Adaptive Event-triggered Control Research Articles

Event-Triggered Asymptotic Tracking Control of Underactuated Ships With Prescribed Performance

Adapting to the navigational high-precision tracking tasks, this paper develops an event-triggered adaptive neural asymptotic tracking control framework for underactuated ships. The prescribed performance control (PPC) technique is employed to address spatial constraints in navigation, where the positional tracking errors are transformed via the transformation functions. By using the approximation of radial basis function neural networks (RBF NNs) in the form of minimum learning parameters (MLPs), the uncertainties coming from the unknown model dynamics, the environmental disturbances and the derivation of virtual control laws are offset all together, and a succinct computation is guaranteed. Assisted by the property of neural basis function, the “algebraic loop” problem in the backstepping design is released. To achieve the asymptotic tracking performance, the hyperbolic tangent functions are incorporated into the control laws, which are characterized by the integral-bounded terms. The event-triggered control (ETC) is designed in the controller-to-actuator (CA) channel. Two separate triggering conditions are constructed for the surge and the yaw motions respectively, which are characterized by the compound thresholds composed of a variable and a constant. The “Zeno” behaviors can thereby be avoided. The proposed scheme has three notable characteristics: 1) the computational complexity is reduced by using the MLP technique with lumped uncertainties; 2) the high-precision tracking performance can be achieved through the asymptotic tracking control; 3) the practical problems of spatial constraints and communication burdens can be solved by fabricating a uniform control framework including PPC and ETC. With the aid of direct Lyapunov candidates and the Barbalat’s lemma, the asymptotic convergence of all the tracking errors is proved. Finally, a numerical experiment corroborates the feasibility of the proposed scheme.

Adaptive Event-Triggered Consensus Tracking Control Schemes for Uncertain Constrained Nonlinear Multi-Agent Systems

Adaptive Event-Triggered Consensus Tracking Control Schemes for Uncertain Constrained Nonlinear Multi-Agent Systems

Neural-Network-Based Adaptive Tracking Control for Nonlinear Multiagent Systems: The Observer Case.

This article focuses on the neural-network (NN)-based adaptive tracking control issue for a class of high-order nonlinear multiagent systems both subjected to the immeasurable state variables and unknown external disturbance. Combining with the radial basis function NNs (RBF NNs), the composite disturbance observer and state observer for each follower are established, respectively. The purpose of this work is to develop NN-based adaptive tracking control schemes such that the output of each follower ultimately tracks that of the leader and all the signals of the closed-loop systems are semiglobally uniformly ultimately bounded by utilizing the backstepping technique. Furthermore, so as to cope with the sparsity of the control resources, the proposed method is extended to the event-triggered case and the adaptive event-triggered tracking control protocol is formulated for nonlinear multiagent systems. Finally, the numerical example is performed to verify the efficacy of the proposed approach.

Adaptive Event-Triggered Vibration Control for Crane Bridge System With Trolley

Adaptive Event-Triggered Vibration Control for Crane Bridge System With Trolley

Adaptive Secure Practical Fault-tolerant Output Regulation of Multiagent Systems With DoS Attacks by Asynchronous Communications

The secure practical fault-tolerant output regulation of multiagent systems (MASs) with denial-of-service (DoS) attacks is considered in this paper. Different from most existing output regulation problems: 1) The leader matrix is only known to a part of followers; 2) The communication networks are unreliable and there exist DoS attacks; 3) Actuator faults exist in the MASs. Firstly, to overcome the impact of DoS attacks, the adaptive edge-event-triggered observer is designed to estimate the leader's matrix and state. The novel edge-event-triggered mechanism enables asynchronous intermittent communication between agents, and the adaptive learning rate makes the observer design independent of the global information for the communication topology. Secondly, in order to compensate for the impact of actuator faults on system stability, a novel resilient adaptive event-triggered fault-tolerant controller is designed, and it has zero-order holder (ZOH) characteristics and avoids chattering phenomenon. Finally, two simulation examples well show the effectiveness of our developed methods.

