Event-triggered Synchronization of Multi-agent Systems with Partial Input Saturation

This paper is concerned with the distributed event/self-triggered synchronization problem for general linear multi-agent systems with partial input saturation. Both the event-based and self-triggered laws are designed using the local sampled, possibly saturated, state, which ensures the bounded synchronization of the multi-agent systems, and exclusion of the Zeno-behavior. The continuous communication between agents is avoided under these triggering protocols. Different from the existing related works, we show the fully distributed design for multi-agent systems, where the synchronization criteria, the designed input laws, and the proposed triggering protocols do not depend on any global information of the communication topology. In addition, the computation load of multi-agent systems is reduced significantly

Event-triggered synchronization of saturated lur’e type systems

This dissertation addresses the problem of master-slave synchronization of nonlinear discrete-time Lur’e systems subject to input saturation via event-triggered control (ETC) techniques. Synchronization, which is considered a remarkable property in the physics literature specially when chaotic systems are under investigation, is achieved through the stabilization of the error between the states of the master and the slave system. Regarding the Lur’e type nonlinearity, two different cases are studied along this work: generic slope-restricted state-dependent nonlinearity and piecewise-affine function. In the ETC paradigm, the control signal is updated aperiodically only after the occurrence of an event, which is generated according to a triggering criterion that depends on the evaluation of a triggering function. In the emulation-based design, a synchronization error feedback controller is given a priori and the task is to compute the event generator parameters ensuring performance and closed-loop stability. On the other hand, in the co-design approach the event generator and the control law are simultaneously designed. Theoretical results are obtained for three types of event-triggered mechanism (ETM), namely: static, dynamic and relaxed. In the last case, practical synchronization conditions are derived as form of ultimately boundedness stability. In order to tune the parameters of the event-based strategy, optimization problems are formulated aiming to reduce the number of control signal updates (number of events) with respect to a time-triggered implementation. Numerical simulations are presented to illustrate the application of the proposed methods.Esta dissertação aborda o problema de sincronização mestre-escravo de sistemas Lur’e não lineares de tempo discreto sujeitos à saturação de entrada via técnicas de controle baseado em eventos. A sincronização, que é considerada uma propriedade importante na literatura de Física especialmente quando sistemas caóticos são investigados, é alcançada através da estabilização do erro entre os estados do mestre e do sistema escravo. Em relação à não linearidade do tipo Lur’e, dois casos diferentes são estudados ao longo do trabalho: não linearidade genérica dependente do estado e restrita em inclinação e função afim por partes. No paradigma de controle baseado em eventos (ETC), o sinal de controle é atualizado aperiodicamente apenas após a ocorrência de um evento, que é gerado de acordo com um critério de disparo que depende da avaliação de uma função de disparo. No projeto baseado em emulação, um controlador por realimentação do erro de sincronização é dado a priori e a tarefa é calcular os parâmetros do gerador de eventos garantindo desempenho e estabilidade em malha fechada. Na abordagem de co-design, o gerador de eventos e a lei de controle são projetados simultaneamente. Resultados teóricos são obtidos para três tipos de mecanismo de geração de eventos (ETM), nomeadamente: estático, dinâmico e relaxado. Neste último caso, condições de sincronização prática são derivadas como uma forma de estabilidade ultimamente limitada. Para sintonizar os parâmetros da estratégia baseada em eventos, problemas de otimização são formulados visando reduzir o número de atualizações do sinal de controle (número de eventos) em relação a uma implementação time-triggered. Simulações numéricas são apresentadas para ilustrar a aplicação dos métodos propostos

Observer-based fuzzy tracking control for an unmanned aerial vehicle with communication constraints

We investigate the trajectory tracking problem of underactuated aerial vehicles with unknown mass in the presence of unknown non-vanishing disturbances using an event-triggered approach, while considering the constraint that the derivative of the reference trajectory is not available. In contrast to existing references where the derivative of the reference trajectory is needed, here we first introduce a high-gain observer to estimate the unknown derivative solely from the reference trajectory. A disturbance observer is designed to compensate for non-vanishing disturbances, such as wind, etc. Fuzzy logic systems are used to approximate the model uncertainty arising from the unknown mass of the vehicle, and then we derive a thrust command law that follows from a desired stabilizing force. Additionally, unlike traditional fixed and relative threshold strategies that rely solely on control signals, we develop a new time-varying eventtriggered mechanism linked to the performance of the controlled system, taking into account factors such as tracking errors, to develop angular velocity commands, enhancing tracking accuracy while efficiently conserving communication resources, especially in the absence of Zeno behavior. We present simulation results to demonstrate the efficacy of the proposed approach and validate the theoretical findings.</p

