In this paper, a decentralized adaptive control scheme is proposed based on backstepping techniques for a class of uncertain interconnected systems with unknown modeling errors and interactions. Unlike existing results, the functions bounding modeling errors and interactions are not required to meet the triangular condition. Namely, such functions are allowed to depend on all the states of the interconnected system. It is shown that the proposed adaptive control scheme can ensure all signals in the closed-loop system bounded. Simulation studies verify the effectiveness of the proposed scheme.

In this paper, a neural network-based adaptive iterative learning control scheme is developed to solve the trajectory tracking problem for rigid robot manipulators with arbitrary initial errors. Time-varying boundary layers are used to relax the zero initial error condition which must be observed in traditional iterative learning control design, and adaptive learning neural networks are constructed to approximate uncertainties in robotic systems, whose optimal weights are estimated by using partial saturation difference learning method. For arbitrary bounded initial state errors, the tracking error of robot manipulators will asymptotically converge to a tunable residual set as the iteration number increases. An illustrative example and the comparisons are provided to demonstrate the effectiveness of the proposed neural network-based adaptive iterative learning control scheme.

In this paper, a novel neural network-based error-track iterative learning control scheme is proposed to tackle trajectory tracking problem for tank gun control systems. Firstly, the system modeling for tank gun control systems is introduced as a preparation of controller design. Then, the reference error trajectory is constructed to deal with the nonzero initial error of iterative learning control. The adaptive iterative learning controller for tank gun control systems is designed by using Lyapunov approach. Adaptive learning neural network is adopted to approximate nonlinear uncertainties, with robust control technique being used compensate the approximation error and external disturbances. As the iteration number increases, the system error can follow the desired error trajectory over the whole time interval, which makes the system state accurately track the reference error trajectory during the predetermined part time interval. Numerical simulations demonstrate the effectiveness of the proposed iterative learning control scheme.

In this paper, an adaptive iterative learning control scheme is proposed to solve the trajectory-tracking problem for tank gun servo systems with input deadzone and arbitrary initial states. A time-varying boundary layer is constructed to deal with the nonzero initial error during the iterative learning controller design. Neural network control and robust control are jointly used to compensate uncertainties and deadzone nonlinearity. The ideal weight of neural network and the upper bound of noncontinuous uncertainties are estimated by using difference learning method. As the iteration number increases, the filtering error can converge to the time-varying boundary layer. All signal are guaranteed to be bounded. A simulation example is presented to verify the effectiveness of the proposed scheme.

In this paper, an error-tracking iterative learning control scheme is proposed for a class of nonlinearly parametric time-delay systems with initial state errors. The iterative learning controller is developed by Lyapunov approach. With a desired error trajectory constructed according to the given method, the robust control and learning control are synthetically applied to compensate for nonlinearly parametric time-delay uncertainties. While the closed-loop system operates, as iteration number increases, the controller can make system error perfectly follow its desired error trajectory over the entire time interval, by which, the system state can precisely track its reference signal in the pre-specified part of above-mentioned interval. Numerical results demonstrate the effectiveness of the proposed error-tracking learning control scheme.