Event-triggered Consensus Algorithm Research Articles

Monolithic design of self-adaptive CMOS converter and robust event-triggered consensus control for integration of multi-renewable energy sources with battery storage system

Recently, integrating renewable energy sources (RESs) has been one of the most effective ways to overcome the power shortage problems due to the cumulative load demand, which cannot be covered using conventional power production. DC-DC converters with a wide voltage conversion range are highly important when integrating RESs to achieve voltage matching. In recent years, many large-scale improvements have been made to the DC-DC converters to improve reliability, effectiveness, flexibility, modularity, and cost-effectiveness. This study investigates the feasibility of integrating RESs and designing the most effective structure using complementary metal-oxide semiconductors (CMOS) for DC-DC converters. A novel monolithic CMOS buck converter with an adaptive sensing feedback system (ASFS) circuit is proposed for renewable integrated systems. The proposed converter based on ASFS provides a wide range of conversion voltages, low cost, and reliability. It depends on the pulse-width modulation (PWM) technique with a voltage-control duty-cycle (VCDC) circuit. The proposed multi-input DC-DC converter is tested to integrate three RESs to form an integrated hybrid system. The proposed converter is built to integrate the PV, wind turbine (WT), wave energy source, and battery energy storage system (BESS). The proposed DC-DC converter is tested to adjust the output voltage of the RESs at 12 V with a load current of 2.4 A to facilitate the integration process. The results show the success of the proposed converter in maintaining the output voltage at 12 V, with maximum efficiency of the CMOS buck DC-DC converter reaching 96.25 % over a wide input voltage range from 15 V to 35 V. Moreover, the maximum peak-peak ripple over the output voltage reaches 600 mV at the maximum input voltage of 35 V. A control method-based event-triggered consensus algorithm has been proposed to regulate the system voltage and ensure active power sharing in the microgrid system. The controller's settling time and rise time have been measured, and a robustness analysis has been performed based on the connections and disconnections of DGs and the impact of faults.

Research on power quality control method for island microgrid based on event-triggered consensus algorithm

Research on power quality control method for island microgrid based on event-triggered consensus algorithm

Enhanced Coordination in the PV–HESS Microgrids Cluster: Introducing a New Distributed Event Consensus Algorithm

To ensure reliable power delivery to customers under potential disturbances, the coordination of a microgrid cluster (MGC) is essential. Various control strategies—centralized, decentralized, distributed, and hierarchical—have been explored in the literature to achieve this goal. The hierarchical control method, with three distinct levels, has proven effective in fostering coordination among microgrids (MGs) within the cluster. The third control level, utilizing a time-triggering consensus protocol, relies on a continuous and reliable communication network for data exchange among MGs, leading to resource-intensive operations and potential data congestion. Moreover, uncertainties introduced by renewable energy sources (RESs) can adversely impact cluster performance. In response to these challenges, this paper introduces a new distributed event-triggered consensus algorithm (DETC) to enhance the efficiency in handling the aforementioned issues. The proposed algorithm significantly reduces communication burdens, addressing resource usage concerns. The performance of this approach is evaluated through simulations of a cluster comprising four DC MGs, in each of which were PV and a hybrid Battery-Super capacitor in the MATLAB environment. The key findings indicate that the proposed DETC algorithm achieves commendable results in terms of voltage regulation, precise power sharing among sources, and a reduction in triggering instants. Based on these results, this method can be deemed as a good development in MGC management, providing a more efficient and reliable means of coordination, particularly in scenarios with dynamic loads and renewable energy integration. It is also a viable option for current microgrid systems, due to its ability to decrease communication loads while retaining excellent performance.

An Active Equalization Method of Battery Pack Based on Event-Triggered Consensus Algorithm

In this paper, a control strategy of a cell-based multi-agent system is proposed to solve the problem of inconsistency of series lithium-ion battery packs. The bidirectional Cuk converter is utilized as an equalizing circuit serving for balancing adjacent cells in a pack. A SOC-based consensus control with a time-triggered mechanism (TTM) is proposed. In order to reduce the actuator updates, the control method is ameliorated by altering TTM to an event-triggered mechanism (ETM). Adjustable balancing currents are designed in both TTM and ETM methods for the acceleration of the equalization process. The cases in dynamic environments under externally imposed charging/discharging currents by adopting TTM and ETM methods are investigated in detail. By comparison, the simulations and hardware-in-the-loop (HIL) experiments with a Typhoon real-time simulator are illustrated to show that, both in standby or external charging/discharging conditions, the proposed ETM algorithms are superior to TTM’s in terms of equalization time and adaptability to the external environment.

