Event-driven Control Research Articles

A cloud-edge framework for energy-efficient event-driven control: an integration of online supervised learning, spiking neural networks and local plasticity rules

Abstract This paper presents a novel cloud-edge framework for addressing energy constraints in complex control systems. Our approach centers around a learning-based controller using Spiking Neural Networks (SNN) on physical plants. By integrating a biologically plausible learning method with local plasticity rules, we harness the energy efficiency, scalability of the newtwork, and low latency of SNNs. This design replicates control signals from a cloud-based controller directly on the plant, reducing the need for constant plant-cloud communication. The plant updates weights only when errors surpass predefined thresholds, ensuring efficiency and robustness in various conditions. Applied to linear workbench systems and satellite rendezvous scenarios, including obstacle avoidance, our architecture dramatically lowers normalized tracking error by 96% with increased network size. The event-driven nature of SNNs minimizes energy consumption, utilizing only about 11.1 × 104 pJ (0.3% of conventional computing requirements). The results demonstrate the system’s adjustment to changing work environments and its efficient use of energy resources, with a moderate increase in energy consumption of 37% for dynamic obstacles, compared to non-obstacle scenarios.

Event-driven intelligent fault-tolerant containment control for nonlinear multiagent systems with unknown disturbances

This paper investigates the event-driven intelligent fault-tolerant containment control problem for nonlinear multiagent systems affected by unknown disturbances, and conducts a comprehensive analysis of the dynamic stability of the system. Reduced-order modelling is employed for system modelling and identification, while radial basis function neural networks are utilised to estimate the nonlinear functions of the system model. An event-driven control scheme is designed based on the backstepping method and Lyapunov functional approach. In contrast to existing consensus control schemes, the proposed event-driven control schemes are specifically tailored to improve the energy efficiency and endurance of nonlinear multiagent systems operating in complex environments. Under the proposed control law, the output of each follower is converge to the convex hull spanned by the outputs of the leaders. The effectiveness of the proposed method is rigorously validated through numerical simulations. Furthermore, a practical example involving the design of a marine surface vehicle using advanced robotics technology is presented to further substantiate the efficacy of the proposed method.

Energy-conserving event-triggered control for multiagent systems with limited resources

An energy-conserving event-driven control method is developed for multiagent systems with unknown disturbances to achieve low-energy formation control. Firstly, an energy-conserving event-driven scheme is developed to save energy by introducing a second trigger function, which is utilized to switch the control input to zero when the consensus error is small. Then, a dynamic energy-conserving event-driven scheme is designed to further decrease the frequency of communication and control by establishing a positive dynamic variable to increase the trigger threshold. Under the designed energy-conserving event-based schemes, the energy consumption during the system stability is significantly reduced, the communication burden is decreased, and the system is Zeno-free and bounded stable. Finally, a four-agent formation example is illustrated to show the effectiveness of the proposed energy-conserving schemes.

Self-triggered formation attitude control for multiple spacecraft with disturbances

This paper studies the attitude consensus for nonlinear multi-spacecraft systems with disturbances and limited communication bandwidth and computing resources. A dynamic event-based attitude controller is developed to conserve communication resources, where a positive constant term is introduced to improve resource conservation efficiency. The developed event-driven controller is updated according to the constructed triggering mechanism, which greatly reduces the number of computations for the control algorithm. The triggering mechanism does not require continuous state information of neighboring spacecraft, which indicates that continuous communication is not needed for the operation of the formation system. Then, a self-triggered mechanism using the current triggering information is designed to further avoid continuous computation, and the avoidance of logarithmic functions enables the trigger interval to be close to that of the dynamic event-driven algorithm. The formation system is Zeno-free and uniformly ultimately bounded stable under the proposed event-based consensus control framework with the triggering strategies. Finally, simulations exhibit that the dynamic event-triggered protocol can reduce the communication frequency by 88%, and the self-triggered protocol can reduce the communication and computation frequency by 83% and have higher control accuracy than the dynamic event-triggered protocol.

Event-triggered adaptive secure tracking control for nonlinear cyber–physical systems against unknown deception attacks

This article presents an event-triggered adaptive neural networks secure tracking control method for a class of nonlinear cyber–physical systems under unknown sensor and actuator deception attacks. To obtain the desired system performance, dynamic surface technique is applied to design controller and radial basis function neural networks are introduced to deal with unknown nonlinear and actuator attacks. By skillfully combining compensation signals with the attack compensators, the unknown deception attacks are effectively mitigated. To reduce the transmission communication load, a novel event-driven control scheme is developed by applying relative threshold triggered mechanism. On this basis, all the signals of closed-loop system are bounded under deception attacks by using Lyapunov stability analysis. Additionally, the presented secure tracking control strategy can ensure the tracking error converges to a small neighborhood of origin and the Zeno behavior is ruled out. Finally, a practical example is employed to verify the feasibility and effectiveness of the designed control algorithm.

