Adaptive Event-triggered Control Research Articles

Event-Triggered Adaptive Control of Coupled Hyperbolic PDEs With Piecewise-Constant Inputs and Identification

In this article, we present an event-triggered boundary control scheme with a, likewise, event-triggered batch least-squares parameter identification for a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2\times 2$</tex-math></inline-formula> hyperbolic PDE-ODE system, where two coefficients of the in-domain couplings between two transport PDEs, and the system parameter of a scalar ODE, are unknown. The triggering condition is designed based on evaluating both the actuation deviation caused by the difference between the plant states and their sampled values, and the growth of the plant norm. When either condition is met, the piecewise-constant control input and parameter estimates are updated simultaneously. For the closed-loop system, the following results are proved: first, the absence of a Zeno phenomenon; second, finite-time exact identification of the unknown parameters in most situations; third, exponential regulation of the plant states to zero. In the numerical simulation, the design is verified in an application of axial vibration control of a mining cable elevator, where the damping coefficients of the cable and the cage are unknown.

Event-Triggered Model Reference Adaptive Control for Linear Partially Time-Variant Continuous-Time Systems With Nonlinear Parametric Uncertainty

In this work, we develop an event-triggered adaptive control approach for solving the state tracking problem of linear partially time-variant continuous-time systems with the nonlinear state-dependent matched parametric uncertainty under unknown system dynamics. First, an event-triggered model reference adaptive controller is designed, which is composed of event-triggered adaptive laws based on the event-updated information and an event-triggering condition depending on the state tracking error of the controlled plant and reference model. Then, the state-tracking error and the error between control parameters and ideal ones of the resulting closed-loop system are proven to be uniformly ultimately bounded. Moreover, based on the designed event-triggering condition, the interevent time between two consecutive triggering points is proven to have a positive lower bound. Finally, a simulation example is provided to show the effectiveness of the proposed approach.

ESO-based adaptive event-triggered load following control design for a pressurized water reactor with samarium–promethium dynamics

Conventional control systems used in nuclear reactors rely on real-time information transmission between the controllers and the actuators, which is unnecessary and imposes a heavy communication burden. To this end, this paper proposes an extended state observer-based adaptive event-triggered control (ESO-AETC) scheme for the load following problem of a pressurized water reactor (PWR) in the presence of uncertainties and limited communication resources. Different from most of the previous controllers presented in the literature, the samarium–promethium dynamics are considered in the constructed PWR model in this paper. Based on the PWR model with the load-dependent parameters and the samarium–promethium dynamics, an ESO, which can estimate lumped uncertainties and unmeasured system states including the relative density of the delayed neutron precursor, the average fuel temperature, the xenon concentration, the iodine concentration, the samarium concentration, the promethium concentration, and the total reactivity, is designed. With the help of the ESO and the event-triggered techniques, an AETC scheme is developed to avoid the real-time communication in the controller-to-actuator channel of the PWR system. It is rigorously proved that the control error can converge to any prescribed residual set in a finite time according to Lyapunov stability theory, excluding the Zeno behavior. Finally, simulation studies involving comparisons with existing works, estimation performance verification, and sensitivity analysis are conducted to assess the proposed ESO-AETC scheme. Simulation results reveal that (1) relative to the pre-existing model-free controller (MFC) and the disturbance observer-based sliding mode controller (DO-SMC), the proposed ESO-AETC scheme is found to provide better load following performance with at least 56.12% lower maximum absolute error (MAE), at least 15.05% lower integral absolute error (IAE), and 87.58% reduced communication resources in the controller-to-actuator channel, (2) the ESO possesses strong estimation capability for system states and lumped uncertainties, and (3) the proposed ESO-AETC scheme yields a low sensitivity with respect to the change of the controller parameters.

Adaptive event-triggered control for almost sure stability for vehicle platooning under interference and stochastic attacks

This paper considers the adaptive event-triggered strategy and controller design of vehicle platooning for stochastic attacks and interferences in communication channels. Bernoulli distribution and Markovian distribution Denial of Service models are introduced in this paper. In designing the controller, Aiming at the stochastic jamming attacks, the stability criterion is presented to guide the controller for almost sure string stability, and meanwhile aiming at the special safety requirements and the reduction of system interferences, the asymmetry event-triggered strategy framework is presented to adapt the transmission environment and the different safety requirements, which is designed to balance the principal concern in different situations. Finally, an example is introduced to demonstrate the controller performances of the vehicle platooning with the Bernoulli distribution and Markovian distribution DoS models, which implies that the presented methods are effective.