An Operator-Theoretic Approach to Robust Event-Triggered Control of Network Systems With Frequency-Domain Uncertainties

In this paper, we study the robustness of the event-triggered consensus algorithms against frequency- domain uncertainties. It is revealed that the sampling errors resulted by event triggering are essentially images of linear finite-gain L2-stable operators acting on the consensus errors of the sampled states and the event-triggered mechanism is equivalent to a negative feedback loop introduced additionally to the feedback system. In virtue of this, the robust consensus problem of the event-triggered network systems subject to additive dynamic uncertainties and network multiplicative uncertainties are considered, respectively. In both cases, quantitative relationships among the parameters of the controllers, the Laplacian matrix of the network topology, and the robustness against aperiodic event triggering and frequency-domain uncertainties are unveiled. Furthermore, the event-triggered dynamic average consensus (DAC) problem is also investigated, wherein the sampling errors are shown to be images of nonlinear finite-gain operators. The robust performance of the proposed DAC algorithm is analyzed, which indicates that the robustness and the performance are negative

A High-Reliability Event-Triggered Secondary Control Strategy for Microgrid Considering Time-Varying Delay

A decentralized hierarchical control strategy with communication time-varying delay is designed for microgrid (MG) to enhance the voltage and frequency regulation. Recently, event-triggered consensus algorithm is used to reduce energy consumption. To improve the communication reliability, data loss due to communication interruption is considered in this article. First, each distributed generator (DG) of MG is regarded as an agent. Based on multiagent consistency theory, the distributed secondary controller is established. Second, the event trigger function of the secondary controller is constructed, and the stability of the secondary controller is proved by Lyapunov theorem. Third, a data predictive controller is established to train and learn the historical voltage, frequency, and active power of MG by using the improved extreme learning machine (ELM) algorithm. When communication is interrupted, the ELM controller is triggered and the lost data is compensated in time. Finally, the effectiveness of the control strategy is verified by simulation. Simulation results show that the proposed control strategy effectively adjusts the frequency and voltage, realizes accurate active power sharing, reduces the communication burden, and improves the reliability of MG.

Model-Free Event-Triggered Consensus Algorithm for Multiagent Systems Using Reinforcement Learning Method

In this article, we study the consensus issues of multiagent systems (MASs) without any information of the system model by using the reinforcement learning (RL) method and event-based control strategy. First, we design an adaptive event-based consensus control protocol using the local sampled state information so that the consensus errors of all agents are uniformly ultimately bounded. The validity of the above event-triggered adaptive control protocol is confirmed by excluding the Zeno behavior within finite time. Then, based on the RL approach, we present a model-free algorithm to get the feedback gain matrix, and accomplish constructing the adaptive event-triggered control strategy without the knowledge of model information. Distinct with the existing related works, this RL-based event-triggered adaptive control algorithm only relies on the local sampled state information, irrelevant to any model information or global network information. Finally, we provide some examples to demonstrate the validity of the above adaptive event-based consensus algorithm.

Event-Triggered Consensus Control of Nonlinear Strict Feedback Multi-Agent Systems

In this paper, we investigate the event-triggered consensus problems of nonlinear strict feedback MASs under directed graph. Based on the high-gain control technique, we firstly give a state-based event-triggered consensus algorithm and prove that Zeno behavior can be excluded. When the full state information is unavailable, a high-gain observer is given to estimate state information of each agent and an observer-based algorithm is developed. Finally, we give an example to verify the effectiveness of both state-based and observer-based event-triggered consensus algorithms.

Model predictive consensus in networked autonomous systems using discrete Laguerre functions and event triggering approach

This paper presents the consensus problem of general linear discrete time networked autonomous agents, using distributed model predictive control (DMPC) and event triggering algorithm. The computational complexity of conventional DMPC is high due to the large values of prediction horizon and control horizon required for real time systems. The increased computational load becomes an obstruction in the practical implementation of DMPC. To solve the issue, a novel design methodology of parameterizing the control input using discrete Laguerre functions is adopted in this paper. The predictive control protocol is applied to a group of networked autonomous agents operating on event triggered consensus algorithm. The proposed event triggered algorithm offers the advantages of lesser communication between the agents along with reduction in controller updates. The closed loop stability of the networked system is established using Lyapunov criterion, based on the terminal state’s equality constraint. Comparison of the proposed algorithm with existing works through numerical examples shows significant reduction in computational load, design parameters and data transmission, while achieving comparable performance. To validate the theoretical results, simulation is carried out on a group of autonomous vehicles and the usefulness of the proposed algorithm on system performance is illustrated.

