Time-varying Switching Topologies Research Articles

Finite-Time Dynamic Event-Triggered Full-State Constraints Consensus Control for Multiagent Systems with Switching Topologies and Mismatched Disturbances

This paper investigates finite-time dynamic event-triggered full-state constraints consensus control for nonlinear multiagent systems with switching topologies and unknown disturbances. First, all the system states will not exceed the predefined range, which has guiding significance for the constraint control of velocity and position in multiagent systems. Second, to balance the control errors and event-triggered control, a novel finite-time dynamic event-triggered scheme is developed for multiagent systems, which can achieve fast convergence rates and save communication resources. Third, to improve the system performances, a finite-time command filter is designed to remove transient errors resulting from filtering errors and explosion of complexity. All closed-loop signals are proved to be bounded, and the tracking errors quickly converge into a narrow neighborhood of zero in finite-time. To sum up, the proposed control scheme balances the effects of unknown disturbances, unknown nonlinear dynamics, and limited communications bandwidth, which can improve the control performance for the multiagent systems under time-varying switching topologies. Finally, simulation results validate the suggested approach using a mechanical platform model.

Hierarchically Distributed Charge Control of Plug-In Hybrid Electric Vehicles in a Future Smart Grid

Plug-in hybrid electric vehicles (PHEVs) are becoming increasingly widespread due to their environmental benefits. However, PHEV penetration can overload distribution systems and increase operational costs. It is a major challenge to find an economically optimal solution under the condition of flattening load demand for systems. To this end, we formulate this problem as a two-layer optimization problem, and propose a hierarchical algorithm to solve it. For the upper layer, we flatten the load demand curve by using the water-filling principle. For the lower layer, we minimize the total cost for all consumers through a consensus-like iterative method in a distributed manner. Technical constraints caused by consumer demand and power limitations are both taken into account. In addition, a moving horizon approach is used to handle the random arrival of PHEVs and the inaccuracy of the forecast base demand. This paper focuses on distributed solutions under a time-varying switching topology so that all PHEV chargers conduct local computation and merely communicate with their neighbors, which is substantially different from the existing works. The advantages of our algorithm include a reduction in computational burden and high adaptability, which clearly has its own significance for the future smart grid. Finally, we demonstrate the advantages of the proposed algorithm in both theory and simulation.

Suboptimal Leader-to-Coordination Control for Nonlinear Systems With Switching Topologies: A Learning-Based Method.

In the cooperative control for multiagent systems (MASs), the key issues of distributed interaction, nonlinear characteristics, and optimization should be considered simultaneously, which, however, remain intractable theoretically even to this day. Considering these factors, this article investigates leader-to-formation control and optimization for nonlinear MASs using a learning-based method. Under time-varying switching topology, a fully distributed state observer based on neural networks is designed to reconstruct the dynamics and the state trajectory of the leader signal with arbitrary precision under jointly connected topology assumption. Benefitted from the observers, formation for MASs under switching topologies is transformed into tracking control for each subsystem with continuous state generated by the observers. An augmented system with discounted infinite LQR performance index is considered to optimize the control effect. Due to the complexity of solving the Hamilton-Jacobi-Bellman equation, the optimal value function is approximated by a critic network via the integral reinforcement learning method without the knowledge of drift dynamics. Meanwhile, an actor network is also presented to assure stability. The tracking errors and estimation weighted matrices are proven to be uniformly ultimately bounded. Finally, two illustrative examples are given to show the effectiveness of this method.

A robust distributed observer design for Lipschitz nonlinear systems with time-varying switching topology

This paper deals with state estimation for a class of Lipschitz nonlinear systems under a time-varying disconnected communication network. A distributed observer consists of some local observers that are connected to each other through a communication network. We consider a situation where a communication network does not remain connected all the time, and the network may be caused by intermittent communication link failure. Moreover, each local observer has access to a local measurement, which may be insufficient to ensure the system’s observability, but the collection of all measurements in the network ensures observability. In this condition, the purpose is to design a distributed observer where the estimated state vectors of all local observers converge to the state vector of the system asymptotically, while local observers exchange estimated state vectors through a communication network and use their local measurements. According to theoretical analysis, a nonlinear and a robust nonlinear distributed observer exist when in addition to the union of all communication topologies being strongly connected during a time interval, the component of each communication graph is also strongly connected during each subinterval. The existence conditions of the distributed observers are derived in terms of a set of linear matrix inequalities (LMIs). Finally, the effectiveness of the presented method is numerically verified using some simulation examples.

Multi-VSM based fuzzy adaptive cooperative control strategy for MVDC traction power supply system

This paper studies the control of a medium-voltage DC (MVDC) traction power supply system for rail transit (RT). In order to optimize the ability of DC voltage fluctuation and frequency regulation of rail trains, a fuzzy adaptive cooperative secondary control strategy based on improved virtual synchronous machine (VSM) is proposed. Firstly, the improved VSM control strategy is adopted in the traction substations (TSSs), which makes the RT trains have the external characteristics of synchronous generator and have certain inertia and damping support capacity, so as to improve the ability of the RT traction system to cope with traction network transient changes. Secondly, the virtual inertia and virtual damping can be dynamically adjusted by the system frequency deviating from the nominal steady-state frequency, and the dynamic fuzzy adjustment can be carried out by the fuzzy logic system to slow down the frequency fluctuation. Then, due to the different power supply distances and parameter settings between multiple TSSs, cooperative control is used to coordinate local TSS perceptions of information between adjacent TSS to achieve consistency of frequency response of multiple TSSs under different parameters. Besides, a time-varying switching topology handoff method is considered to select the optimal communication topology between adjacent base stations. Finally, simulation results verify the effectiveness of the proposed control strategy.

