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

Stochastic computing (SC) is a type of logic computation based on stochastic bit stream instead of the binary numbers (BNs). It is pseudo-analog computation in the digital domain that can realize multiplication, addition, and complex function computations. In this brief, a new encoding method, referred to as amplitude and frequency encoding (AFE) for SC, is presented. It extends SC to use multibit streams instead of bit streams to represent data. This method still uses the expectation of streams to achieve computation. Compared with the conventional stochastic bit stream, AFE realizes low-latency and low-area occupation than the conventional SC method. The rationality of the circuits is also examined from a mathematical perspective. The hardware logic circuit of AFE SC, including the multiplier, adder, and converter, was designed and implemented by a field-programmable gate array (FPGA). In addition, the latency of the computing, area, and precision of AFE SC was measured based on the FPGA board.

Yuhao Chen

Papers

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

Stochastic Computing Using Amplitude and Frequency Encoding