Dual-Channel Event-Triggered Output Feedback Control for Linear System with Unavailable States

This paper investigates the problem of event-triggered dissipative observer-based output feedback control for a class of Takagi-Sugeno (T-S) fuzzy singular systems with unavailable states, time-varying delays, and imprecise membership functions. The considered singular system is presented as a T-S fuzzy singular system which gives a more realistic presentation for a variety of non-linear dynamical systems than conventional state space representation. The general framework we designed is based on a fuzzy observer-based controller with time varying delay along with extended dissipativity, which provides the H∞, L2-L∞ and dissipative performance indices and is robust against the disturbances and time-varying delay. Moreover, a novel Lyapunov-Krasovskii functional is adopted to analyze the closed loop stability of the fuzzy system. The solvability of Lyapunov-Krasovskii functional results in the formation of linear matrix inequalities. The controller and observer gains can be obtained by solving the linear matrix inequalities. Simulations are performed to validate the proposed scheme.

This article investigates the adaptive neural network (NN) event-triggered containment control problem for a class of nonlinear multiagent systems (MASs). Since the considered nonlinear MASs contain unknown nonlinear dynamics, immeasurable states, and quantized input signals, the NNs are adopted to model unknown agents, and an NN state observer is established by using the intermittent output signal. Subsequently, a novel event-triggered mechanism consisting of both the sensor-to-controller and controller-to-actuator channels are established. By decomposing quantized input signals into the sum of two bounded nonlinear functions and based on the adaptive backstepping control and first-order filter design theories, an adaptive NN event-triggered output-feedback containment control scheme is formulated. It is proved that the controlled system is semi-globally uniformly ultimately bounded (SGUUB) and the followers are within a convex hull formed by the leaders. Finally, a simulation example is given to validate the effectiveness of the presented NN containment control scheme.

This paper addresses periodic event-triggered output-feedback control (PETOFC) and event-triggered state-feedback control (ETSFC) for polynomial systems modeled by a linear-like representation with state-dependent coefficients. Periodic event-triggering evaluates conditions at fixed intervals, preventing Zeno behavior, while state-feedback control guarantees a minimum inter-event interval. Stability is analyzed using linear matrix inequalities. Under the proposed event-triggered controllers and using the sum-of-squares programming, the asymptotic stability of the closed-loop systems is ensured. Finally, the effectiveness of the proposed controllers are illustrated through two numerical examples.

This paper investigates the problem of event-triggered dynamic output feedback control for networked Takagi-Sugeno (T-S) fuzzy systems with asynchronous premise variables. Our attention is focused on the design of an event-triggered fuzzy dynamic output feedback controller that guarantees that the closed-loop system is asymptotically stable. First, with consideration of the limited bandwidth and restricted network resource, an event-triggered communication scheme is proposed to save the limited network resource. Second, different from the existing results in the literature, a fuzzy dynamic output feedback controller with asynchronous premise variables is constructed such that the premise membership functions are not necessarily the same as the networked T-S fuzzy systems. Then, by using the Wirtinger inequality and some slack matrices, a less conservative stability and stabilization conditions for the existence of fuzzy dynamic output feedback controller are derived in the form of linear matrix inequalities, which can be solved by the standard MATLAB toolbox. Finally, an example is given to demonstrate the effectiveness of the designed event-triggered fuzzy dynamic output feedback controller.

Distributed robust event-triggered adaptive control for a class of uncertain nonlinear multi-agent systems with actuator saturation

This paper investigates the event-triggered train formation control problem for multiple autonomous surface vehicles (ASVs) formation system in polar communication interference environment. Firstly, a distributed resilient guidance algorithm is introduced to generate the reference route based on waypoints. In the guidance algorithm, the distributed resilient leader predictor (RLP) is applied to obtain the states of ice-breaking ship when communication fails, and the resilient train formation scheme is designed to compute the reference signals for ASVs. Subsequently, an adaptive neural event-triggered train formation control algorithm is developed. In the control algorithm, the neural networks (NNs) are conducted to approximate model uncertainties, and event-triggered control (ETC) is employed to minimize controller updates. Furthermore, the threshold of the event-triggered mechanism (ETM) can be dynamically adjusted by states of system. It is proved that the formulated algorithm can ensure the prediction errors converge and multiple ASVs system is stable in polar communication interference environment. Finally, two simulation experiments are adopted to illustrate the effectiveness of the proposed algorithm.

This paper investigates the problem of event-triggered networked predictive control for systems with data loss. An event-triggered networked predictive control system is proposed. Based on predictive control model, a data loss compensation strategy is presented and an extended event-triggered transmission mechanism is developed. The closed-loop event-triggered predictive control system is described as a switched system and sufficient closed-loop stability conditions related to event-triggered mechanism are established. Under the event-triggered networked predictive control scheme, the consumption of the communication resources is reduced. Finally, an example is provided to illustrate the effectiveness of the proposed method.