Dynamic event-triggered adaptive fuzzy control for stochastic nontriangular nonlinear systems with unknown backlash-like hysteresis

A finite-time adaptive control is discussed in this paper for stochastic nontriangular nonlinear systems with unknown backlash-like hysteresis. The performance function is introduced to ensure that the tracking error of the system converges to the specified region in a finite time. Secondly, a controller is proposed using the fuzzy logic systems to approximate the unknown functions and backstepping technique. In addition, adding the dynamic event-triggered mechanism is able to reduce the network resources more effectively. At last, a few simulation examples are presented to demonstrate effective performance.

Dynamic event-triggered adaptive control for zero-sum games of nonlinear stochastic systems

This paper considers solving the zero-sum games (ZSGs) of nonlinear stochastic systems under the architecture of dynamic event-triggered mechanism (DETM). The adaptive dynamic programming (ADP) approach with great approximate computing power is proposed to seek the saddle-point equilibrium solution of ZSGs. First, the Hamilton–Jacobi–Isaacs equation of stochastic version is constructed which is the key to solving the two-player ZSGs. Then, the ADP method is employed to search the approximated solution under the dynamic triggering mechanism. On the foundation of static event-triggered mechanism (SETM), the DETM is derived through introducing an internal dynamic invariable to further lower the update frequency for the strategies. Additionally, the stability of the closed system is demonstrated in detail via the Lyapunov theory. Finally, two representative examples are provided to present the availability and reliability of this approach from different perspectives.

An event-triggered trajectory planning and tracking scheme for automatic berthing of unmanned surface vessel

This study proposes a trajectory planning and tracking scheme for the fully actuated USV's automatic berthing. First, a three-degree-of-freedom model (3-DOF) of USV is employed, considering model uncertainty, actuator saturation, and environmental disturbances. Moreover, an A* algorithm with the Bezier curve (AB) approach is proposed to plan the smooth berthing trajectory. To make the velocity of the USV converge to zero at the berth, an interpolation method is proposed to densify the waypoints at the end of the berthing trajectory. Most of the previous studies about USV's tracking control pay little attention to real-time performance, which may increase the computational cost of the control system and affect its stability. Therefore, to improve the computational performance during the USV's berthing, an event-triggered adaptive horizon model predictive control (ETAHMPC) method is proposed. Comparative simulation experiments are performed in two scenarios to verify the effectiveness and superiority of the proposed ETAHMPC scheme. The results show that the proposed scheme can effectively reduce computational costs while ensuring control performance.

Adaptive event-triggered fault-tolerant consensus of linear heterogeneous multiagent systems via hierarchical approach

This article investigates the event-triggered fault-tolerant consensus problem for heterogeneous linear multiagent systems (MASs) with actuator failures. By introducing the hierarchical control scheme, a distributed adaptive event-triggered fault-tolerant control method is proposed. The upper layer and the lower layer comprise the hierarchical control structure. The upper layer is made up of a virtual reference system and a distributed cooperative controller with event-triggered mechanism, while the lower layer is composed of an actual system and a fault-tolerant tracking controller. The main contribution of this work is that the phenomena of fault propagation could be prevented employing presented control method when compared to the most commonly used distributed fault-tolerant control methods. And continuous transmission between agents can be eliminated due to the event-triggered mechanism. It is shown that the consensus error converges to zero asymptotically. Finally, the effectiveness of the theoretical result is illustrated by a simulation example.

Prescribed Settling Time Adaptive Neural Network Consensus Control of Multiagent Systems with Unknown Time-Varying Input Dead-Zone

For a class of multiagent systems with an unknown time-varying input dead-zone, a prescribed settling time adaptive neural network consensus control method is developed. In practical applications, some control signals are difficult to use effectively due to the extensive existence of an input dead-zone. Moreover, the time-varying input gains further seriously degrade the performance of the systems and even cause system instability. In addition, multiagent systems need frequent communication to ensure a system’s consistency. This may lead to communication congestion. To solve this problem, an event-triggered adaptive neural network control method is proposed. Further, combined with the prescribed settling time transform function, the developed consensus method greatly increases the convergence rate. It is demonstrated that all followers of multiagent systems can track the virtual leader within a prescribed time and not exhibit Zeno behavior. Finally, the theoretical analysis and simulation verify the effectiveness of the designed control method.

