The problem of flocking of second-order multiagent systems with connectivity preservation is investigated in this paper. First, for estimating the algebraic connectivity as well as the corresponding eigenvector, a new decentralized inverse power iteration scheme is formulated. Then, based on the estimation of the algebraic connectivity, a set of distributed gradient-based flocking control protocols is built with a new class of generalized hybrid potential fields which could guarantee collision avoidance, desired distance stabilization, and the connectivity of the underlying communication network simultaneously. What is important is that the proposed control scheme allows the existing edges to be broken without violation of connectivity constraints, and thus yields more flexibility of motions and reduces the communication cost for the multiagent system. In the end, nontrivial comparative simulations and experimental results are performed to demonstrate the effectiveness of the theoretical results and highlight the advantages of the proposed estimation scheme and control algorithm.

Flocking of Second-Order Multiagent Systems With Connectivity Preservation Based on Algebraic Connectivity Estimation

https://hal.univ-reunion.fr/hal-01624984/document

A review of fault tolerant control strategies applied to proton exchange membrane fuel cell systems

Overview and benchmark analysis of fuel cell parameters estimation for energy management purposes

Variable numerous types of equipment/subsystems may exist in complex systems, which makes it difficult to analyze and identify electromagnetic emission sources. A theory named “basic emission waveform theory” is presented in this paper to solve this problem. This innovation theory characterizes emission with four basic waveforms, including square wave, sine wave, damped oscillation, and spike wave. Then, the effectiveness of the theory is discussed in both theoretical and engineering ways. In particular, the basic waveforms reflect the physical characteristics of the equipment. Furthermore, the basic waveforms generally do not vary with the locations or loading conditions. Therefore, the identification and analysis of an emission source can be realized by analyzing the basic waveforms from the emission of a complex system. Finally, the applications of damped oscillation and square wave in emission source identification field are used as validations of the proposed theory. Moreover, an example of emission source identification for an electric vehicle is also presented. The examples show that the proposed theory can be used to simply and accurately identify and analyze the emission sources of complex systems.

Basic Emission Waveform Theory: A Novel Interpretation and Source Identification Method for Electromagnetic Emission of Complex Systems

This article investigates the event-triggered leader–follower consensus control problem for linear continuous-time multiagent systems (MASs). A new consensus protocol and an event-triggered communication (ETC) strategy based on a closed-loop state estimator are designed. The closed-looped state estimator renders us more accurate state estimations, therefore the triggering times can be decreased while maintaining control performance. We show that the consensus of MASs can be achieved by employing the proposed control scheme. In addition, the Zeno-phenomena can be excluded. As a practical application, our method is successfully applied to the vehicle platoon control. The effectiveness is further verified by simulations conducted on the Prescan platform.

Distributed Model-Based Event-Triggered Leader–Follower Consensus Control for Linear Continuous-Time Multiagent Systems

The proton exchange membrane (PEM) fuel cells have attracted significant attention due to high efficiency and low pollution. The performance and lifetime of the fuel cell power system depend on the heat and water management, gas flow control and pressure regulation. This paper focuses on the oxygen excess ratio regulation for the fuel cell system to improve the performance and extend the lifetime. Firstly, a third-order model of the air delivery subsystem is introduced based on suitable assumption and simplification. Secondly, the third-order model is transformed to the non-affine nonlinear form, and the oxygen excess ratio regulation is reconsidered as load current tracking problem. Using the backstepping technique and neural network, an adaptive controller is proposed for the tracking problem and guarantees the system's stability in presence of modeling errors, external disturbances and uncertainties. Finally, the simulation illustrates the effectiveness of the adaptive controller.

Adaptive control of air delivery system for PEM fuel cell using backstepping

This paper investigates the problem of connectivity-preserving flocking of multiple autonomous agents with second-order dynamics. First, the inverse power iteration algorithm is formulated in a completely distributed manner to estimate the algebraic connectivity, i.e., the second smallest eigenvalue of the group Laplacian, as well as the corresponding eigenvector. Furthermore, distributed gradient-based flocking algorithms that exploit decentralized eigenvalue/eigenvector estimation are developed both to steer the agent group to the desired flocking motion and to maintain the global connectivity of the underlying network during maneuvers. Different from the common potential/tension function method which keeps certain fixed edges all the time, the algorithm proposed in this paper guarantees the global connectivity which allows any existing edge to be broken, thus gives more freedom of motions for the agents. Finally, nontrivial simulations are performed to demonstrate the correctness and effectiveness of the theoretical results.

Distributed flocking of second-order multi-agent systems with global connectivity maintenance

It is important to predict fuel cells' behaviors for fuel cell control, power management and other practical applications. In this paper, a Gaussian radial basis function (GRBF) variable neural network is used to on-line identify the PEM (Polymer Electrolyte Membrane) fuel cell model. The structure of the neural network changes over time according to the required accuracy and complexity. Finally, a real test data of fuel cell power system is used to illustrate the effectiveness of the variable neural network for online identification of the fuel cell model. The result shows that this method guarantees the output of the predictive model attains the required accuracy.

On-line identification of fuel cell model with variable neural network

Cooperative adaptive cruise control(CACC), as one of the most important technologies of intelligent transportation system(ITS), combines the adaptive cruise control(ACC) with short range communication(DSRC), which can make the platooning distance much more smaller and can avoid collision efficiently As we all know, the more information the vehicles can receive from the platoon, the better overall stability the vehicles can keep However, that will lead to an increase in communication resource consumption and cause signal interference with the surrounding vehicles Therefore, we need to make a tradeoff between the stability of the platoon and communication expense when designing the platoon controller In this paper we proposed a distributed topology switching algorithm for heterogeneous vehicle platoon, by which the vehicles can switch communication topology automatically according to their state changes and design the liner controller based on the patial given topology We can learn from the simulation results that the proposed topology switching strategy can balance the control performance and communication spending obviously

Distributed Topology Switching Strategy Designing for Heterogeneous Vehicle Platoons

This paper first designs the compensation for the nonlinear friction which exists when the position servo platform runs at very low rates; Second, sliding mode variable structure controller based on disturbance obserber was proposed for the external distances and internal parameter variables of the system. On the one hand the disturbance observer can compensate most of the external disturbances and the uncertainty, further the chattering of SMVSC can be reduced; On the other hand the SMVSC could eliminate the disturbance observer errors, to meet the requirements of the system tracking performance. Simulation results show that, the sliding mode variable structure controller based on a disturbance observer has certain robustness for external disturbances and parameter uncertainty, and achieves good control effect.

Tao Cai

Papers

Adaptive control of air delivery system for PEM fuel cell using backstepping

Distributed flocking of second-order multi-agent systems with global connectivity maintenance

On-line identification of fuel cell model with variable neural network

Distributed Topology Switching Strategy Designing for Heterogeneous Vehicle Platoons

Sliding mode variable structure controller design based on disturbance observer