In this paper, we investigate the problem of output feedback control for a class of uncertain nonlinear systems with event-triggered input. The considered system contains not only unknown system parameters, but also general nonlinear functions which are not required to be globally Lipschitz, in contrast to most of the existing results in the area. Besides providing two different event-triggered strategies without input-to-state stable (ISS) assumption with respect to the measurement errors, we propose a new way to encode and decode the event-triggered control signals to further decrease the communication rate. With our newly-proposed encoding-decoding mechanism, each time when the triggering event is violated, only one-bit signal, either 1 or 0, is rendered to transmit through the communication channel between the controller and actuator. Clearly, this signal transmission mechanism is more effective and consumes less channel bandwidth. Through Lyapunov analyses, it is proved that the boundedness of all the signals is ensured and the output signal can be regulated to a compact set around zero which is adjustable.

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.

Unknown failures and time delays of actuators which may degrade system performance seem inevitable in practical systems. However, the available results to compensate for unknown failures of time delay actuators based on adaptive approaches are very limited. In this paper, we address such a problem by considering controlling a class of second-order systems with unknown actuator failures and input delays. Firstly, the controlled system is transformed into a triangular structure model. Meanwhile, the input time delay is transformed into dynamics of the output signal. Then, an adaptive controller is developed using backstepping. Not only can the uncertainties due to actuator failure be compensated, but the unknown input delay has also been effectively restrained under the proposed control scheme. The upper bound condition of strength scalar μ is proposed. It is a sufficient condition for system stability. When the strength scalar satisfies this condition, the design parameters exist and the system is stable under the controlling of the proposed controller. Finally, the effectiveness of the proposed control scheme is verified through simulation results, including numerical simulation and actual system simulation. Through the comparative simulation, it can be seen that the control scheme proposed in this paper has faster convergence speed.

In this paper, the event-triggered control problem is investigated using backstepping techniques for nonlinear systems with dead-zone input. The external disturbance and unknown parameters are also considered in the controller’s design. It is well known that errors in input signal measurements are inevitable. In event-triggered control, such errors will directly affect whether the control signal is updated. This measurement error can be seen in the form of interference to the threshold. Therefore, unlike traditional event-triggered control, the existence of threshold disturbance is considered in the controller’s design. The proposed controller can not only compensate for the uncertainties caused by external disturbance and unknown parameters but can also suppress the unknown effects caused by threshold interference. In addition, to obtain a continuous controller, a smooth function is constructed to approximate the discontinuous sign function. In this way, Zeno behavior is successfully avoided. The boundedness of all signals and the tracking performance of the system can be guaranteed by the proposed control scheme. Numerical simulation and actual system simulation demonstrate the effectiveness of the proposed control scheme. The comparative simulation results also verify this event-triggered controller’s advantages, including better tracking performance and fewer trigger times.