Model-based Event-triggered Control Research Articles

Adaptive Command Filtered Control for a Class of Random Nonlinear Systems Under Model-Based Event-Triggered Control Design

Adaptive Command Filtered Control for a Class of Random Nonlinear Systems Under Model-Based Event-Triggered Control Design

Event-triggered output feedback control design for polynomial fuzzy systems

This paper presents a novel approach to event-triggered output feedback control of polynomial fuzzy systems that exhibit unmeasurable system states. To stabilize the system state, a fuzzy model-based event-triggered control scheme is proposed based on output measurement information. The transmission frequency is modulated using a periodic event-trigger protocol, which is scheduled by resorting to a set of historically released packets to ensure better dynamic performance. The event-triggering condition uses the current samples to determine the next trigger and also takes into account the triggering time-dependent gain parameter, thereby significantly reducing network burden. The proposed approach ensures the existence of both polynomial controller gain and event-triggered parameters by satisfying sufficient conditions based on the sum-of-squares approach and the polynomial fitting approximation algorithm. The addressed system’s stability conditions are solved using MATLAB SOSTOOLS. Three numerical examples, including an invented pendulum model, illustrate the superiority and applicability of the methodology. The main contributions of this work are the introduction of a triggering instant dependent periodic event-triggered output feedback control design, which reduces network traffic and enhances dynamic performance. The approach is validated through numerical examples, which demonstrate its superiority and applicability.

Model-based event-triggered control for linear systems using a finite-time convergent observer

In this paper, we present an improved model-based event-triggered control approach for regulation of networked linear time-invariant plants by integrating a finite-time convergent observer. Utilizing the send-on-delta concept, we introduce two event-triggering strategies that substantially decrease the number of samples transmitted over the sensor-to-controller communication channel: (1) one strategy guarantees global asymptotic stability of a tunable compact neighborhood of the origin with a finite number of sampling events; (2) the other strategy ensures global asymptotic stability of the origin with arbitrarily large inter-sampling time. Through the lens of hybrid systems theory, we demonstrate that both methods avoid Zeno solutions, making them suitable for digital implementation, and both strategies exhibit robustness against both small and large perturbations. We showcase the advantages of our proposed strategies over other model-based approaches using a perturbed linearized batch reactor example.

Stability Analysis for Model-Based Event-Triggered Nonlinear Control Systems Under DoS Attacks

In this paper, we study the stability of a novel nonlinear event-triggered control system under denial-of-service (DoS) attacks based on an ISS-Lyapunov function analysis. A new dual-mode model-based event-triggering strategy is proposed which works based on consistency between the approximate and exact discretization of the nonlinear plant. We combine an event-triggered module with a controller based on the state prediction, together with a packetized transmission at the controller-to-actuator channel to provide the desired stability properties under DoS attacks. We also provide proof of Zeno-free behavior for the event-based system. Our proposed method provides a maximum percentage of time that the system can tolerate attacks without performance degradation. Finally, a numerical example is used to illustrate the effectiveness of the proposed approach.

Model-based event-triggered control of switched discrete-time systems

In this paper, we aim to study model-based event-triggered control for a class of uncertain switched discrete-time systems composed of stabilizable and unstabilizable subsystems. A nominal model is introduced at the controller side to form a dynamic controller so that it can provide a kind of approximate estimate of the system state for system input even the overall switched discrete-time system is running in open-loop during any two consecutive event-triggered instants. By using multi-Lyapunov function method and the average dwell time switching strategy, stability conditions given in linear matrix inequality form for the closed-loop switched discrete-time system are derived. The design of control gains is also given. Finally, a numerical example and a physical example are provided to verify the effectiveness and usefulness of the proposed method.

Model-Based Adaptive Event-Triggered Tracking Control of Discrete-Time Nonlinear Systems Subject to Strict-Feedback Form

The consumption of communication resources is an essential issue when control tasks are implemented in a wireless network environment. In order to lessen the network resources, a novel model-based (MB) adaptive event-triggered (ET) tracking control scheme is put forward in this article for strict-feedback discrete-time nonlinear systems. In this article, an event-based adaptive model is constructed by the combination of an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -step-ahead predictor and event-sampled neural networks. Then, the adaptive neural model is used for designing the MB ET controller. Besides, a modified ET condition is constructed without any delay. By combining a decoupled backstepping framework, the reverse Lyapunov stability technology is developed to verify the ultimate boundedness of all closed-loop signals and the convergence of the tracking error. Compared to the zero-order hold method, which keeps transmitted state signals unchanged in the interevent period, the proposed MB ET control scheme can keep the real-time update of state signals transmitted to the controller. It means that the triggering error will be smaller by the MB trigger mechanism, thereby improving the event-based tracking performance and further saving communication resources. Comparisons of simulation results are given to verify the effectiveness of the proposed control scheme.

