Event-triggered Robust Control Research Articles

A new approach to event-triggered robust control of T–S fuzzy systems

This paper is devoted to the problem of robust control for continuous-time T–S fuzzy systems with outliers via new piecewise event-triggered (ET) mechanisms. First, in order to address the deterioration of system performance caused by outliers, the design of fuzzy controllers with adaptive saturation of states is presented. Then, new piecewise ET mechanisms are constructed, in which the ET rules follow the different regions where the premise variables (PVs) of the system model are located, and an ET robust control scheme is further studied through the ET mechanisms. The controller designed in the absence of ET mechanisms can still be used in a situation where ET mechanisms are introduced, while a co-design of the controller and ET mechanisms is required in the existing results. Finally, the effectiveness of the designed strategy is verified by a simulation example.

Event-Triggered Robust Control Design to Improve the Dynamic Stability of WECS-Based Microgrids

Event-Triggered Robust Control Design to Improve the Dynamic Stability of WECS-Based Microgrids

Event-Triggered Path Following Robust Control of Underactuated Unmanned Surface Vehicles with Unknown Model Nonlinearity and Disturbances

An effective path-following controller is a guarantee for stable sailing of underactuated unmanned surface vehicles (USVs). This paper proposes an event-triggered robust control approach considering an unknown model nonlinearity, external disturbance, and event-triggered mechanism. The proposed method consists of guidance and dynamic control subsystems. Based on the tracking error dynamics equations, the guidance subsystem is designed to achieve the guidance law. For the dynamic control subsystem, the radial basis function neural networks (RBFNNs) are designed to approximate the unknown model nonlinearity and external disturbances to improve the robustness of the proposed method. In addition, an event-triggered mechanism is constructed to reduce the triggering times. The closed-loop system is proven to be stable, and the effectiveness of the proposed method is illustrated through simulation results.

Model-free adaptive dynamic event-triggered robust control for unknown nonlinear systems using iterative neural dynamic programming

This paper proposes an adaptive dynamic event-triggered (ADET) robust control method for unknown nonlinear systems using iterative neural dynamic programming (INDP). Firstly, the ADET robust control problem is transformed into an ADET optimal control problem by introducing an infinite domain integral function. Then, dynamic variables and adaptive thresholds are designed for discrete-time systems with auxiliary variables. The proposed ADET method reduces computation and transmission costs compared to existing static and dynamic event triggering methods. INDP is utilized to learn the optimal solution of the ADET Hamilton-Jacobi-Bellman equation within the heuristic dynamic programming (HDP) framework, providing system stability and algorithm convergence. The INDP algorithm employs model, action, and critic neural networks and includes a method to directly minimize the iterative cost function in the back-propagation process. The neural network implementation of the INDP algorithm is detailed. Simulation results demonstrate that the proposed ADET method based on INDP achieves faster convergence with fewer transmitted data and control updates.

Adaptive Dynamic Programming-Based Event-Triggered Robust Control for Multiplayer Nonzero-Sum Games With Unknown Dynamics1-4mmPlease verify and confirm the term "Multi-Player" has been changed to "Multiplayer" in the title of this article.

In this article, the event-triggered robust control of unknown multiplayer nonlinear systems with constrained inputs and uncertainties is investigated by using adaptive dynamic programming. To relax the requirement of system dynamics, a neural network-based identifier is constructed by using the system input-output data. Subsequently, by designing a nonquadratic value function, which contains the bounded functions, the system states, and the control inputs of all players, the event-triggered robust stabilization problem is converted into an event-triggered constrained optimal control problem. To obtain the approximate solution of the event-triggered Hamilton-Jacobi (HJ) equation, a critic network for each player is established with a novel weight updating law to relax the persistence of excitation condition based on the experience replay technique. Furthermore, according to the Lyapunov stability theorem, the present event-triggered robust optimal control ensures the multiplayer system to be uniformly ultimately bounded. Finally, two simulation examples are employed to show the effectiveness of the present method.