Event-triggered adaptive decentralised control of interconnected nonlinear systems with Bouc-Wen hysteresis input

ABSTRACT This article presents an event-based adaptive decentralised output feedback control scheme for interconnected systems with Bouc-Wen hysteresis and unmeasured system states. To reduce some unnecessary data transmissions, a novel dynamic threshold adjustable event-triggering mechanism is proposed. In contrast with the traditional static threshold event-triggering mechanism, communication efficiency is greatly enhanced. Then, a neural networks-based observer is constructed to address the problem of unmeasured states, and the dynamic surface control method is used to address the ‘explosion of complexity’ that comes up when traditional backstepping design processes are used. Meanwhile, the Nussbaum function is introduced to eliminate the effect of unknown hysteresis. By resorting to the Lyapunov stability theory, it can be verified that all signals in the close-loop system are uniformly ultimately bounded. Finally, a simulation example is given to verify the effectiveness of the developed control scheme.

Dynamic Event-Triggered Adaptive NN Control for Switched Uncertain Nonlinear Systems.

This article is concerned with the problem of dynamic event-triggered adaptive neural network (NN) control for a class of switched strict-feedback uncertainty nonlinear systems. A novel switched command filter-based dynamic event-triggered adaptive NN control approach is set up by exploiting the backstepping and command filter and the common Lyapunov function method. Since adaptive controllers of subsystems are event triggered, then if the switching happens between any two consecutive triggering instants, asynchronous switching will arise between candidate controllers of subsystems and subsystems. Unlike the existing literature, where maximum asynchronous time is restricted, without any strict limitations on maximum asynchronous time being needed in this article, the asynchronous switching problem is directly handled by proposing a novel switching dynamic event-triggered mechanism (DETM) and event-triggered adaptive controllers of subsystems. Moreover, a piecewise constant variable is introduced into the switching DETM, which overcomes the difficulty of switched measurement error being discontinuous. Also, a strictly positive lower bound of interevent times is obtained. Finally, a continuous stirred tank reactor system and a numerical example are presented to demonstrate the effectiveness of the developed approach.

Event-Triggered Adaptive Output-Feedback Control of Switched Stochastic Nonlinear Systems With Actuator Failures: A Modified MDADT Method.

This article investigates the adaptive event-triggered output-feedback control problem for a class of switched stochastic nonlinear systems with actuator faults. In the existing works, the developed results on adaptive control for switched stochastic nonlinear systems are almost based on the average dwell-time method, and how to construct a desired adaptive controller in the frame of the mode-dependent average dwell time (MDADT) remains a control dilemma. By presenting a general adaptive control rule based on the MDADT, this article implements the adaptive output-feedback control for the switched stochastic system under interest. In the process of controller design, fuzzy-logic systems, a flexible approximator, are utilized to approximate the unknown nonlinear functions. The dynamic surface design approach is employed to avoid taking derivatives of the constructed virtual controls to decrease the difficulty of complex calculation greatly. Meanwhile, a switched observer is designed to estimate the unknown states. In the frame of backstepping design, an event-triggered-based adaptive output-feedback controller is constructed such that all signals existing in the closed-loop system are ultimately bounded under a class of switching signals with MDADT property. Finally, the simulation results show the validity of the proposed control strategy.

Adaptive Event-Triggered Neural Network Control for Switching Nonlinear Systems With Time Delays.

The adaptive event-triggered-based neural network control is explored for switching nonlinear systems with nonstrict-feedback structure and time-varying delays in this article. First, the switching observer is designed to estimate the unmeasurable states. Due to the existence of time-varying input delay, a compensation system is introduced. The average dwell-time (ADT) scheme and the event-triggered controller are established. Furthermore, the semiglobal uniform ultimate boundedness (SGUUB) of all the variables in the closed-loop system is achieved and the Zeno behavior is avoided. Finally, the numerical simulation shows that our proposed control approach is effective.

Memory-based adaptive event-triggered secure control of Markovian jumping neural networks suffering from deception attacks

Memory-based adaptive event-triggered secure control of Markovian jumping neural networks suffering from deception attacks

Finite-time H∞ control for switched fuzzy systems: A dynamic adaptive event-triggered control approach

This paper investigates the finite-time H∞ control problem for switched fuzzy systems using the edge-dependent average dwell time switching. A dynamic event-triggered mechanism is adopted to alleviate communication pressure. An adaptive law is set up to adjust the threshold on-line, which deeply affects the triggering times. It is rigorously proved that the Zeno behavior is excluded. A set of dynamic event-triggered controllers and edge-dependent average dwell time switching signals are co-designed to achieve desired performance. Finally, a chemical reaction example is given to validate the effectiveness of the proposed method.