Model-based event-triggered adaptive formation control for underactuated surface vehicles via the minimal learning parameter technique

This article presents an adaptive neural formation control algorithm for underactuated surface vehicles by the model-based event-triggered method. In the algorithm, the leader–follower structure is employed to construct the formation model. Meanwhile, two new coordinate variables are introduced to avoid the possible singularity problem that exists in the polar coordinate system. Furthermore, the event-triggered mechanism is developed by constructing the adaptive model in a concise form. Related state variables and control parameters are required to be updated only at the event-triggered instants. Thus, the communication load between the controller and the actuator could be effectively reduced. Besides, for merits of the radial basis function neural network and the minimal learning parameter techniques, only two adaptive parameters are employed to compensate for the effects of the model uncertainties and the external disturbances. With the Lyapunov theory, all signals in the closed-loop system are proved to be semi-global uniformly ultimately bounded. Finally, numerical simulations are conducted to illustrate the effectiveness and feasibility of the proposed algorithm.

Event-triggered compound learning tracking control of autonomous surface vessels in the measurement network

This paper investigates the tracking control of the autonomous surface vessels (ASVs) with the time-varying disturbances. While the echo state network (ESN) accounts for the unknown dynamics in the model, a model-based event-triggered control (MBETC) scheme is presented by using the compound learning technique, which combines the learning of the ESN and the estimation of the compound disturbance. Different from the existing compound learning, the proposed scheme updates the estimates of the ESN weights and the compound disturbances in an event-triggered manner, in which two novel prediction errors are involved in their update laws. The values of prediction errors are obtained by using the online-recorded data during the inter-event time. To solve the recently proposed problem of “jumps of virtual control laws” arising in the backstepping-based event-triggered control (ETC), an event-triggered adaptive model is established to generate the continuous estimates of the states and direct the control laws. By the merit of ETC, the proposed scheme can importantly reduce the communication traffic in the measurement network compared with its continuous substitutes. By the merit of online-recorded-data-based compound learning, the proposed scheme can achieve the good understanding of synthetic uncertainties. All the errors in the closed-loop system are proved to be semi-globally uniformly ultimately bounded (SGUUB). Finally, a numerical example corroborates the proposed scheme.

Model-Based Event-Triggered Sliding-Mode Control for Multi-Input Systems: Performance Analysis and Optimization

This article is concerned with the model-based event-triggered sliding-mode control (SMC) issue for multi-input systems, which is motivated by some existing results in a single-input case. A model-based event-triggered SMC scheme is first designed. In particular, a triggered condition is co-designed with SMC to achieve the reachability condition of a specified sliding surface. Thus, it can effectively mitigate the burden of data communication, and also eliminate the effect of the matched external disturbance and the model uncertainties in both system and input. For ensuring the stability of the model dynamics and the resulting sliding-mode dynamics simultaneously, an auxiliary disturbance input is introduced to the nominal model by compensating the switching term of the designed SMC law. Furthermore, the positive lower bound for the minimum interevent time is analyzed to ensure the feasibility of the proposed approach. To illustrate the proposed model-based event-triggered SMC approach from a practical viewpoint, two design problems to maximize the system robustness and performance are proposed, respectively. The nontrivial optimization problems are then solved by a genetic algorithm (GA). Finally, jet transport aircraft is utilized to demonstrate the effectiveness of the proposed results and algorithm.

Adaptive Neural Network Model-based Event-triggered Attitude Tracking Control for Spacecraft

This article investigates the problem of attitude tracking control for spacecraft with limited communication, unknown system parameters, and external disturbances. An adaptive control scheme with an event-triggered mechanism (ETM) is proposed to alleviate the communication burden. Radial Basis Function Neural Network (RBFNN) estimation model is developed to provide the input signals for the control module in this control scheme. Estimated attitude information of the spacecraft generated from the estimation model will only be transmitted to the control module at the instants when the ETM is violated. The neural network (NN) and the estimation model will be updated complying with an adaptive algorithm at the discrete triggering instants. It’s substantiated that all the errors of attitude tracking converge towards corresponding residuals and there are no accumulated triggering instants. Numerical simulation also demonstrates the effectiveness of the proposed control method.

