A continuous-time consensus algorithm using neurodynamic system for distributed time-varying optimization with inequality constraints

In this paper, a distributed time-varying convex optimization problem with inequality constraints is discussed based on neurodynamic system. The goal is to minimize the sum of agents’ local time-varying objective functions subject to some time-varying inequality constraints, each of which is known only to an individual agent. Here, the optimal solution is time-varying instead of constant. Under an undirected and connected graph, a distributed continuous-time consensus algorithm is designed by using neurodynamic system, signum functions and log-barrier penalty functions. The proposed algorithm can be understood through two parts: one part is used to reach consensus and the other is used to achieve gradient descent to track the optimal solution. Theoretical studies indicate that all agents will achieve consensus and the proposed algorithm can track the optimal solution of the time-varying convex problem. Two numerical examples are provided to validate the theoretical results.     

Seeking strategy design for distributed nonsmooth games and its application

This paper investigates the nonsmooth noncooperative games (NGs) over unbalanced digraphs, where each player has a nondifferentiable payoff function. Meanwhile, these players are subject to local nonsmooth inequality constraints, local convex set constraints and coupled equality constraints. Due to the coexistence of nonsmooth payoff functions, unbalanced digraphs and constraints, existing generalized Nash equilibrium (GNE) seeking strategies are fail to address our problem. Based on subgradient descents, differential inclusions, projection and primal-dual methods, we develop a distributed seeking strategy. Besides, the convergence of the strategy is proved. Finally, the proposed method is applied to electricity market games (EMGs) of smart grids.     

Distributed optimization problem for second-order multi-agent systems with event-triggered and time-triggered communication

This paper deals with the large category of convex optimization problems on the framework of second-order multi-agent systems, where each distinct agent is assigned with a local objective function, and the overall optimization problem is defined as minimizing the sum of all the local objective functions. To solve this problem, two distributed optimization algorithms are proposed, namely, a time-triggered algorithm and an event-triggered algorithm, to make all agents converge to the optimal solution of the optimization problem cooperatively. The main advantage of our algorithms is to remove unnecessary communications, and hence reduce communication costs and energy consumptions in real-time applications. Moreover, in the proposed algorithms, each agent uses only the position information from its neighbors. With the design of the Lyapunov function, the criteria about the controller parameters are derived to ensure the algorithms converge to the optimal solution. Finally, numerical examples are given to illustrate the effectiveness of the proposed algorithms.     

Two-stage continuous-time triggered algorithms for constrained distributed optimization over directed graphs

This paper proposes two-stage continuous-time triggered algorithms for solving distributed optimization problems with inequality constraints over directed graphs. The inequality constraints are penalized by adopting log-barrier penalty method. The first stage of the proposed algorithms is capable of finding the optimal point of each local optimization problem in finite time. In the second stage of the proposed algorithms, zero-gradient-sum algorithms with time-triggered and event-triggered communication strategies are considered in order to reduce communication costs. Then, with the help of LaSalle’s invariance principle, it is proved that the state solution of each agent reaches consensus at the optimal point of the considered penalty distributed optimization problem, and Zeno behavior is also excluded. Finally, numerical examples are given to illustrate the effectiveness of the proposed algorithms.     

Primal-dual stochastic distributed algorithm for constrained convex optimization

This paper investigates distributed convex optimization problems over an undirected and connected network, where each node’s variable lies in a private constrained convex set, and overall nodes aim at collectively minimizing the sum of all local objective functions. Motivated by a variety of applications in machine learning problems with large-scale training sets distributed to multiple autonomous nodes, each local objective function is further designed as the average of moderate number of local instantaneous functions. Each local objective function and constrained set cannot be shared with others. A primal-dual stochastic algorithm is presented to address the distributed convex optimization problems, where each node updates its state by resorting to unbiased stochastic averaging gradients and projects on its private constrained set. At each iteration, for each node the gradient of one local instantaneous function selected randomly is evaluated and the average of the most recent stochastic gradients is used to approximate the true local gradient. In the constrained case, we show that with strong-convexity of the local instantaneous function and Lipschitz continuity of its gradient, the algorithm converges to the global optimization solution almost surely. In the unconstrained case, an explicit linear convergence rate of the algorithm is provided. Numerical experiments are presented to demonstrate correctness of the theoretical results.     