A PID Control Architecture Based on IEC 61499

This work presents an approach for PID control and optimization in a distributed system, using the IEC 61499 control standard. This standard enables communication among different PLCs, which are used to develop a three-layered event-driven control of a SISO loop. The lowest layer is in charge of cyclical data acquisition. The second layer carries out an event-based PID control. The highest layer runs a control optimization algorithm, specifically a simple tuning approach based on Ziegler-Nichols that is used to determine the PID parameters for different operating points. The proposed approach is assessed in a tank level SISO control problem, whose behavior can be modeled as a first-order plus dead time system in each operating point. For that purpose, it has been implemented using two PLCs and a software PLC running on an industrial computer. The experimental results on the SISO level control loop show the feasibility of the proposed approach for event-driven control in a distributed system and open interesting research questions in the intersection of controller design and distributed automation.

ОБЗОР НЕЙРОМОРФНЫХ ЧИПОВ ДЛЯ СИСТЕМ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

Neuromorphic chips are innovative computing architectures inspired by the principles of biological neural networks and promise to revolutionize the way we build energy-efficient, high-perfor-mance artificial intelligence (AI) systems. These chips offer a new way of processing data based on paral-lelism, event-driven control, and adaptive learning, which can significantly improve performance at low power consumption. This article discusses the main architectural features of neuromorphic chips, their types and areas of application, and analyzes current challenges and development prospects. The ad-vantages and limitations of such chips for AI tasks, as well as their potential in the future, are shown.

Reinforcement Learning for Robust Dynamic Event-Driven Constrained Control.

We consider a robust dynamic event-driven control (EDC) problem of nonlinear systems having both unmatched perturbations and unknown styles of constraints. Specifically, the constraints imposed on the nonlinear systems' input could be symmetric or asymmetric. Initially, to tackle such constraints, we construct a novel nonquadratic cost function for the constrained auxiliary system. Then, we propose a dynamic event-triggering mechanism relied on the time-based variable and the system states simultaneously for cutting down the computational load. Meanwhile, we show that the robust dynamic EDC of original nonlinear-constrained systems could be acquired by solving the event-driven optimal control problem of the constrained auxiliary system. After that, we develop the corresponding event-driven Hamilton-Jacobi-Bellman equation, and then solve it through a unique critic neural network (CNN) in the reinforcement learning framework. To relax the persistence of excitation condition in tuning CNN's weights, we incorporate experience replay into the gradient descent method. With the aid of Lyapunov's approach, we prove that the closed-loop auxiliary system and the weight estimation error are uniformly ultimately bounded stable. Finally, two examples, including a nonlinear plant and the pendulum system, are utilized to validate the theoretical claims.

Protocol-Based Synchronization of Stochastic Jumping Inertial Neural Networks Under Image Encryption Application.

This work investigates the protocol-based synchronization of inertial neural networks (INNs) with stochastic semi-Markovian jumping parameters and image encryption application. The semi-Markovian jumping process is adopted to characterize INNs under sudden complex changes. To conserve the limited available network bandwidth, an adaptive event-driven protocol (AEDP) is developed in the corresponding semi-Markovian jumping INNs (S-MJINNs), which not only reduces the amount of data transmission but also avoids the Zeno phenomenon. The objective is to construct an adaptive event-driven controller so that the drive and response systems maintain synchronous relationships. Based on the appropriate Lyapunov functional, integral inequality, and free weighting matrix, novel criteria are derived to realize the synchronization. Moreover, the desired adaptive event-driven controller is designed under a semi-Markovian jumping process. The proposed method is demonstrated through a numerical example and an image encryption process.

Adaptive Event-Driven Control for Continuous Positive Stochastic Jump Models With Cyber Attacks

Adaptive Event-Driven Control for Continuous Positive Stochastic Jump Models With Cyber Attacks

Fully Distributed Event-Driven Adaptive Consensus of Unknown Linear Systems.

This article considers the consensus problem of unknown linear multiagent systems (MASs) through adaptive event-driven control in leader-follower and leaderless networks. The proposed event-driven algorithms do not involve any global information related to the network communication structure and rely only on local information exchange to achieve consensus on MASs and are therefore fully distributed. Furthermore, the constraint of continuous communication among the agents is eliminated in terms of control law updates and triggering state monitoring. Another desirable aspect of this article is that the design process of the control algorithms is independent of the parameters of each agent's dynamics and thus does not require precise information about the dynamics of MASs. We further exclude the Zeno behavior of each agent by proving the existence of a strict positive lower bound between any two adjacent events. Finally, the effectiveness of the proposed adaptive event-driven algorithms is verified by a simulation example.