Event-triggered stochastic consensus for networked Lagrangian systems with semi-Markov switching topologies and communication delays

In this paper, the event-triggered stochastic consensus for networked Lagrangian systems with semi-Markov switching topologies and communication delays is considered. An event-triggered sampling control strategy is used to design the distributed stochastic consensus scheme of networked Lagrangian systems for two cases with leader and leaderless. Two delay-dependent consensus criteria are derived in the sense of mean square by use of a suitable Lyapunov-Krasovskii functional and the stochastic delayed Halanay inequality, respectively. A key feature of the developed event-triggered consensus algorithm is to introduce a suitable adjustment parameter on consensus control gains characterizing the effect of both semi-Markov switching topology and communication delay. Finally, a numerical example of four manipulators with two links is presented to illustrate the effectiveness of the developed event-triggered methodology.

Cloud-Fog Computing-Based Distributed Event-Triggered Consensus Predictive Compensation for Optimal Energy Management in Microgrid under DoS Attack

A cloud-fog computing-based event-triggered distributed energy optimization management method based on predictive attack compensation is proposed to address the problem of denial of service (DoS) attack, the complexity of computation, and the bandwidth constraint on the communication network in microgrids. Firstly, in order to optimize the energy supply of microgrid and maximize the profit, the minimum cost function of maintaining the balance of supply and demand is given considering the power loss of microgrid. Secondly, considering the problem of bandwidth-constrained communication, a distributed event-triggered consensus algorithm is proposed based on fog computing. Thirdly, a model predictive compensation algorithm based on cloud computing is proposed, which uses the mismatched power between supply and demand at the historical time before the attack to predict and compensate the missing data of the agent power at the current time and many times after attack. Finally, the effectiveness of the proposed method is verified by simulation results.

Modeling and Control of Islanded DC Microgrid Clusters With Hierarchical Event-Triggered Consensus Algorithm

This paper proposes a distributed hierarchical control framework for energy storage systems (ESSs) in DC microgrid clusters, which achieves voltage regulation and current sharing for ESSs in each microgrid as well as the whole microgrid cluster. The primary control stage adopts a droop controller which only requires local information while the secondary control stage provides correction terms for ESSs within microgrids. The tertiary control stage samples the pinned ESSs in different microgrids with low sampling rate to provide the voltage setpoint, which ensures global current sharing among microgrid cluster. The corresponding multilayered event-triggered consensus algorithm for clusters is proposed to reduce the communication cost generated by operation of the distributed controller. Both the control framework and the consensus algorithm can be extended for satisfying higher dimensional regulation needs. The controller is validated in a DC microgrid cluster through simulation under different scenarios, and the results illustrate the effectiveness of the proposed controller.

Hierarchical control strategy for networked DC microgrid based on adaptive dynamic program and event-triggered consensus algorithm considering economy and actuator fault

At present, the hierarchical control strategy which includes cyber and physical layers is one of the most widely used control strategies in networked direct current microgrid. In general, through the controller and actuator, the communication data in cyber layer can be used to accomplish the control missions in physical layer, e.g. achieving stable operation or economic operation etc. However, when actuator fault occurs in communication, the normal operation of cyber layer will be affected. Thus, the control effect of physical layer will be also affected. Considering with the above problems, a hierarchical control strategy based on adaptive dynamic program and event-triggered consensus algorithms is proposed to realize the economic operation of microgrid. In this strategy, the main designs are as follows: 1. To deal with the actuator fault problem in the process of consensus control, the fault observer and adaptive dynamic program-based controller are designed in the cyber layer; 2. Also in the cyber layer, an event-triggered consensus algorithm is proposed to adjust distributed energy resources to output their optimal power and make microgrid run economically; 3. To maintain the voltage stable while the power is adjusted, a secondary control strategy is proposed in the physical layer. Finally, the experimental results have verified the effectiveness of the designed strategies.

Event-triggered control of second-order nonlinear multi-agent systems with directed topology

This paper investigates the distributed event-triggered control problems of second-order nonlinear multi-agent systems (MASs) with directed graph. Firstly, an event-triggered consensus algorithm is developed for second-order nonlinear MASs. It is proved that MASs reach practical consensus and Zeno-behavior doesn't occur. Secondly, for leader–follower MASs, an event-triggered tracking algorithm is presented, which ensures that follower agents track leader agent even when leader agent has a nonzero input. Finally, a numerical simulation is given to show effectiveness of the proposed consensus algorithm.

Event-Triggered Control for a Class of Nonlinear Multiagent Systems With Directed Graph

By using the event-triggered technique, we study the distributed control of a class of nonlinear multiagent systems (MASs) with aperiodic sample information. Taking advantage of the combinational sample measurements, we first present an event-triggered consensus algorithm for the leaderless MAS. Thereafter, we design the event-triggered tracking algorithm for the leader–follower MAS with a leader having bounded input. Furthermore, we prove that both consensus and tracking controllers do not exist Zeno behavior. Finally, a numerical example is given to verify the designed event-triggered consensus algorithm.

