Variable neural networks for adaptive control of nonlinear systems

This paper is concerned with the adaptive control of continuous-time nonlinear dynamical systems using neural networks. A novel neural network architecture, referred to as a variable neural network, is proposed and shown to be useful in approximating the unknown nonlinearities of dynamical systems. In the variable neural networks, the number of basis functions can be either increased or decreased with time, according to specified design strategies, so that the network will not overfit or underfit the data set. Based on the Gaussian radial basis function (GRBF) variable neural network, an adaptive control scheme is presented. The location of the centers and the determination of the widths of the GRBFs in the variable neural network are analyzed to make a compromise between orthogonality and smoothness. The weight-adaptive laws developed using the Lyapunov synthesis approach guarantee the stability of the overall control scheme, even in the presence of modeling error(s). The tracking errors converge to the required accuracy through the adaptive control algorithm derived by combining the variable neural network and Lyapunov synthesis techniques. The operation of an adaptive control scheme using the variable neural network is demonstrated using two simulated example

Motion control and vibration suppression of flexible lumped systems via sensorless LQR control

This work attempts to achieve motion control along with vibration suppression of flexible systems by developing a sensorless closed loop LQR controller. Vibration suppression is used as a performance index that has to be minimized so that motion control is achieved with zero residual vibration. An estimation algorithm is combined with the regular LQR to develop sensorless motion and vibration controller that is capable of positioning multi degrees of freedom flexible system point of interest to a pre-specified target position with zero residual vibration. The validity of the proposed controller is verified experimentally by controlling a sensorless dynamical system with finite degrees of freedom through measurements taken from its actuator