Event-triggered predictive control for networked control systems with DoS attacks

This paper presents a new model-based event-triggered predictive control (MB-ETPC) protocol to stabilize networked control systems (NCSs) subject to denial-of-service (DoS) attacks. Firstly, we introduce two kinds of DoS attack models, which are applied to sensor-to-controller and controller-to-actuator communication channels, then we give the corresponding schemes according to the different DoS attacks. Secondly, on the premise of ensuring the stability of NCSs, the mechanism can not only reduce the pressure of network bandwidth, but also can effectively compensate for the negative impact of DoS attacks on system performance. Next, considering the existence of DoS attacks and communication delays, we construct a closed-loop system model, which combines the model-based network control systems, the predictive control scheme and the event-triggered control (ETC) scheme all together. Based on the model, the sufficient condition of NCSs stability is given in the form of linear matrix inequalities (LMIs) by using Lyapunov theory method. Finally, two illustrative simulation examples are presented to demonstrate the effectiveness of the proposed method.

Sufficient stabilizing bit rate conditions for an [formula omitted]-dimensional nonlinear system based on event triggering

This paper investigates the problem of stabilizing an n-dimensional nonlinear system whose feedback packets are transmitted via the digital network. In the presence of the bounded processing delay and network delay, the sampling time instant of a feedback packet is not the same as its receiving time instant. The larger difference between these time instants, the higher bit rate is required to stabilize the concerned system. Moreover, not only these delays, but also feedback dropouts and process noise will deteriorate the system performance. Thus we propose a model-based event-triggered control method to stabilize the system and save the occupied network bandwidth. Besides the transmission bits inside the feedback packets, their sampling time instants also convey the state information. The proposed event-triggered method can make full use of the feedback packets, especially their receiving time instant information. Compared with the periodic sampling method, our method performs better and requires lower stabilizing bit rates while the desired input-to-state stability can still be ensured. The obtained bit rate conditions only depend on the Lipschitz constant, the upper bounds of the delays and the dropout rate, and are independent of the bounded process noise.

Model-based dual-stage event-triggered control of linear system with two time scales

ABSTRACTIn this paper, the model-based event-triggered control is addressed for a class of linear systems with two time scales. The singular perturbation technique is used to analyse the stability of the event-triggered control closed-loop system with a dual-stage event-triggering condition. For the differences between the matrices of the plant and the model, the relations between the parameters in the event-triggering conditions are revealed for the stability and the sampling performance of the closed-loop systems. Specifically, the parameters in the event-triggering conditions can be selected in a range independent of the matrices in the model. Finally, two numerical simulations are provided to illustrate the efficiency and feasibility of the proposed results.

Model-based event-triggered dynamic output predictive control of networked uncertain systems with random delay

ABSTRACTThis paper explores a dynamic output predictive control issue for networked systems with random delays and uncertainties. Based on the networked predictive control systems, an adaptive event-triggered dynamic output predictive control strategy is presented. And a networked delay compensator method is employed to compensate the communication delay actively. Furthermore, the problem we addressed can be transformed into the delay-dependent stability and stabilisation problem of networked control systems. Finally, the Lyapunov–Krasovskii functional approach and linear matrix inequality method are introduced to solve the expected solution. Both the results of numerical simulations and experiment are used to verify the effectiveness of the proposed method.

Model-Based Event-Triggered Tracking Control of Underactuated Surface Vessels With Minimum Learning Parameters.

This article studies the model-based event-triggered control (ETC) for the tracking activity of the underactuated surface vessel (USV). Following this ideology, the continuous acquisition of states is no longer needed, and the communication traffic is reduced in the channel of sensor to controller. The control laws are fabricated in the frame of an adaptive model, which is renewed with the states of the original system whenever the triggering condition is violated. In the scheme, both internal and external uncertainties are approximated by the neural networks (NNs). To decrease the computing complexity, the minimum learning parameters (MLPs) are involved both in the adaptive model and the derived controller. The adaptive laws of only two MLPs are devised, and their updating only happens at triggering instants. Using the MLPs, an adaptive triggering condition is further derived. To avoid the "Zeno" phenomenon in small tracking errors, a dead-zone operator is designed for the triggering condition. Furthermore, we incorporate the dynamic surface control (DSC) into the controller design, such that the jumping of virtual control laws at triggering instants is smoothed and the problem of "complexity explosion" is circumvented. Through the techniques of the impulsive dynamic system and the direct Lyapunov function, the parameter setting for the DSC is derived to guarantee the semiglobal uniformly ultimate boundedness (SGUUB) of all the error signals in the closed-loop system. Finally, the effectiveness of the proposed scheme is validated through the simulation.