Improved Dynamic Event-Triggered Robust Control for Flexible Robotic Arm Systems with Semi-Markov Jump Process

In this paper, we investigate the problem of a dynamic event-triggered robust controller design for flexible robotic arm systems with continuous-time phase-type semi-Markov jump process. In particular, the change in moment of inertia is first considered in the flexible robotic arm system, which is necessary for ensuring the security and stability control of special robots employed under special circumstances, such as surgical robots and assisted-living robots which have strict lightweight requirements. To handle this problem, a semi-Markov chain is conducted to model this process. Furthermore, the dynamic event-triggered scheme is used to solve the problem of limited bandwidth in the network transmission environment, while considering the impact of DoS attacks. With regard to the challenging circumstances and negative elements previously mentioned, the adequate criteria for the existence of the resilient H∞ controller are obtained using the Lyapunov function approach, and the controller gains, Lyapunov parameters and event-triggered parameters are co-designed. Finally, the effectiveness of the designed controller is demonstrated via numerical simulation using the LMI toolbox in MATLAB.

Event-Triggered robust control scheme for sub-synchronous resonance mitigation in DFIG-based WECS

Series compensation is an economically feasible solution for facilitating the integration of high levels of wind energy sources into the grid. However, high levels of series compensation in transmission lines typically results in sub-synchronous resonance (SSR) in doubly-fed-induction-generator (DFIG)-based wind energy conversion systems (WECS). The introduction of negative equivalent resistance due to negative SSR slip results in self-sustaining oscillating instability and potentially loss of stability of the whole system. This paper proposes an event-triggered sliding mode control (ETSMC)-based control approach for the effective damping of sub-synchronous resonance. The ETSMC control is designed for the rotor side converter of the DFIG, and a phase compensator unit is augmented with the ETSMC control to enhance its efficacy in mitigating specific unstable mode. The proposed control approach is validated using a DFIG-based WECS in IEEE first benchmark model. The results prove the high efficacy of the control approach in mitigating SSR and ensuring system’s fast recovery. In addition to the attributes of the SMC approach, reliance on an event triggering mechanism leads to only seldom control updates, thereby solving the problem of requirement of high communication bandwidth and computational power.

Event‐triggered robust control for multi‐player nonzero‐sum games with input constraints and mismatched uncertainties

AbstractIn this article, an event‐triggered robust control (ETRC) method is investigated for multi‐player nonzero‐sum games of continuous‐time input constrained nonlinear systems with mismatched uncertainties. By constructing an auxiliary system and designing an appropriate value function, the robust control problem of input constrained nonlinear systems is transformed into an optimal regulation problem. Then, a critic neural network (NN) is adopted to approximate the value function of each player for solving the event‐triggered coupled Hamilton–Jacobi equation and obtaining control laws. Based on a designed event‐triggering condition, control laws are updated when events occur only. Thus, both computational burden and communication bandwidth are reduced. We prove that the weight approximation errors of critic NNs and the closed‐loop uncertain multi‐player system states are all uniformly ultimately bounded thanks to the Lyapunov's direct method. Finally, two examples are provided to demonstrate the effectiveness of the developed ETRC method.

Prescribed chattering reduction control for quadrotors using aperiodic signal updating

In the paper, a prescribed chattering reduction control using aperiodic signal updating is presented for quadrotors subject to parameter uncertainties and external disturbances. Using estimation errors instead of tracking errors to update adaptive laws, estimator-based minimum learning parameter (EMLP) observers capable of relaxing computational complexity are respectively explored in translational and rotational loops to reject fast time-varying disturbances, such that transient oscillations can be efficiently mitigated even with a large adaptive gain. Meanwhile, quantitative analysis for transient learning performance is characterized by means of L2 norms of time differential of neural network weights. With the aid of disturbance estimates, a relative event-triggered robust control law is derived by inserting a compensation term to guarantee a favorable trajectory tracking with Zeno free behaviors and decreased sampling cost. Besides, an appointed-time prescribed performance control (APPC) is established, enforcing trajectory tracking errors to evolve within pre-given regions even in face of triggering errors, where a piecewise and continuous finite-time behavior function, rather than an exponential decaying function, is applied to enable a preassigned fast convergence time without retuning controller parameters. Finally, the stability of closed-loop system is proved via Lyapunov synthesis, while comparative studies are provided to validate the effectiveness of presented control method.