Iterative Learning Consensus Tracking for Multi-Agent Systems With Output Constraints and Data Losses

In this paper, the consensus tracking problem for nonlinear multi-agent systems subjected to the output constraint, data loss and switching topologies is considered. Firstly, a consensus term is defined based on the communication information in present of the nonlinear output saturation and time-varying switching topologies. Since the random data loss is taken into consideration, the consensus term is redesigned by an introducing stochastic variable, which obeys the Bernoulli sequence with known probability. Then, a novel distributed ILC algorithm is designed by using the incomplete communication data of agents, which is more universal than the results without considered nonlinear constraint and random factors simultaneously. Through the contraction mapping method, an obtained convergence condition can also guarantee the asymptotic convergence of the agent along the iteration axis under nonlinear saturation factor and random factors. It is verified that the proposed algorithm can handle more complex situations in the consensus control of multi-agent systems. Simulation examples are further provided to verify the effectiveness of the proposed algorithm.

Data Driven Distributed Bipartite Consensus Tracking for Nonlinear Multiagent Systems via Iterative Learning Control

This article explores a data-driven distributed bipartite consensus tracking (DBCT) problem for discrete-time multi-agent systems (MASs) with coopetition networks under repeatable operations. To solve this problem, a time-varying linearization model along the iteration axis is first established by using the measurement input and output (I/O) data of agents. Then a data-driven distributed bipartite consensus iterative learning control (DBCILC) algorithm is proposed considering both fixed and switching topologies. Compared with existing bipartite consensus, the main characteristic is to construct the proposed control protocol without requiring any explicit or implicit information of MASs' mathematical model. The difference from existing iterative learning control (ILC) approaches is that both the cooperative interactions and antagonistic interactions, and time-varying switching topologies are considered. Furthermore, through rigorous theoretical analysis, the proposed DBCILC approach can guarantee the bipartite consensus reducing tracking errors in the limited iteration steps. Moreover, although not all agents can receive information from the virtual leader directly, the proposed distributed scheme can maintain the performance and reduce the costs of communication. The results of three examples further illustrate the correctness, effectiveness, and applicability of the proposed algorithm.

Distributed <inline-formula> <tex-math notation="LaTeX">$H_\infty$ </tex-math> </inline-formula> State Estimation for a Class of Filtering Networks With Time-Varying Switching Topologies and Packet Losses

In this paper, the distributed ${H_{\infty }}$ state estimation problem is investigated for a class of filtering networks with time-varying switching topologies and packet losses. In the filter design, the time-varying switching topologies, partial information exchange between filters, the packet losses in transmission from the neighbor filters and the channel noises are simultaneously considered. The considered topology evolves not only over time, but also by event switches which are assumed to be subjects to a nonhomogeneous Markov chain, and its probability transition matrix is time-varying. Some novel sufficient conditions are obtained for ensuring the exponential stability in mean square and the switching topology-dependent filters are derived such that an optimal ${H_{\infty }}$ disturbance rejection attenuation level can be guaranteed for the estimation disagreement of the filtering network. Finally, simulation examples are provided to demonstrate the effectiveness of the theoretical results.

Formation Merging Control in 3D under Directed and Switching Topologies

The paper studies the formation merging problem for a leader-follower network. That is, how to control a team of agents called followers so that they are merged with a team of agents called leaders to form a larger globally rigid formation. Under the premise that a group of leaders move in a globally rigid formation with their synchronized velocity known to the followers, we show that the followers can asymptotically merge themselves to the formation for arbitrarily initial configurations. Each follower selects its neighbors and also its control law according to the target formation they aim to achieve and thus it allows directed and time-varying switching topologies. It is shown that a globally rigid formation can be merged asymptotically for the leader-follower network in a setup with directed and time-varying graphs if and only if every follower frequently has a joint path from at least a leader.

A revisit to the consensus for linearized Vicsek model under joint rooted leadership via a special matrix

We address the exponential consensus problem for the linearized Vicsek model which was introduced by Jadbabaie et al. in [10] under a joint rooted leadership via the $(sp)$ matrices. This model deals with self-propelled particles moving in the plane with the same speed but differentheadings interacting with neighboring agents by a linear relaxation rule.When the time-varying switching topology of the neighbor graph satisfies some weak connectivity condition, namely, `` joint connectivity condition'' in the spatial-temporal domain, it is well known that the consensus for the linearized Vicsek model can be achieved asymptotically. In this paper, we extend the theory of $(sp)$ matrices and apply it to revisit this asymptotic consensus problem and give an explicit estimate on the maximum Lyapunov exponent, when the underlying network topology satisfies the joint rooted leadership which is directed and non-symmetric.