Adaptive neural network control of a robotic manipulator with unknown backlash-like hysteresis

This study proposes an adaptive neural network controller for a 3-DOF robotic manipulator that is subject to backlashlike hysteresis and friction. Two neural networks are used to approximate the dynamics and the hysteresis non-linearity. A neural network, which utilises a radial basis function approximates the robot's dynamics. The other neural network, which employs a hyperbolic tangent activation function, is used to approximate the unknown backlash-like hysteresis. The authors also consider two cases: full state and output feedback control. For output feedback, where system states are unknown, a high gain observer is employed to estimate the states. The proposed controllers ensure the boundedness of the control signals. Simulations are also performed to show the effectiveness of the controllers

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

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

Intelligent Position, Pressure and Depth Sensing in a Soft Optical Waveguide Skin

In this paper, we develop and validate a simple processing method suitable for position, pressure and depth sensing for a simple Soft Optical Waveguide Skin (SOWS). The soft skin consists of an elastomeric sensitive area (69 cm2 and 2 mm thick) free of any other material pattern except for infrared emitters and photodetectors embedded at its periphery. When sensing area is touched, the photodetectors experience a loss in light intensity, and if a proper processing algorithm is developed, spatial information can be retrieved. Using adaptive boosting and decision trees as base learners, we develop classification and regression models to map this light intensity variation to our variables of interest: position, pressure and depth of indentation. This simple approach achieves high accuracy in predicting touch position with a spatial resolution of 9 mm, while simultaneously estimating with high precision the pressure level and depth of indentation

Adaptive voltage and frequency control of islanded multi-microgrids

This paper introduces an adaptive voltage and frequency control method for inverter-based distributed generations (DGs) in a multi-microgrid (MMG) structure using distributed cooperative control and adaptive neural networks (ANN). First, model-based controllers are designed using the Lyapunov theory and dynamics of the inverter-based DGs. ANNs are then utilized to approximate these dynamics, resulting in an intelligent controller, which does not require a priori information about DG parameters. Also, the proposed controllers do not require the use of voltage and current proportional-integral controllers normally found in the literature. The effectiveness of the proposed controllers are verified through simulations under different scenarios on an MMG test system. Using Lyapunov analysis, it is proved that the tracking error and the neural network weights are uniformly ultimately bounded, which results in achieving superior dynamic voltage and frequency regulation