Adaptive Event-Triggered Path Tracking Control for Self-Driving Vehicles Against Denial-of-Service Attacks

Adaptive Event-Triggered Path Tracking Control for Self-Driving Vehicles Against Denial-of-Service Attacks

Adaptive Event-Triggered Robust Model Predictive Control for Multiagent Systems With Time-Varying Delay Under Arbitrary Network Topology

Adaptive Event-Triggered Robust Model Predictive Control for Multiagent Systems With Time-Varying Delay Under Arbitrary Network Topology

Decentralized Event-Triggered Adaptive Control for Interconnected Nonlinear Systems With Actuator Failures

In this article, the problem of decentralized event-triggered fault-tolerant control (FTC) for a class of interconnected nonlinear systems with unknown strong coupling and actuator failures is considered. In order to enable each subsystem output to track the desired trajectory, a new decentralized adaptive control scheme is given. First, an event-triggering mechanism is introduced to reduce the signal transmission frequency between the controller and the actuator. Second, a fuzzy high-gain observer is designed for each subsystem to estimate unknown nonlinear functions and actuator efficiency factor. Third, a decentralized FTC strategy is proposed to compensate for the effects of actuator failures and achieve the desirable system tracking performance. With the aid of graph theory, it is shown that all the closed-loop signals are semiglobally uniformly ultimately bounded, and the tracking error of each subsystem can converge to an arbitrarily small residual set by adjusting a design parameter. The effectiveness of the proposed scheme is demonstrated by a practical interconnected system.

Dynamic Event-triggered Adaptive Control for Uncertain Nonlinear Switched Systems Based on MDADT Method

Dynamic Event-triggered Adaptive Control for Uncertain Nonlinear Switched Systems Based on MDADT Method

Adaptive Event-Triggered Control of Stochastic Nonlinear Systems With Unknown Dead Zone

We consider the tracking control problem for a class of stochastic nonlinear systems in strict-feedback structure via output feedback signal in this article. The controlled plant is assumed subject to unknown dead-zone input. By utilizing the fact that the unknown dead-zone input function can be modeled as a time-varying nonlinear function and a bounded disturbance, and selecting appropriate design parameters, we show that the effect of unknown dead zone can be compensated. We design a fuzzy state observer via fuzzy logic modeling technique to estimate the unknown system states. Then, we prove, via Lyapunov stability analysis, that the controlled plant is bounded in probability. In addition, all the signals in the closed-loop system are guaranteed to be globally bounded in probability. The tracking errors are also ensured to converge to a small neighborhood of the origin. Finally, to show the effectiveness of the proposed control strategy, a simulation example of one-link manipulator is presented in the simulation section.

Aperiodically Intermittent Adaptive Event-triggered Control for Linear Multi-agent Systems

Aperiodically Intermittent Adaptive Event-triggered Control for Linear Multi-agent Systems

Distributed adaptive event-triggered control for general linear singular multi-agent systems

<abstract><p>This paper investigates the leader-following consensus of general linear singular multi-agent systems. A fully distributed adaptive event-triggered control protocol is first proposed by using the relative state estimate information between neighboring agents. Moreover, the proposed protocol does not require continuous communication between neighbors, which greatly alleviate the negative impact of communication. In addition, a novel adaptive gain is proposed, which will avoid using the Laplace of communication graph. Zeno behavior is excluded by proving that the inter-event times are lower bounded by a positive constant. Finally, a numerical simulation is proposed to verify the effectiveness and reliability of the protocol.</p></abstract>

Prescribed Performance Bipartite Consensus Control for Stochastic Nonlinear Multiagent Systems Under Event-Triggered Strategy.

In this article, the event-triggered bipartite consensus problem for stochastic nonlinear multiagent systems (MASs) with unknown dead-zone input under the prescribed performance is studied. To surmount the influence of the dead-zone input, the dead-zone model is transformed into a linear term and a disturbance term. Meanwhile, the prescribed tracking performance is realized by developing a speed function, which means that all tracking errors of MASs can converge to a predefined set in a given finite time. Moreover, the unknown nonlinear dynamics are approximated by fuzzy-logic systems. By combining the dynamic surface approach and the Lyapunov stability theory, we design an adaptive event-triggered control algorithm, such that the bipartite consensus problem of stochastic nonlinear MASs can be achieved, and all signals are semiglobally uniformly ultimately bounded in probability of the closed-loop systems. Finally, simulation examples are proposed to verify the feasibility of the algorithm.

Event-triggered adaptive NN practical finite-time control with its application to MPCVD reactors

The practical finite-time control problem of uncertain nonlinear systems is investigated in this paper. To address the uncertain nonlinearities of the system, neural networks are introduced to approximate the lumped nonlinearities containing the system unknown functions. On the other hand, to alleviate the signal transmission pressure of the system, an improved event-triggered mechanism is presented to reduce the controller update frequency without degrading the control performance of the system. By using practical finite-time stability, it is obtained that the system tracking errors are practical finite-time stable without Zeno behavior. Finally, the effectiveness of the proposed method is verified by the simulation results of its application to a microwave plasma chemical vapor deposition (MPCVD) reactor system.