Based on our discussions on event-triggered ESO in Chap. 3, we further consider the problem of event-triggered ADRC design in this chapter. Specifically, we consider the problem of event-triggered sampled-data control for networked systems. To do this, a nonlinear continuous-time system with uncertainty and disturbance is introduced, for which an event-triggered control problem based on an ADRC approach is formulated. For this problem, we consider two different scenarios. First, we investigate the scenario that the control signal is transmitted at each event instant while the output measurements are transmitted in real time. For this scenario, an easy-to-implement event-triggering strategy based on the state observation information provided by an ESO is proposed, for which the asymptotic boundedness of the observation error is obtained and the stability of the closed-loop system can be guaranteed. By introducing some mild restrictions on the initial values of the observer and plant, the boundedness of observation error can be maintained at any time.

Derivative-based event-triggered control for networked systems with quantization

In this chapter, we investigate a class of constrained nonlinear networked control systems (NCSs) subject to external disturbances, input and state constraints, and network-induced constraints. From a practical perspective, the network-induced constraints considered include the time delays and packet dropouts on both the sensor-to-controller (S-C) channel and the controller-to-actuator (C-A) channel simultaneously. The min-max RHC method is proposed to design the control packets by incorporating the external disturbances into the optimization problem. Moreover, the input-to-state practical stability (ISpS) of the resulting nonlinear NCS is established by constructing a novel Lyapunov function.

The accumulated effect of channel noise, including sensor‐to‐controller (SC) noise and controller‐to‐actuator (CA) noise, has a significant impact on the convergence performance of iterative learning control (ILC) systems over wireless networks. In this study, the relation between input error, channel noise and learning gain is derived, which reveals the fact that the contribution of the SC noise and the CA noise to the input error are all influenced by the learning gain. Based on this discovery, a method is proposed to improve the convergence performance of the ILC system when the SC noise and CA noise are independent and Gaussian distributed. Specifically, this method adaptively selects the learning gain through minimising the trace of input error covariance matrix. With the adaptively selected learning gain, the convergence performance of the ILC system is improved significantly. Moreover, the effect of channel noise variance on the convergence speed of the ILC system with the proposed method is analysed theoretically. Finally, numerical experiments are given to illustrate the effectiveness of the proposed method and corroborate the theoretical analysis, respectively.

This paper considers the mixed H 2 /H ∞ control of networked control systems where random time delays existing in sensor-to-controller (S-C) and controller-to-actuator (C-A) links are modeled by Markov chains. The designed output feedback controller is two-mode-dependent, which depends on the available S-C and C-A delay information. The closed-loop system is formulated as a special jump linear system. The definitions of the H 2 and H ∞ norms are further proposed to reflect the special characteristics of the system. The H 2 and mixed H 2 /H ∞ control problems are solved via the linear matrix inequality (LMI) optimization approaches. Simulation examples illustrate the effectiveness of the proposed methods.

This article investigates the secure control strategy for networked switched systems against multiple denial‐of‐service (DoS) attackers by employing a two‐level Stackelberg game approach. Specifically, the sensor‐to‐controller (SC) DoS attacker group, the controller, and the controller‐to‐actuator (CA) DoS attacker group are modeled as three players in a game‐theoretic framework. Based on their respective positions in the network transmission process, a two‐level Stackelberg game is formulated. The sequence of actions is defined as follows: the SC DoS attacker group acts first, followed by the controller, and finally the CA DoS attacker group. The attack strength coefficient is introduced to quantify the contributions of individual attackers within a multiple‐attacker group. The packet loss rates and the controller gains are utilized to construct strategy sets for the attackers and the controller, respectively. Moreover, an algorithm is developed to compute the corresponding two‐level Stackelberg equilibrium based on the convex optimization approach. To handle the open‐loop and asynchronous switching scenarios caused by DoS attacks, sufficient conditions based on dwell‐time (DT) condition are derived to ensure the mean‐square stability. Moreover, considering the nonconvexity and strong nonlinearity of optimizing the switching signal, an improved particle swarm optimization (PSO) algorithm incorporating DT‐constrained decoding method is proposed to enhance the system performance. Finally, a practical example involving a continuous stirred tank reactor is presented to validate the effectiveness and superiority of the proposed methods.

In this paper, the event-triggered dynamic output feedback controller design problem is addressed for a class of discrete-time nonlinear systems in network environment. An augmented closed-loop system is constructed and the sufficient conditions to ensure the mean-square asymptotically stability of the closed-loop system with a Hankel-norm performance by considering the event-triggered condition and using the reciprocally convex technique. It is shown that the solution of the fuzzyrule-dependent dynamic output feedback controller parameters can be obtained by variable substitution method. An example is provided to demonstrate the effectiveness of the proposed design method.

The investigation uses a state estimation fuzzy technique to address the problem of dynamic event-triggered secure control in Markov jump neural networks with time-varying delays subject to cyber-attacks. Two different deception attacks are given, each employing stochastic variables with a Bernoulli distribution. Specifically, this article presents a dynamic event-triggered strategy and energy restrictions to reduce network traffic and protect network resources during deception attacks. To prevent the system state from becoming unavailable, a state estimator is concurrently developed using the output signal. The application of Lyapunov stability theory yields sufficient criteria that ensure neural network stability. The study also accurately describes the controller gain's by solving Linear matrix inequality. Numerical examples are provided to demonstrate the theoretical results.