Distributed optimization with information-constrained population dynamics     

A. Pantoja  G. Obando  N. Quijano 《Journal of The Franklin Institute》2019,356(1):209-236

In a multi-agent framework, distributed optimization problems are generally described as the minimization of a global objective function, where each agent can get information only from a neighborhood defined by a network topology. To solve the problem, this work presents an information-constrained strategy based on population dynamics, where payoff functions and tasks are assigned to each node in a connected graph. We prove that the so-called distributed replicator equation (DRE) converges to an optimal global outcome by means of the local-information exchange subject to the topological constraints of the graph. To show the application of the proposed strategy, we implement the DRE to solve an economic dispatch problem with distributed generation. We also present some simulation results to illustrate the theoretic optimality and stability of the equilibrium points and the effects of typical network topologies on the convergence rate of the algorithm.  相似文献   

Stabilization of switched linear singular systems with state reset     

Xiaoqing Xiao  Ju H. Park  Lei Zhou 《Journal of The Franklin Institute》2019,356(1):237-247

This paper investigates the stability and stabilization of switched linear singular systems with state reset at switching instants. Based on the dynamics decomposition of singular subsystems, a sufficient stability condition for the system with the given state reset is obtained. Then, the stabilization problem by state reset is investigated and an algorithm for computing the reset matrices is presented. The obtained results extend some previous works on both singular switched systems and reset control for normal switched systems. Finally, a numerical example is presented to illustrate the effectiveness of the proposed approach.  相似文献   

The filtering based parameter identification for bilinear-in-parameter systems     

Xuehai Wang  Feng Ding 《Journal of The Franklin Institute》2019,356(1):514-538

This paper discusses the parameter estimation for a class of bilinear-in-parameter systems with colored noise. By utilizing the filtering technique, we derive the relationship between the filtered output and the measurement output and obtain two linear regressive sub-models. A filtering based multi-innovation stochastic gradient algorithm is derived for interactively identifying each sub-model. The proposed algorithm avoids the estimation of correlated noise and improves the parameter estimation accuracy by making full use of the measurement data. The numerical simulation results indicate that the proposed algorithm has higher estimation accuracy than the hierarchical multi-innovation stochastic gradient algorithm.  相似文献   

Global sampled-data output feedback stabilization for a class of stochastic nonlinear systems with time-varying delay     

Wenmin He  Jian Guo  Zhengrong Xiang 《Journal of The Franklin Institute》2019,356(1):292-308

This paper studies the global sampled-data output feedback stabilization problem for a class of stochastic nonlinear systems. The considered system is in non-strict feedback form with unknown time-varying delay. A state observer is introduced to estimate the unmeasured states. With the help of the backstepping method, a linear sampled-data output feedback controller is constructed. By choosing an appropriate Lyapunov–Krasoviskii functional and an allowable sampling period, it is shown that the stochastic system can be globally asymptotically stabilized in the mean square sense under the developed control scheme. Finally, two examples are presented to verify the effectiveness of the designed control scheme.  相似文献   

Multi-lagged-input iterative dynamic linearization based data-driven adaptive iterative learning control     

Na Lin  Ronghu Chi  Biao Huang  Zhongsheng Hou 《Journal of The Franklin Institute》2019,356(1):457-473

This article presents a multi-lagged-input based data-driven adaptive iterative learning control (M-DDAILC) method for nonlinear multiple-input-multiple-output (MIMO) systems by virtue of multi-lagged-input iterative dynamic linearization (IDL). The original nonlinear and non-affine MIMO system is equivalently transformed into a linear input-output incremental counterpart without loss of dynamics. The proposed learning law utilizes the desired trajectory to cancel the influence from iteration-by-iteration variations, as well as additional multi-lagged inputs to improve control performance. The developed iterative estimation law is more effective and also makes estimation of the unknown parameters easier because the dynamics for each parameter to represent are decreased by dividing the system into multiple components in the multi-lagged-input IDL formulation. Moreover, the proposed M-DDAILC does not need an explicit and accurate model. It is proved to be iteratively convergent with rigorous analysis. Both a numerical example and a practical application to a permanent magnet linear motor are provided to verify the validity and applicability of the proposed method.  相似文献   

Urban transportation network analysis from a thermodynamic perspective     

Mohamad Sleiman  Rachid Bouyekhf  Adbellah El Moudni 《Journal of The Franklin Institute》2019,356(1):351-381