Polytopic Event-Triggered Robust Model Predictive Control for Constrained Linear Systems

This paper studies the event-triggered robust model predictive control (MPC) problem for constrained linear systems subject to bounded disturbances. For control computations, explicit MPC reduces online optimizations to simple function evaluations, thus is suitable for resource constrained systems. We propose an event-triggering mechanism (ETM) for an explicit robust model predictive controller such that function evaluations are not needed at every time instant and the ETM itself requires comparably less online computations. In the proposed ETM, we design a contracting polytopic trigger set with respect to system constraints and the explicit robust control law. The resulting event-triggered control scheme ensures robust constraint satisfaction and robust stability for the closed-loop system while avoiding the Zeno-like behavior. Simulation results illustrate the validity of the event-triggered robust control scheme.

Event-Triggered Robust Control for Output Consensus of Unknown Discrete-Time Multiagent Systems With Unmodeled Dynamics.

This article investigates the event-triggered output consensus problem for a class of unknown heterogeneous discrete-time linear multiagent systems in the presence of unmodeled dynamics. The agents have individual nominal dynamics with unknown parameters, and the unmodeled dynamics are in the form of multiplicative perturbations. A novel design framework is developed based on an event-triggered internal reference model and a distributed model reference adaptive controller. To deal with the heterogeneity of the multiagent system, the event-triggered internal reference model is designed to generate a virtual reference signal for each agent with a dynamic event-triggering mechanism being adopted to reduce the communication burden between neighboring agents. To handle the unknown parameters and unmodeled dynamics, the robust model reference adaptive controller is then designed to follow the generated virtual reference signal. It is shown that if the unmodeled dynamics satisfy certain conditions, then the boundedness of all the signals and variables in the closed-loop system and convergence of consensus errors to a residual set are guaranteed. Moreover, the consensus errors will converge to zero asymptotically in the absence of unmodeled dynamics. Compared with existing related works, the proposed framework is able to deal with the agents with individual unknown nominal dynamics and unmodeled dynamics. Moreover, the proposed framework is fully distributed in the sense that no knowledge of any global information is needed. Finally, the performance of the proposed method is validated by examples.

Event-triggered robust control for quadrotors with preassigned time performance constraints

In this paper, an event-triggered robust control for quadrotors with preassigned time performance constraints is presented. In order to prolong operational time and enhance mission completion efficiency subject to limited onboard power, a switching threshold event-triggered strategy is embedded in controller-to-actuator and sensor-to-controller channels, where switching threshold event-triggered based extended state observers (SET-ESO) are respectively established in the trajectory and attitude subsystems to accomplish the real-time estimation of linear velocity, angular velocity along with lumped disturbances using intermittent measurements, and the quantitative analysis between the ultimate observation error and parameters of SET-ESO is revealed. Based on the estimation results, an event-triggered output feedback control law including a robust compensation term is established to realize an anti-disturbance time-varying trajectory tracking without involving Zeno phenomena. Moreover, to eliminate severe fluctuations at the transient inherent in traditional prescribed performance control (TPPC) with a fast convergent rate, a tracking differentiator-based prescribed performance control (TDPPC) mechanism is exploited to ensure the trajectory state converge to a pre-given region with a preassigned prescribed time, such that a smooth and pre-specified finite-time tracking capability independent of control parameters is preserved. Next, with the aid of Lyapunov synthesis, all the signals involved in the closed-loop quadrotor system are proved to be bounded, and the preassigned time performance constraints are not violated. The simulation and comparison results are shown to validate the effectiveness of the proposed control scheme.

Robust Stabilization of a Class of Nonlinear Systems via Aperiodic Sensing and Actuation

This article proposes a framework to design a robust controller for a class of nonlinear networked control systems using aperiodic feedback information. Here, the nonlinearity and parameter variations of system model are considered as sources of uncertainty. To tackle the uncertainty in system dynamics, a linear robust control law is derived by applying the optimal control theory. Two different architectures of closed-loop systems are considered. In the first one, system and controller are not collocated; instead they are interconnected by means of a shared communication network. In the second architecture, system, controller and actuator are all collocated with their respective outputs available at all time-instead, sensors and controller are connected through a shared communication channel. In both architectures, the feedback loop is closed through the network. Owing to its shared nature, the network may suffer from bandwidth limitations. To save the network bandwidth, state and input information are transmitted aperiodically within the feedback loop. With this aim, the paper adopts an event-triggered control technique so as to reduce the transmission overhead. Applying Input-to-State Stability theory, we derive two different event-triggered robust control laws that stabilize the uncertain nonlinear system. Finally, we show that the designed event-triggered controllers satisfy the trade-off between control performance and saving in network bandwidth in the presence of uncertainty. The developed control algorithm is implemented and validated through numerical simulations.