Event-triggered adaptive fuzzy asymptotic control design for uncertain stochastic nonlinear systems

The adaptive fuzzy asymptotic tracking control (AFATC) method is presented for uncertain stochastic nonlinear systems by virtue of event-triggered mechanism. The fuzzy logic systems are borrowed to address system uncertainties. By applying the backstepping technique, the AFATC strategy is recursively constructed. Different from the existing approximation-based methods, the developed controller can achieve asymptotic convergence feature. Moreover, the event-triggered mechanism is integrated into the controller design process to reduce data transmission and unnecessary resource waste. By applying the Lyapunov analysis and several helpful lemmas, the stability and convergence of the considered systems are ensured. Finally, the availability of the theory analysis is verified by a chemical reactor system.

Event-Triggered output feedback adaptive control for nonlinear switched interconnected systems with unknown control coefficients

This paper proposes an event-triggered adaptive control scheme for switched nonlinear interconnected systems with unknown control coefficients and unknown backlash hysteresis. Firstly, unknown interconnected terms and unknown nonlinearities of the system are approximated through fuzzy logic systems. By introducing the Nussbaum function and choosing an appropriate Lyapunov function, an event-triggered output feedback controller is developed. The proposed control scheme can guarantee that the whole states are semi-globally uniformly ultimately bounded, meanwhile, the elimination of Zeno behavior is ensured. Eventually, the effectiveness of the method is verified by simulation results.

Prescribed performance adaptive event-triggered consensus control for multiagent systems with input saturation.

In this paper, a prescribed performance adaptive event-triggered consensus control method is developed for a class of multiagent systems with the consideration of input dead zone and saturation. In practical engineering applications, systems are inevitably suffered from input saturation. In addition, input dead zone is widely existing. As the larger signal is limited and the smaller signal is difficult to effectively operate, system efficacious input encounters unknown magnitude limitations, which seriously impact system control performance and even lead to system instability. Furthermore, when constrained multiagent systems are required to converge quickly, the followers would achieve it with drastic and quick variation of states, which may violate the constraints and even cause security problems. To address those problems, an adaptive event-triggered consensus control is proposed. By constructing the transform function and the barrier Lyapunov function, while state constrained is guaranteed, multiagent systems quickly converge with prescribed performance. Finally, some examples are adopted to confirm the effectiveness of the proposed control method.

Event-triggered adaptive control of hypersonic vehicles subject to actuator nonlinearities

This paper investigates the event-triggered adaptive control problem for air-breathing hypersonic vehicles (AHVs) subject to physical actuator nonlinearities. In contrast with abundant AHV control strategies belonging to the traditional sample-data framework, the presented adaptive control works with an event-triggered mechanism, which naturally reduces the executing frequency of scramjet fuel-to-air equivalency ratio (FER) and aerodynamic control surface (ACS). During the event-triggered controller design, practical actuator characteristics including magnitude-rate composite constraints and servo transient dynamics are synthetically considered. In the velocity loop, the built-in FER saturation, which is a fusion of physical FER magnitude and rate constraints, is handled by reshaping the velocity reference with the help of an adaptive reference governor. Meanwhile in the altitude loop, a neural control allocator directly generates feasible ACS commands via a neural network that is well trained by means of the optimal control allocation. As the result, ACS magnitude-rate composite constraints and servo transient dynamics can be accommodated without implementing any time-consuming optimization searching process. Theoretical analysis indicates the guaranteed closed-loop stability and tracking performance while excluding the undesired Zeno behavior. Simulations validate the effectiveness of the proposed event-triggered adaptive control.

A fully distributed adaptive event-triggered control for output regulation of multi-agent systems with directed network

This paper considers the cooperative output regulation problem for heterogeneous linear multi-agent systems via an event-triggered control strategy for the directed graph. First, to cancel the requirement of continuous update of the control rule, a novel distributed event-triggered control protocol such that each agent can determine when to update the control rule asynchronously, where the Zeno behavior is strictly ruled out based on the contradiction. Later, to make the multi-agent systems more flexible, based on the adaptive control technology, the cooperative output regulation problem of heterogeneous linear multi-agent systems can be solved in a fully distributed manner, where the global information, such as the number of agents or the eigenvalues of Laplacian matrix, is no longer needed. Furthermore, the considered communication network is a more general directed network, in which the minimum communication channel of the directed network is halved compared with the undirected case. Finally, the effectiveness of the proposed control strategy is verified on a group of heterogeneous agents.