Adaptive controller based on barrier Lyapunov function for a composite Cartesian-delta robotic device for precise time-varying position tracking

This study presents the design of an adaptive event-driven controller for solving the trajectory tracking problem of a composite robotic device made up of a three-dimensional Cartesian and a parallel Delta robot. The proposed composite device has a mathematical model satisfying a standard Lagrangian structure affected by modeling uncertainties and external perturbations. The adaptive gain of the controller is considered to enforce the convergence of the tracking error while the state bounds are satisfied. The barrier Lyapunov function addresses the preconceived state constraints for both robotic devices by designing a time-varying gain that guarantees the ultimate boundedness of the tracking error under the effect of external perturbations. The event-driven approach considers that the Cartesian robot is moving into a predefined invariant zone near to the origin. In contrast, the delta robot can complete the tracking problem once the end-effector is inside the given zone. The suggested controller was evaluated using a virtual representation of the composite robotic device showing better tracking performance (while the restrictions are satisfied) than the performances obtained with the traditional linear state feedback controllers. Analyzing the mean square error and its integral led to confirming the benefits of using the adaptive barrier control.

Extended Discrete-State Event-Driven Hardware-in-the-Loop Simulation for Power Electronic Systems Based on Virtual-Time-Ratio Regulation

This paper proposes an extended discrete-state event-driven (EDSED) controller hardware-in-the-loop (CHIL) method for power electronic systems, employing virtual time ratio (VTR) regulation to achieve CHIL function. Specifically, the proposed VTR in CHIL simulation is introduced to define the time ratio at which the mathematical model of the power circuit runs in the simulator. Additionally, by adjusting different VTRs, the data interaction between the simulator and controller is modified and employed as an event to trigger both simulation and control calculations, ensuring a precise correlation between simulation data and control information. The proposed EDSED CHIL simulation method is validated through CHIL and full hardware experiments involving a power electronic transformer and a multi-port power router. This paper offers an effective CHIL solution for testing control strategies and codes in power electronic systems characterized by complex coupling and high frequency.

Event-driven demand response control of air-conditioning to enable grid-responsive buildings

Fast demand response capability of a grid-responsive building is of particularly high value to meet urgent power balance needs of power grids. In this study, an event-driven control strategy is proposed to effectively unlock building energy flexibility for fast demand response using building air-conditioning systems. The proposed control strategy can properly distribute the chilled water among different zones in a building to allow uniform thermal comfort compromise. Validation tests are conducted and the results show that the limited cooling supply can be distributed properly and the same indoor air temperature profiles can be achieved eventually among the indoor spaces. The power demand of the chiller plant is reduced by 170 kW (5%) by adopting the proposed event-driven control, while achieving the same comfort compromise as existing time-driven control. The average accumulated valve travel distance is also reduced by 54.6%, reducing the wear and tear of the AHU valves significantly.

Optimizing Data Center Energy Efficiency via Event-Driven Deep Reinforcement Learning

To reduce the skyrocketing energy consumption of data centers, the prevailing approaches adopt the time-driven manner to control IT and cooling subsystems. These methods suffer from highly dynamic system states, complex action spaces and the risk of instability caused by frequent and unnecessary control operations. To tackle these problems, we propose a novel event-driven control paradigm and an optimization algorithm, under the deep reinforcement learning (DRL) framework. The principle is to make decisions based on certain critical events (e.g., overheating), rather than fixed periodic control. Specifically, we design an event-driven optimization framework to trigger control operations. Then, we present several models to describe IT and cooling subsystems, and mathematically define events to capture four types of prior factors that impact system performance. Furthermore, we develop an event-driven DRL (E-DRL) optimization algorithm to dispatch jobs and regulate cooling facilities for energy efficiency. Using two different types of real workload traces, we conduct extensive experiments to demonstrate that: 1) E-DRL reduces the number of regulating decisions by 70% <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 95% while achieving a comparable or even better energy efficiency in comparison with the state-of-the-art algorithm; and 2) E-DRL can adapt the control frequency to the changing operational conditions and diverse workloads.

Energetic optimization of an autonomous mobile socially assistive robot for autism spectrum disorder.