Multiple model-based event-triggered adaptive control of a class of discrete-time nonlinear systems

ABSTRACTIn this study, the problem of event-triggered-based adaptive control (ETAC) for a class of discrete-time nonlinear systems with unknown parameters and nonlinear uncertainties is considered. Both neural network (NN) based and linear identifiers are used to approximate the unknown system dynamics. The feedback output signals are transmitted, and the parameters and the NN weights of the identifiers are tuned in an aperiodic manner at the event sample instants. A switching mechanism is provided to evaluate the approximate performance of each identifier and decide which estimated output is utilised for the event-triggered controller design, during any two events. The linear identifier with an auxiliary output and an improved adaptive law is introduced so that the nonlinear uncertainties are no longer assumed to be Lipschitz. The number of transmission times are significantly reduced by incorporating multiple model schemes into ETAC. The boundedness of both the parameters of identifiers and the system outputs is demonstrated though the Lyapunov approach. Simulation results demonstrate the effectiveness of the proposed method.

Asynchronous Networked MPC With ISM for Uncertain Nonlinear Systems

A model-based event-triggered control scheme for nonlinear constrained continuous-time uncertain systems in networked configuration is presented in this paper. It is based on the combined use of model-predictive control (MPC) and integral sliding-mode (ISM) control, and it is oriented to reduce the packet transmission over the network both in the direct path and in the feedback path, in order to avoid network congestion. The key elements of the proposed control scheme are the ISM local control law, the MPC remote controller, a smart sensor, and a smart actuator, both containing a copy of the nominal model of the plant. The role of the ISM control law is to compensate matched uncertainties, without amplifying the unmatched ones. The MPC controller with tightened constraints generates the control component oriented to comply with state and control requirements and is asynchronous since the underlying constrained optimization problem is solved only when a triggering event occurs. In the paper, the robustness properties of the controlled system are theoretically analyzed, proving the regional input-to-state practical stability of the overall control scheme.

Adaptive model‐based event‐triggered control of networked control system with external disturbance

This study is concerned with adaptive model-based event-triggered control of an uncertain continuous system with external disturbance. The proposed framework incorporates two important control techniques for reducing communication burden and regulating the states of the system online in control network, that is, adaptive model-based networked control system and event-triggered control (ETC). An adaptive model of the plant is capable of generalising the zero-order hold implementation in traditional ETC schemes, while also providing stability thresholds that are robust to model uncertainties. In the adaptive model-based controller, the authors present an update law to estimate the parameters of the adaptive model at triggered instant. After revisiting the adaptive model property in the context of event-triggered communication, an event-triggered condition is proposed using the Lyapunov technique. The stability condition of the proposed approach does not need explicit knowledge of the plant parameters, but are given only in terms of the parameters of the adaptive model and some bounds in the model uncertainties. In addition, lower bound on transmission periods are provided. Meanwhile, L 2 stability with respect to external disturbance is examined. A real-time simulation example is presented to demonstrate the effectiveness of the theoretical results.

Stability of model-based event-triggered control systems: a separation property

ABSTRACTTo save resource of communication, this paper investigates the model-based event-triggered control systems. Two main problems are considered in this paper. One is, for given plant and model, to design event conditions to guarantee the stability of the systems. The other is to consider the effect of the model matrices on the stability. The results show that the closed-loop systems can be asymptotically stabilised with any model matrices in compact sets if the parameters in the event conditions are within the designed ranges. Then, a separation property of model-based event-triggered control is proposed. Namely, the design of the controller gain and the event condition can be separated from the selection of the model matrices. Based on this property, an adaption mechanism is introduced to the model-based event-triggered control systems, which can further improve the sampling performance. Finally, a numerical example is given to show the efficiency and feasibility of the developed results.

Model-based event-triggered control for linear plant with threshold variable and model states

Summary This paper studies the robustness of model-based event-triggered control systems with respect to the differences between the plant and model matrices. Two types of event conditions, which involve an additional threshold variable and the norm of model states, are investigated, respectively. The tunable parameters in both the event conditions are designed according to the differences between the plant and model matrices. Also, the uncertainties in the plant matrices are considered, and the asymptotic stability can be guaranteed robustly. Moreover, the relationship between the tunable parameters and the model matrices is revealed. Namely, on the one hand, there exists a range of the tunable parameters such that the closed-loop system is asymptotically stable with model matrices in any compact set. On the other hand, if the differences between the plant and model matrices are small enough, the tunable parameters can be set arbitrarily large. Finally, a numerical example is provided to illustrate the efficiency and feasibility of the obtained results. Copyright © 2016 John Wiley & Sons, Ltd.