Stability analysis for a class of switched systems with state constraints via time-varying Lyapunov functions

This article investigates the stability analysis for a class of continuous-time switched systems with state constraints under pre-specified dwell time switchings. The state variables of the studied system are constrained to a unit closed hypercube. Firstly, based on the definition of set coverage, the system state under saturation is confined to a convex polyhedron and the saturation problem is converted into convex hull. Then, sufficient conditions are derived by introducing a class of multiple time-varying Lyapunov functions in the framework of pre-specified dwell time switchings. Such a dwell time is an arbitrary pre-specified constant which is independent of any other parameters. In addition, the proposed Lyapunov functions can efficiently eliminate the “jump” phenomena of adjacent Lyapunov functions at switching instants. The feature of this paper is that the definition of set coverage is utilized to replace the restriction on the row diagonally dominant matrices with negative diagonal elements to analyze stability. The other feature of the constructed time-varying Lyapunov functions is that there are two time-varying functions. One of the two time-varying functions contains the jump rate, which will present a certain degree of freedom in designing the dwell time switching signal. An iterative linear matrix inequality (LMI) algorithm is presented to verify the sufficient conditions. Finally, two examples are presented to show the validity of the method.     

An event-triggering algorithm for decentralized stochastic optimization over networks

In this paper, we study the problem of decentralized optimization to minimize a finite sum of local convex cost functions over an undirected network. Compared with the existing works, we focus on improving the communication efficiency of the stochastic gradient tracking method and propose an effective event-triggering decentralized stochastic gradient tracking algorithm, namely, ET-DSGT. ET-DSGT utilizes the event-triggering mechanism in which each agent only broadcasts its estimators at the event time to effectively avoid real-time communication, thus improving communication efficiency. In addition, we present a theoretical analysis to show that ET-DSGT with a decaying step-size can converge to the exact global minimum. Moreover, we show that for each agent, the time interval between two successive triggering times is greater than the iteration interval under certain conditions. Finally, we provide several simulations to demonstrate the effectiveness of ET-DSGT.     

Decentralized fault tolerant model predictive control of discrete-time interconnected nonlinear systems

This paper presents a novel approach to address the decentralized fault tolerant model predictive control of discrete-time interconnected nonlinear systems. The overall system is composed of a number of discrete-time interconnected nonlinear subsystems at the presence of multiple faults occurring at unknown time-instants. In order to deal with the unknown interconnection effects and changes in model dynamics due to multiple faults, both passive and active fault tolerant control design are considered. In the Active fault tolerant case an online approximation algorithm is applied to estimate the unknown interconnection effects and changes in model dynamics due to multiple faults. Besides, the decentralized control strategy is implemented for each subsystem with the model predictive control algorithm subject to some constraints. It is showed that the proposed method guarantees input-to-state stability characterization for both local subsystems and the global system under some predetermined assumptions. The simulation results are exploited to illustrate the applicability of the proposed method.     

Decentralized guaranteed cost stabilization of time-delay large-scale systems based on reduced-order observers

This paper addresses the problem of decentralized guaranteed cost stabilization (DGCS) of large-scale systems with delays both in the isolated subsystems and interconnections based on reduced-order observers. Sufficient conditions for the existence of delay-independent decentralized guaranteed cost controller (DGCC) are given in terms of linear matrix inequalities (LMIs). Furthermore, a convex optimization problem with LMIs constraints is formulated to design the optimal DGCC which minimizes the guaranteed cost of the closed-loop large-scale systems. Finally, a simulation is performed to show the effectiveness of the proposed control scheme.     

An adaptive penalty-like continuous-time algorithm to constrained distributed convex optimization

This paper considers a nonsmooth constrained distributed convex optimization over multi-agent systems. Each agent in the multi-agent system only has access to the information of its objective function and constraint, and cooperatively minimizes the global objective function, which is composed of the sum of local objective functions. A novel continuous-time algorithm is proposed to solve the distributed optimization problem and effectively characterize the appropriate gain of the penalty function. It should be noted that the proposed algorithm is based on an adaptive strategy to avoid introducing the primal-dual variables and estimating the related exact penalty parameters. Additional, it is demonstrated that the state solution of the proposed algorithm achieves consensus and converges to an optimal solution of the optimization problem. Finally, numerical simulations are given and the proposed algorithm is applied to solve the optimal placement problem and energy consumption problem.     

Efficient iterative method for SOAV minimization problem with linear equality and box constraints and its linear convergence

This study proposes an efficient algorithm for the sum-of-absolute-values (SOAV) minimization problem with linear equality and box constraints by exploiting alternating direction method of multipliers (ADMM). In the iteration of ADMM, efficient algorithms for the calculations of proximal points, which are the solutions of sub-problems and have great effects on the computation efficiency, are employed. By focusing on the dynamical structure of the iteration, the linear convergence of the proposed algorithm is proven. Furthermore, a practical application for mechanical system control with discrete-valued control illustrates the advantages of the proposed methods.     

