An Output-Recurrent-Neural-Network-Based Iterative Learning Control for Unknown Nonlinear Dynamic Plants

We present a design method for iterative learning control system by using an output recurrent neural network (ORNN). Two ORNNs are employed to design the learning control structure. The first ORNN, which is called the output recurrent neural controller (ORNC), is used as an iterative learning controller to achieve the learning control objective. To guarantee the convergence of learning error, some information of plant sensitivity is required to design a suitable adaptive law for the ORNC. Hence, a second ORNN, which is called the output recurrent neural identifier (ORNI), is used as an identifier to provide the required information. All the weights of ORNC and ORNI will be tuned during the control iteration and identification process, respectively, in order to achieve a desired learning performance. The adaptive laws for the weights of ORNC and ORNI and the analysis of learning performances are determined via a Lyapunov like analysis. It is shown that the identification error will asymptotically converge to zero and repetitive output tracking error will asymptotically converge to zero except the initial resetting error

Research on RBF neural network model reference adaptive control system based on nonlinear U – model

The overall objective of this study is to design the nonlinear U-model-based radial basis function neural network model reference adaptive control system, through research into a class of complex time-varying nonlinear plants. First, the ideal nonlinear plant is adopted as the reference model and transformed into the U-model representation. In the process, the authors establish the corresponding relationship between the degrees of the reference nonlinear model and the controlled nonlinear plants, and carry out research into the corresponding coefficient relationship between the reference nonlinear model and the controlled nonlinear plants. Also, the impact of the adjusting amplitude and tracking speed of the model on the system control accuracy is analyzed. Then, according to the learning error index of the neural network, the paper designs the adaptive algorithm of the radial basis function neural network, and trains the network by the error variety. With the weight coefficients and network parameters automatically updated and the adaptive controller adjusted, the output of controlled nonlinear plants can track the ideal output completely. The simulation results show that the model reference adaptive control system based on RBF neural network has better control effect than the nonlinear U-model adaptive control system based on the gradient descent method

Neural control of nonlinear systems with composite adaptation for improved convergence of Gaussian networks

The use of composite adaptive laws for control of the ane class of nonlinear systems having unknown dynamics is proposed. These dynamics are approximated by Gaussian radial basis function neural networks whose parameters are updated by a composite law that is driven by both tracking and estimation errors. This is motivated by the need to improve the speed of convergence of the unknown parameters, hence resulting in better system performance. To ensure global stability despite the inevitable network approximation errors, the control law is augmented with a low gain sliding mode component and deadzone adaptation is used for the indirect part of the composite law. The stability of the system is analyzed and the effectiveness of the method is demonstrated by simulation.peer-reviewe

Research on parallel nonlinear control system of PD and RBF neural network based on U model

The modelling problem of nonlinear control system is studied, and a higher generality nonlinear U model is established. Based on the nonlinear U model, RBF neural network and PD parallel control algorithm are proposed. The difference between the control input value and the output value of the neural network is taken as the learning target by using the online learning ability of the neural network. The gradient descent method is used to adjust the PD output value, and ultimately track the ideal output. The Newton iterative algorithm is used to complete the transformation of the nonlinear model, and the nonlinear characteristic of the plant is reduced without loss of modelling precision, consequently, the control performance of the system is improved. The simulation results show that RBF neural network and PD parallel control system can control the nonlinear system. Moreover, the control system with Newton iteration can improve the control effect and anti-interference performance of the system

Research on Adaptive Neural Network Control System Based on Nonlinear U-Model with Time-Varying Delay

U-model can approximate a large class of smooth nonlinear time-varying delay system to any accuracy by using time-varying delay parameters polynomial. This paper proposes a new approach, namely, U-model approach, to solving the problems of analysis and synthesis for nonlinear systems. Based on the idea of discrete-time U-model with time-varying delay, the identification algorithm of adaptive neural network is given for the nonlinear model. Then, the controller is designed by using the Newton-Raphson formula and the stability analysis is given for the closed-loop nonlinear systems. Finally, illustrative examples are given to show the validity and applicability of the obtained results

NASA Center for Intelligent Robotic Systems for Space Exploration

NASA's program for the civilian exploration of space is a challenge to scientists and engineers to help maintain and further develop the United States' position of leadership in a focused sphere of space activity. Such an ambitious plan requires the contribution and further development of many scientific and technological fields. One research area essential for the success of these space exploration programs is Intelligent Robotic Systems. These systems represent a class of autonomous and semi-autonomous machines that can perform human-like functions with or without human interaction. They are fundamental for activities too hazardous for humans or too distant or complex for remote telemanipulation. To meet this challenge, Rensselaer Polytechnic Institute (RPI) has established an Engineering Research Center for Intelligent Robotic Systems for Space Exploration (CIRSSE). The Center was created with a five year $5.5 million grant from NASA submitted by a team of the Robotics and Automation Laboratories. The Robotics and Automation Laboratories of RPI are the result of the merger of the Robotics and Automation Laboratory of the Department of Electrical, Computer, and Systems Engineering (ECSE) and the Research Laboratory for Kinematics and Robotic Mechanisms of the Department of Mechanical Engineering, Aeronautical Engineering, and Mechanics (ME,AE,&M), in 1987. This report is an examination of the activities that are centered at CIRSSE