Periodic event-triggered robust output feedback control for nonlinear uncertain systems with time-varying disturbance

This paper investigates the periodic event-triggered robust output feedback control problem for a class of nonlinear uncertain systems subject to time-varying disturbance. By means of the feedback domination approach and disturbance compensation technique, a new framework of periodic event-triggered robust control strategy is developed in the form of output feedback, which encompasses a discrete-time event-triggering transmission scheme that is only intermittently monitored at sampling instants and a discrete-time output feedback controller consisting of a set of linear difference equations. The proposed robust method may reduce the communication resource utilization as compared to the non-event triggering schemes while maintaining a desirable closed-loop system performance even in the presence of a general class of time-varying disturbance and nonlinear uncertainties. The closed-loop system under the proposed control scheme is actually modeled as a hybrid system, and it is shown that the global practical stability of the closed-loop hybrid system is guaranteed by selecting a sufficiently large scaling gain and a sufficiently small sampling period. Finally, the experimental results on a DC–DC buck power converter are presented to illustrate the effectiveness of the proposed control approaches.

Adaptive Critic Designs for Event-Triggered Robust Control of Nonlinear Systems With Unknown Dynamics.

This paper develops a novel event-triggered robust control strategy for continuous-time nonlinear systems with unknown dynamics. To begin with, the event-triggered robust nonlinear control problem is transformed into an event-triggered nonlinear optimal control problem by introducing an infinite-horizon integral cost for the nominal system. Then, a recurrent neural network (RNN) and adaptive critic designs (ACDs) are employed to solve the derived event-triggered nonlinear optimal control problem. The RNN is applied to reconstruct the system dynamics based on collected system data. After acquiring the knowledge of system dynamics, a unique critic network is proposed to obtain the approximate solution of the event-triggered Hamilton-Jacobi-Bellman equation within the framework of ACDs. The critic network is updated by using simultaneously historical and instantaneous state data. An advantage of the present critic network update law is that it can relax the persistence of excitation condition. Meanwhile, under a newly developed event-triggering condition, the proposed critic network tuning rule not only guarantees the critic network weights to converge to optimums but also ensures nominal system states to be uniformly ultimately bounded. Moreover, by using Lyapunov method, it is proved that the derived optimal event-triggered control (ETC) guarantees uniform ultimate boundedness of all the signals in the original system. Finally, a nonlinear oscillator and an unstable power system are provided to validate the developed robust ETC scheme.

Neural robust stabilization via event-triggering mechanism and adaptive learning technique

The robust control synthesis of continuous-time nonlinear systems with uncertain term is investigated via event-triggering mechanism and adaptive critic learning technique. We mainly focus on combining the event-triggering mechanism with adaptive critic designs, so as to solve the nonlinear robust control problem. This can not only make better use of computation and communication resources, but also conduct controller design from the view of intelligent optimization. Through theoretical analysis, the nonlinear robust stabilization can be achieved by obtaining an event-triggered optimal control law of the nominal system with a newly defined cost function and a certain triggering condition. The adaptive critic technique is employed to facilitate the event-triggered control design, where a neural network is introduced as an approximator of the learning phase. The performance of the event-triggered robust control scheme is validated via simulation studies and comparisons. The present method extends the application domain of both event-triggered control and adaptive critic control to nonlinear systems possessing dynamical uncertainties.

Event-triggered robust control for networked control systems with actuator failures and time-varying transmission delays

Purpose – The purpose of this paper is with robust control problem for event-triggered networked control systems (NCSs) with actuator failures and time-varying transmission delays. Design/methodology/approach – A random sequence is introduced to describe the actuator faults, and a novel event-triggering communication scheme is adopted in the sensor-to-controller channel. By taking the event-triggered mechanism and network transmission delay into consideration, a delay system model is constructed. Findings – Based on Lyapunov stability theory and free weighting matrix method, the feasibility criteria for co-designing both the controller gain and the trigger parameters are derived. Finally, a simulation example is exploited to demonstrate the effectiveness of the proposed linear matrix inequalities (LMIs) approach. Originality/value – The introduced approach is interesting for NCSs with actuator failures and time-varying transmission delays.