The paper explores the application of thermodynamic formalism to model and control transportation networks. Specifically, by considering the vehicles as the abstract energy supplied to the system, we show in certain circumstances that certain thermodynamic concepts such as temperature, thermal capacity and thermal equilibrium can have the corresponding notions in transportation context. In addition, despite the lack of a natural principle in transportation context that corresponds to the second law of thermodynamic as we will show, the most important thermodynamic notion, which is the entropy, can be also defined in order to measure the disorder of transportation systems. It is then shown that the state when all lanes have the same occupancy corresponds to the thermal equilibrium arising in isolated thermodynamic system. This equilibrium occupancy leads to a minimum entropy corresponding to a minimal disorder. Besides, by taking the transportation entropy as the storage function, a robust dissipativity based control strategy is presented to reduce the disorder and render the system better organized. Finally, an example is worked out to illustrate the results.  相似文献   

Formation control and collision avoidance for a class of multi-agent systems     

Yutong Liu  Hongjun Yu  Peng Shi  Cheng-Chew Lim 《Journal of The Franklin Institute》2019,356(10):5395-5420

In this paper, a control scheme is proposed for a group of elliptical agents to achieve a predefined formation. The agents are assumed to have the same dynamics, and communication among the agents are limited. The desired formation is realized based on the reference formation and the mapping decision. In the control design, searching algorithms for both cases of minimum distance and tangents are established for each agent and its neighbors. In order to avoid collision, an optimal path planning algorithm based on collision angles, and a self-center-based rotation algorithm are also proposed. Moreover, randomized method is used to provide the optimal mapping decision for the underlying system. Two examples and analyses are presented to demonstrate the effectiveness and potential of the new control scheme.  相似文献   

Finite-time synchronization of delayed complex dynamic networks via aperiodically intermittent control     

Taiyan Jing  Daoyuan Zhang  Jun Mei  Yuling Fan 《Journal of The Franklin Institute》2019,356(10):5464-5484

In this paper, the finite-time synchronization problem of complex dynamic networks with time delay is studied via aperiodically intermittent control. By compared with the existed results concerning aperiodically intermittent control, some new results are obtained to guarantee the synchronization of networks in a finite time. Especially, a new lemma is proposed to reduce the convergence time. In addition, based on aperiodically intermittent control scheme, the essential condition ensuring finite-time synchronization of dynamic networks is also obtained, and the convergence time is closely related to the topological structure of networks and the maximum ratio of the rest width to the aperiodic time span. Finally, a numerical example is provided to verify the validness of the proposed theoretical results.  相似文献   

H∞ tracking control for a class of asynchronous switched nonlinear systems with uncertain input delay     

S. Pezeshki  M.A. Badamchizadeh  A.R. Ghiasi  S. Ghaemi 《Journal of The Franklin Institute》2019,356(12):5927-5943

This paper studies the problems of stability and H∞ model reference tracking performance for a class of asynchronous switched nonlinear systems with uncertain input delay. First, it is assumed switched controller and corresponding piecewise Lyapunov function are unknown but the derivative of piecewise Lyapunov function has a condition; this condition implies that the nominal system (system without input delay and disturbance) is exponentially stable by any switched controller which satisfies this condition. With this assumption, a proper Lyapunov–Krasovskii functional is constructed. By employing this new functional and average dwell time technique, the delay-dependent input-to-state stability criteria are derived under a certain delay bound; in addition, a mechanism which finds the upper bound of input delay is proposed. Finally, a kind of state feedback control law which fulfils condition of aforesaid piecewise Lyapunov function is introduced to guarantee the input-to-state stability and H∞ model reference tracking performance. Simulation examples are presented to demonstrate the efficacy of results.  相似文献   

Optimal synchronization controller design for complex dynamical networks with unknown system dynamics     

Ya-Wei Cao  Guang-Hong Yang  Xiao-Jian Li 《Journal of The Franklin Institute》2019,356(12):6071-6086

In this paper, the optimal synchronization controller design problem for complex dynamical networks with unknown system internal dynamics is studied. A necessary and sufficient condition on the existence of the optimal control minimizing a quadratic performance index is given. The optimal control law consists of a feedback control and a compensated feedforward control, and the feedback control gain can be obtained by solving the well-known Algebraic Riccati Equation (ARE). Especially, in the presence of unknown system dynamics, a novel adaptive iterative algorithm using the information of system states and inputs is proposed to solve the ARE to get the optimal feedback control gain. Finally, a simulation example shows the effectiveness of the theoretical results.  相似文献