Event-triggered Bipartite Consensus for Multi-agent Systems with Antagonistic Interactions

This paper investigates event-triggered control for first-order multi-agent systems with structurally balanced signed graphs. A distributed continuous event-triggered consensus mechanism is firstly proposed for each agent, including the control law based on local sampled states, and the triggering condition constructed with its own state, the sampled states of itself and its neighbours, and an extra signal. No knowledge of the global network is needed. By applying Lyapunov stability theory and the algebraic graph theory, we prove that all agents can reach agreement with identical magnitude but opposite sign while Zeno behaviour is excluded. Moreover, the periodic implementation of the event-triggered consensus algorithm is proposed. The criteria of co-designing the triggering condition parameter and the verification period is established to guarantee the bipartite consensus of the systems. Finally, simulations, including a numerical example and a group of multiple mobile robots, are shown to illustrate the efficiency of the proposed theoretical results.

Co-Regulated Consensus of Cyber-Physical Resources in Multi-Agent Unmanned Aircraft Systems

Intelligent utilization of resources and improved mission performance in an autonomous agent require consideration of cyber and physical resources. The allocation of these resources becomes more complex when the system expands from one agent to multiple agents, and the control shifts from centralized to decentralized. Consensus is a distributed algorithm that lets multiple agents agree on a shared value, but typically does not leverage mobility. We propose a coupled consensus control strategy that co-regulates computation, communication frequency, and connectivity of the agents to achieve faster convergence times at lower communication rates and computational costs. In this strategy, agents move towards a common location to increase connectivity. Simultaneously, the communication frequency is increased when the shared state error between an agent and its connected neighbors is high. When the shared state converges (i.e., consensus is reached), the agents withdraw to the initial positions and the communication frequency is decreased. Convergence properties of our algorithm are demonstrated under the proposed co-regulated control algorithm. We evaluated the proposed approach through a new set of cyber-physical, multi-agent metrics and demonstrated our approach in a simulation of unmanned aircraft systems measuring temperatures at multiple sites. The results demonstrate that, compared with fixed-rate and event-triggered consensus algorithms, our co-regulation scheme can achieve improved performance with fewer resources, while maintaining high reactivity to changes in the environment and system.

Distributed Event-triggered Consensus Control for Heterogeneous Multi-agent Systems under Fixed and Switching Topologies

This paper investigates the distributed consensus problem for heterogeneous multi-agent systems via event-triggered scheme. To alleviate the communication burden, a distributed event-triggered consensus algorithm using an open-loop estimate and neighboring information is proposed. In the proposed event-triggered scheme, the control input of each agent is dependent on the estimate and information at discrete instants. Firstly, the novel event-triggered consensus algorithm is proposed for fixed topology, which involves the discrete communication between neighboring agents. Then, for the switching topology, the triggering condition is proposed by virtue of a topology-dependent dwell time approach. Further, the corresponding consensus procedures are given. Finally, the effectiveness of the proposed scheme is illustrated by numerical results.

A performance guaranteed sampled-data event-triggered consensus approach for linear multi-agent systems

The paper proposes a novel performance guaranteed sampled-data event-triggered consensus (PSEC) algorithm for linear multi-agent systems configured as directed networks. To reduce information exchanges and preserve communication resources, a sampled-data event detector is incorporated at each agent. Communication between agents is based on the fulfillment of distributed state-dependent event-triggering conditions. PSEC ensures a guaranteed exponential convergence rate and is resilient to norm-bounded uncertainties in control gains resulting from implementation distortions. The Lyapunov–Krasovskii theorem is used to incorporate the performance objectives. The design parameters, namely, the heterogeneous control gains and a transmission threshold, are simultaneously computed using a constrained convex optimization framework with linear matrix inequalities. Numerical simulations based on an experimental spacecraft formation flying multi-agent system quantify the effectiveness of the proposed PSEC approach.

Sampling-based event-triggered consensus for multi-agent systems

This work considers the event-based consensus algorithms for multi-agent systems with periodic sampling schemes. An event-triggered consensus algorithm using sampled states are proposed. The measurement error in the event design is defined based on the state of local neighborhood center in order to reduce the number of events. Based on this algorithm, a self-triggered consensus protocol is proposed to further reduce the amount of communication and state sampling, as well as the number of events and thus the controller updates. It is noted that by introducing the periodic sampling scheme, Zeno behaviors can be naturally avoided in both of the algorithms. The effectiveness of the proposed algorithms is illustrated by simulation examples.