Observer-based event-triggered and set-theoretic neuro-adaptive controls for constrained uncertain systems

In this study, several new observer-based event-triggered and set-theoretic control schemes are presented to advance the state of the art in neuro-adaptive controls. In the first part, six new event-triggered neuro-adaptive control (ETNAC) schemes are presented for uncertain linear systems. These comprehensive designs offer flexibility to choose a design depending upon system performance requirements. Stability proofs for each scheme are presented and their performance is analyzed using benchmark examples. In the second part, the scope of the ETNAC is extended to uncertain nonlinear systems. It is applied to a case of precision formation flight of the microsatellites at the Sun-Earth/Moon L1 libration point. This dynamic system is selected to evaluate the performance of the ETNAC techniques in a setting that is highly nonlinear and chaotic in nature. Moreover, factors like restricted controls, response to uncertainties and jittering makes the controller design even trickier for maintaining a tight formation precision. Lyapunov function-based stability analysis and numerical results are presented. Note that most real-world systems involve constraints due to hardware limitations, disturbances, uncertainties, nonlinearities, and cannot always be efficiently controlled by using linearized models. To address all these issues simultaneously, a barrier Lyapunov function-based control architecture called the segregated prescribed performance guaranteeing neuro-adaptive control is developed and tested for the constrained uncertain nonlinear systems, in the third part. It guarantees strict performance that can be independently prescribed for each individual state and/or error signal of the given system. Furthermore, the proposed technique can identify unknown dynamics/uncertainties online and provides a way to regulate the control input --Abstract, page iv

Distributed Event-Triggered Nonlinear Fusion Estimation under Resource Constraints

This paper studies the event-triggered distributed fusion estimation problems for a class of nonlinear networked multisensor fusion systems without noise statistical characteristics. When considering the limited resource problems of two kinds of communication channels (i.e., sensor-to-remote estimator channel and smart sensor-to-fusion center channel), an event-triggered strategy and a dimensionality reduction strategy are introduced in a unified networked framework to lighten the communication burden. Then, two kinds of compensation strategies in terms of a unified model are designed to restructure the untransmitted information, and the local/fusion estimators are proposed based on the compensation information. Furthermore, the linearization errors caused by the Taylor expansion are modeled by the state-dependent matrices with uncertain parameters when establishing estimation error systems, and then different robust recursive optimization problems are constructed to determine the estimator gains and the fusion criteria. Meanwhile, the stability conditions are also proposed such that the square errors of the designed nonlinear estimators are bounded. Finally, a vehicle localization system is employed to demonstrate the effectiveness and advantages of the proposed methods.Comment: 15 pages,9 figures. The first draft was completed in June 2021, and this is the revised versio

Non-fragile estimation for discrete-time T-S fuzzy systems with event-triggered protocol

summary:This paper investigates the non-fragile state estimation problem for a class of discrete-time T-S fuzzy systems with time-delays and multiple missing measurements under event-triggered mechanism. First of all, the plant is subject to the time-varying delays and the stochastic disturbances. Next, a random white sequence, the element of which obeys a general probabilistic distribution defined on $[0,1]$, is utilized to formulate the occurrence of the missing measurements. Also, an event generator function is employed to regulate the transmission of data to save the precious energy. Then, a non-fragile state estimator is constructed to reflect the randomly occurring gain variations in the implementing process. By means of the Lyapunov-Krasovskii functional, the desired sufficient conditions are obtained such that the Takagi-Sugeno (T-S) fuzzy estimation error system is exponentially ultimately bounded in the mean square. And then the upper bound is minimized via the robust optimization technique and the estimator gain matrices can be calculated. Finally, a simulation example is utilized to demonstrate the effectiveness of the state estimation scheme proposed in this paper

On the benefits of saturating information in consensus networks with noise

In a consensus network subject to non-zero mean noise, the system state may be driven away even when the disagreement exhibits a bounded response. This is unfavourable in applications since the nodes may not work properly and even be faulty outside their operating region. In this paper, we propose a new control algorithm to mitigate this issue by assigning each node a favourite interval that characterizes the nodes desired convergence region. The algorithm is implemented in a self-triggered fashion. If the nodes do not share a global clock, the network operates in a fully asynchronous mode. By this algorithm, we show that the state evolution is confined around the favourite interval and the node disagreement is bounded by a simple linear function of the noise magnitude, without requiring any priori information on the noise. We also show that if the nodes share some global information, then the algorithm can be adjusted to make the nodes evolve into the favourite interval, improve on the disagreement bound and achieve asymptotic consensus in the noiseless case