The usage of socially assistive robots for autism therapies has increased in recent years. This novel therapeutic tool allows the specialist to keep track of the improvement in socially assistive tasks for autistic children, who hypothetically prefer object-based over human interactions. These kinds of tools also allow the collection of new information to early diagnose neurodevelopment disabilities. This work presents the integration of an output feedback adaptive controller for trajectory tracking and energetic autonomy of a mobile socially assistive robot for autism spectrum disorder under an event-driven control scheme. The proposed implementation integrates facial expression and emotion recognition algorithms to detect the emotions and identities of users (providing robustness to the algorithm since it automatically generates the missing input parameters, which allows it to complete the recognition) to detonate a set of adequate trajectories. The algorithmic implementation for the proposed socially assistive robot is presented and implemented in the Linux-based Robot Operating System. It is considered that the optimization of energetic consumption of the proposal is the main contribution of this work, as it will allow therapists to extend and adapt sessions with autistic children. The experiment that validates the energetic optimization of the proposed integration of an event-driven control scheme is presented.

ON/OFF control for reaching a steady state attractive region in batches Petri nets

This paper addresses an event-driven control problem of controlled generalized batches Petri nets without discrete nodes, i.e., a class of hybrid Petri net formalisms. The goal of the controller is to reach a given steady state from an initial marking qualified as blocking, by controlling the flow of transitions. To this end, we firstly characterize an attractive region of the steady state as an optimal solution of a linear programming problem (LPP). Next, based on the ON/OFF state of transitions, which depends on the enabling conditions and the firing quantities, an ON/OFF event-driven control strategy is proposed for reaching, from a blocking initial marking quantity, the nearest marking quantity in the attractive region. The admissible controlled firing flow vector is computed according to a LPP which maximizes the firing flow of ON-transitions to its maximal firing flow while it imposes a null firing flow to OFF-transitions. The proposed strategy is event-driven and the control actions are updated only when specific events occur. The basic idea of this new control strategy is to fire each transition as fast as possible at any event step until the minimal firing quantity vector is reached.

Synchronization of hybrid switching diffusions delayed networks via stochastic event-triggered control

In this paper, the synchronization problem of stochastic complex networks with time delays and hybrid switching diffusions (SCNTH) is concerned based on event-triggered control. Therein, a new class of event-triggered function is proposed for the control design. Particularly, different from the existing work, the triggered instant generated by event-triggered control in this paper is a stochastic sequence instead of a number sequence to be more realistic for stochastic systems, which is a breakthrough. Furthermore, some sufficient conditions are derived to guarantee asymptotical synchronization in mean square, exponential synchronization in mean square and almost surely exponential synchronization of SCNTH based on sampled-data control, event-driven control theory and stability analysis. Meanwhile, the Zeno phenomenon can be avoided. Then, the synchronization of single-link robot arms is investigated in detail as a practical application of the obtained results. Ultimately, a numerical example is given for demonstration.

Impulsive stabilization of complex-valued stochastic complex networks via periodic self-triggered intermittent control

The aim of this article is to research the stabilization issue of complex-valued stochastic Markovian switching complex networks with time delays and time-varying multi-links (CSMCTM) via periodic self-triggered intermittent impulsive control (PSIIC). Thereinto, PSIIC is designed for the first time by combining intermittent impulsive control with periodic self-triggered control for intermittent control. It is worth emphasizing that the triggered protocol is designed to be more flexible, and easier to implement than previously reported triggered protocol. Then, by means of impulsive control, intermittent control, event-driven control theory and stability analysis, a stabilization criterion of CSMCTM in the sense of exponential stability in mean square is obtained. Whereafter, the stability of a class of complex-valued inertial neural networks is researched as a practical application of our theoretical results. Ultimately, a numerical example gives a corresponding verification.

An adaptive event-driven fault-tolerant consensus proposed for decentralized multiple AUVs with actuator multiplicative faults

The present paper studies a distributed leaderless multi-autonomous underwater vehicle (AUV) system under a heterogeneous topology network where multiplicative faults and input saturation affect actuators, and the actuator fault severity could not be equal in each AUV. For this system, an adaptive event-driven fault-tolerant consensus control protocol is proposed. The present paper also introduces adaptive parameters in the trigger function to improve the self-regulation capability of the event-driven mechanism system. The proposed fully distributed event-driven fault-tolerant consensus strategy depends on the adaptive coupling gain of the system, and it eliminates the global information restriction. It was found that the designed event-driven controller has a good fault tolerance effect on multiplicative faults of actuators. The states of the multi-AUV system could asymptotically converge to the same conditions without Zeno behavior. Finally, the Lyapunov functional method proves the system's astringency and stability, and the numerical simulation presents the strategy's effectiveness.