Dynamic event-triggered adaptive control for uncertain stochastic nonlinear systems

In this paper, the dynamic event-triggered adaptive tracking control problem is investigated via backstepping technology for uncertain stochastic nonlinear systems. First, the stochastic nonlinear system with unknown parameter is considered. By introducing an additional dynamic variable, a dynamic event-triggered adaptive controller is designed such that the closed-loop signals are uniformly ultimately bounded in the sense of the fourth moment. Then, a more general partially unknown stochastic nonlinear system is further considered, and the designed adaptive neural network control scheme ensures that the closed-loop signals are fourth moment semi-globally uniformly ultimately bounded (SGUUB). The proposed dynamic event-triggering mechanism (DETM) guarantees that the lengths of time intervals between each two consecutive events are lower-bounded by a positive constant. It is necessary to point out that the DETM is better at saving resources than the static event-triggering mechanism (SETM). Finally, two simulations are conducted to show the validity of the control strategies for these two systems, respectively.

Event-triggered-based adaptive asymptotic tracking control for stochastic nonlinear time-delay systems with actuator and sensor faults

The issue of dynamic event-triggered adaptive asymptotic tracking control for stochastic nonlinear time-delay systems with actuator and sensor faults is investigated in this article. Utilizing fuzzy logic systems and projection operator, unknown time delays are calculated. Compared with the existing methods to overcome the effect of time delay, the estimation of the time delay information is considered to avoid the controller singularity problem. Moreover, to reckon the upper bound of fuzzy logic system approximation errors and other residuals, an error adaptive law is developed in the controller construction. With the assistance of stochastic Barbalat’s lemma and adaptive fuzzy control strategy, a novel asymptotic tracking fuzzy controller is proposed under multiple faults, which guarantees that the whole signals are bounded in probability. Besides, a dynamic event triggering control (DETC) is employed to save the communication resources. A practical example is supplied to demonstrate the effectiveness of the proposed control approach.

Adaptive event-triggered fuzzy control for hot strip finishing mill via a disturbance observer-based method

In this paper, a disturbance observer-based adaptive event-triggered control scheme is proposed for hot strip finishing mill against the problem brought by the interstand tension and the looper angle. Firstly, a standard backstepping method is adopted to the design of control scheme with the consideration that the interstand looper possesses particular structure of the dynamic model. Then, via selecting a suitable gain function, the unknown disturbance can be estimated well by the disturbance observer. Meanwhile, it is noted that the looper current control has very fast dynamics when generating the looper torque. Therefore, the actuator torque can be viewed as directly related to the control signal. In addition, since the strip tension fluctuation has an effect on width and thickness, it will be dampened until larger than looper angle fluctuation. Furthermore, unknown nonlinear terms generated by the process of finishing mills can be approximated by linear functions integrated by fuzzy logic systems. The proposed event-triggered control scheme is used to guarantee the Lyapunov stability and performance requirements of the overall system. The effectiveness of the proposed controller is illustrated in simulation results and application.

Global adaptive event-triggered output feedback controller design for high-order nonlinear systems

This article focuses on the adaptive event-triggered output feedback stabilization problem for a class of high-order systems with uncertain output function. Firstly, an adaptive event-triggered mechanism with a dynamic gain is designed for the nominal system. Then the gain is employed into the observer and event-triggered controller to dominate the nonlinearities. Thirdly, it is proved that all system states converge to zero and the Zeno-behavior is excluded. Finally, a numerical example reveals the effectiveness of the proposed event-triggered control strategy.

Adaptive event-triggered dynamic distributed control of switched positive systems with switching faults

This paper designs the dynamic output-feedback controller of switched positive systems subject to switching faults using an improved adaptive event-triggering mechanism. An adaptive event-triggering condition is addressed in the form of 1-norm by virtue of the measurable outputs of distributed sensors and the corresponding error. An error-based closed-loop control system whose dynamic variable relies on a state observer is obtained. A multiple copositive Lyapunov function is constructed to deal with the positivity and stability of the systems. The matrix decomposition and linear programming approaches are used to design and compute the controller and observer gains. An improved average dwell time scheme is proposed to handle the switching faults. The contributions of this paper lie in that: (i) An adaptive event-triggering mechanism is established for switched positive systems, (ii) A framework on the fault of switching signal is constructed, and (iii) A dynamic distributed controller is proposed for the considered systems. Finally, two illustrative examples are given to verify the effectiveness of the obtained results.

Fixed-time Adaptive Event-triggered Control for a Class of Uncertain Nonlinear Systems with Input Hysteresis

Fixed-time Adaptive Event-triggered Control for a Class of Uncertain Nonlinear Systems with Input Hysteresis