Event-Triggered Output Feedback Control of Switched Nonlinear Systems With Input Saturation

Global Regulation of Feedforward Nonlinear Systems: A Logic-Based Switching Gain Approach

In this article, we investigate the global regulation problem for a class of feedforward nonlinear systems. Notably, the systems under consideration allow unknown input-output-dependent nonlinear growth rates, which has not been considered in existing works. A novel logic-based switching (LBS) gain approach is proposed to counteract the system uncertainties and nonlinearities. The key idea of the proposed approach is that whenever the Lyapunov function does not decrease as desired, the switching mechanism is activated so that a new gain is adopted. Furthermore, a tanh-type speed-regulation function is embedded into the switching mechanism for the first time to improve the convergence speed and transient performance. Then, a switching adaptive output feedback (SAOF) controller, which is of concise form and low complexity, is proposed based on the developed switching mechanism. It is shown that the objective of global regulation is achieved with faster convergence speed and better transient performance under the proposed controller. Moreover, it is also shown that the proposed control approach can be extended with the enhanced switching mechanism to deal with feedforward nonlinear systems with external disturbances. Finally, numerical examples are presented to demonstrate the effectiveness and advantages of our approach in comparison with the existing approaches.

Adaptive Dynamic Event-Triggered Tracking Control for Uncertain Switched Nonlinear Systems Under State-Dependent Switching

This article introduces an adaptive dynamic event-triggered technique for addressing the output tracking control problem of uncertain switched nonlinear systems with prescribed performance. First, a switching dynamic event-triggered mechanism (SDETM) is established to alleviate network burden and conserve computational resources. A notable aspect is the inclusion of asynchronous switching between the switching subsystems and controllers. Second, a state-dependent switching law ensuring a dwell-time constraint is designed, which avoids the frequent switching phenomenon within any finite time interval. Third, an SDETM and an adaptive dynamic event-triggered controller are developed to confine the output tracking error within predefined decaying boundaries, while ensuring that all the signals of the closed-loop switched system remain within bounded regions. Finally, the validity and applicability of the developed control scheme are demonstrated through a one-link manipulator example.

Event-Triggered Adaptive Neural Network Tracking Control for Uncertain Systems With Unknown Input Saturation Based on Command Filters

This brief presents a modified event-triggered command filter backstepping tracking control scheme for a class of uncertain nonlinear systems with unknown input saturation based on the adaptive neural network (NN) technique. First, the virtual control functions are reconstructed to address the uncertainties in subsystems by using command filters. A piecewise continuous function is employed to deal with the unknown input saturation problem. Next, an event-triggered tracking controller is developed by utilizing the adaptive NN technique. Compared with standard NN control schemes based on multiple-function-approximators, our controller only requires a single NN. The closed-loop system stability is analyzed based on the Lyapunov stability theorem, and it is shown that the Zeno behavior is also avoided under the designed event-triggering mechanism. Simulation studies are performed to validate the effectiveness of our controller.

Asymptotic adaptive output feedback event-triggered control of uncertain strict-feedback nonlinear systems with sensors triggering

In this paper, event-triggered output feedback control of a class of high-order nonlinear strict-feedback systems with parametric uncertainties is investigated, in which both of the controller and the parameter estimator are triggered based on a set of event-triggered conditions. Firstly a new one-step control design framework is proposed for the strict-feedback nonlinear systems, therefore both expressions of the controller and the parameter estimate laws are much more simple than those of the recursive design approaches such as backstepping control. Secondly observers are designed to estimate the unknown states, and a set of event-triggering mechanism is proposed for the sensors such that the states are transmitted through the communication network only at the triggering points. The estimated parameter is obtained without real-time integration due to the event-triggered estimator. It is proved that our proposed control law guarantees the closed-loop system is globally bounded and the system output converges to zero asymptotically. It is also proved that the Zeno behavior is excluded. Simulation results demonstrate the effectiveness of the proposed control scheme.

Saturation-Tolerant Prescribed Control for a Class of MIMO Nonlinear Systems

This article proposes a saturation-tolerant prescribed control (SPC) for a class of multiinput and multioutput (MIMO) nonlinear systems simultaneously considering user-specified performance, unmeasurable system states, and actuator faults. To simplify the control design and decrease the conservatism, tunnel prescribed performance (TPP) is proposed not only with concise form but also smaller overshoot performance. By introducing non-negative modified signals into TPP as saturation-tolerant prescribed performance (SPP), we propose SPC to guarantee tracking errors not to violate SPP constraints despite the existence of saturation and actuator faults. Namely, SPP possesses the ability of enlarging or recovering the performance boundaries flexibly when saturations occur or disappear with the help of these non-negative signals. A novel auxiliary system is then constructed for these signals, which bridges the associations between input saturation errors and performance constraints. Considering nonlinearities and uncertainties in systems, a fuzzy state observer is utilized to approximate the unmeasurable system states under saturations and unknown actuator faults. Dynamic surface control is employed to avoid tedious computations incurred by the backstepping procedures. Furthermore, the closed-loop state errors are guaranteed to a small neighborhood around the equilibrium in finite time and evolved within SPP constraints although input saturations and actuator faults occur. Finally, comparative simulations are presented to demonstrate the feasibility and effectiveness of the proposed control scheme.