that the robot follows a desired destination generated by impedance learning. A barrier Lyapunov function is used to address the effect of state constraints. With the proposed control, the stability of the closed-loop system is achieved via Lyapunov’s stability theory, and the tracking performance is guaranteed under the condition of state constraints and uncertainty. Some simulation studies are carried out to illustrate the effectiveness of the proposed scheme.

Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning

Adaptive control-based Barrier Lyapunov Functions for a class of stochastic nonlinear systems with full state constraints

Adaptive finite-time tracking control of full state constrained nonlinear systems with dead-zone

This paper studies the tracking control problem for an uncertain ${n}$ -link robot with full-state constraints The rigid robotic manipulator is described as a multiinput and multioutput system Adaptive neural network (NN) control for the robotic system with full-state constraints is designed In the control design, the adaptive NNs are adopted to handle system uncertainties and disturbances The Moore–Penrose inverse term is employed in order to prevent the violation of the full-state constraints A barrier Lyapunov function is used to guarantee the uniform ultimate boundedness of the closed-loop system The control performance of the closed-loop system is guaranteed by appropriately choosing the design parameters Simulation studies are performed to illustrate the effectiveness of the proposed control

Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints

In this paper, we consider the trajectory tracking of a marine surface vessel in the presence of output constraints and uncertainties. An asymmetric barrier Lyapunov function is employed to cope with the output constraints. To handle the system uncertainties, we apply adaptive neural networks to approximate the unknown model parameters of a vessel. Both full state feedback control and output feedback control are proposed in this paper. The state feedback control law is designed by using the Moore–Penrose pseudoinverse in case that all states are known, and the output feedback control is designed using a high-gain observer. Under the proposed method the controller is able to achieve the constrained output. Meanwhile, the signals of the closed loop system are semiglobally uniformly bounded. Finally, numerical simulations are carried out to verify the feasibility of the proposed controller.

Adaptive Neural Network Control of a Marine Vessel With Constraints Using the Asymmetric Barrier Lyapunov Function

In this paper, we present adaptive neural network tracking control of a robotic manipulator with input deadzone and output constraint A barrier Lyapunov function is employed to deal with the output constraints Adaptive neural networks are used to approximate the deadzone function and the unknown model of the robotic manipulator Both full state feedback control and output feedback control are considered in this paper For the output feedback control, the high gain observer is used to estimate unmeasurable states With the proposed control, the output constraints are not violated, and all the signals of the closed loop system are semi-globally uniformly bounded The performance of the proposed control is illustrated through simulations

Zhao Yin

Papers

Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints

Adaptive Neural Network Control of a Marine Vessel With Constraints Using the Asymmetric Barrier Lyapunov Function

Neural Network Control of a Robotic Manipulator With Input Deadzone and Output Constraint