极大极小问题的模松弛拟强次可行方向算法

结合模松弛SQP算法和强次可行方向算法思想,给出了初始点任意选取的新的拟强次可行方向算法。每步迭代,只需求解一个总有最优解的二次规划子问题来产生主搜索方向,引入一种新的非单调曲线搜索来产生步长,在较弱的条件下,可以得到算法的全局收敛性。     

On strict Lyapunov functions for some non-homogeneous super-twisting algorithms

In this paper strict, non-smooth Lyapunov functions for some non-homogeneous versions of the super-twisting algorithm are proposed. Convergence under the action of bounded perturbations for two basic forms of non-homogeneous algorithms will be studied by means of the Lyapunov functions. Since the homogeneity property cannot be used directly to prove stability of the algorithms, the availability of a Lyapunov function is of great importance for analysis and design in these cases. Moreover, exponential or finite-time and local or global stability are required to be established, since they are not derived from the homogeneity.     

Robust exponential stability for uncertain time-varying delay systems with delay dependence

This paper investigates the exponential stability problem for uncertain time-varying delay systems. Based on the Lyapunov-Krasovskii functional method, delay-dependent stability criteria have been derived in terms of a matrix inequality (LMI) which can be easily solved using efficient convex optimization algorithms. These results are shown to be less conservative than those reported in the literature. Four numerical examples are proposed to illustrate the effectiveness of our results.     

基于求解TSP问题的ACA-GA-PSO算法

为了有效求解TSP问题,提出一种融合蚁群算法、遗传算法、粒子群优化算法思想的混合算法。该算法基于最大-最小蚁群系统框架,在选择下一个城市时采用局部搜索策略避免陷入局部最优,在每次循环结束时用演化交叉策略优化得到的全局最短路径,从而提高求解TSP问题的求解精度及收敛速度。TSPLIB中不同规模的TSP问题的仿真实验结果表明了该算法的有效性与可行性。     

Stability analysis of stochastic fuzzy Markovian jumping neural networks with leakage delay under impulsive perturbations

This paper investigates the global asymptotic stability of stochastic fuzzy Markovian jumping neural networks with mixed delays under impulsive perturbations in mean square. The mixed delays include constant delay in the leakage term (i.e., “leakage delay”), time-varying delay and continuously distributed delay. By using the Lyapunov functional method, reciprocal convex approach, linear convex combination technique, Jensen integral inequality and the free-weight matrix method, several novel sufficient conditions are derived to ensure the global asymptotic stability of the equilibrium point of the considered networks in mean square. The proposed results, which do not require the differentiability and monotonicity of the activation functions, can be easily checked via Matlab software. Finally, two numerical examples are given to demonstrate the effectiveness and less conservativeness of our theoretical results over existing literature.     

组合同伦法求一般非线性规划问题

文章把含有等式约束和不等式约束的一般非线性规划问题转化为只有不等式约束的非线性规划问题,然后构造一个新的同伦方程,与牛顿法结合得到一个新的同伦算法,在变形锥条件下,证明了算法的全局线性收敛性。     

Barrier Lyapunov function-based decentralized adaptive neural control for uncertain high-order stochastic nonlinear interconnected systems with output constraints

Decentralized adaptive neural backstepping control scheme is developed for uncertain high-order stochastic nonlinear systems with unknown interconnected nonlinearity and output constraints. For the control of high-order nonlinear interconnected systems, it is assumed that nonlinear system functions are unknown. It is for the first time to control stochastic nonlinear high-order systems with output constraints. Firstly, by constructing barrier Lyapunov functions, output constraints are handled. Secondly, at each recursive step, only one adaptive parameter is updated to overcome over-parameterization problems, and RBF neural networks are used to identify unknown nonlinear functions so that the difficulties caused by completely unknown system functions and stochastic disturbances are tackled. Finally, based on the Lyapunov stability method, the decentralized adaptive control scheme via neural networks approximator is proposed, ultimately reducing the number of learning parameters. It is shown that the designed controller can guarantee all the signals of the resulting closed-loop system to be semi-globally uniformly ultimately bounded (SGUUB), and the tracking errors for each subsystem are driven to a small neighborhood of zero. The simulation studies are performed to verify the effectiveness of the proposed control strategy.     

Correntropy based model predictive controller with multi-constraints for robust path trajectory tracking of self-driving vehicle

Self-driving vehicles must be equipped with path tracking capability to enable automatic and accurate identification of the reference path. Model Predictive Controller (MPC) is an optimal control method that has received considerable attention for path tracking, attributed to its ability to handle control problems with multiple constraints. However, if the data acquired for determining the reference path is contaminated by non-Gaussian noise and outliers, the tracking performance of MPC would degrades significantly. To this end, Correntropy-based MPC (CMPC) is proposed in this paper to address the issue. Different from the conventional MPC model, the objective of CMPC is constructed using the robust metric Maximum Correntropy Criterion (MCC) to transform the optimization problem of MPC to a non-concave problem with multiple constraints, which is then solved by the Block Coordinate Update (BCU) framework. To find the solution efficiently, the linear inequality constraints of CMPC are relaxed as a penalty term. Furthermore, an iterative algorithm based on Fenchel Conjugate (FC) and the BCU framework is proposed to solve the relaxed optimization problem. It is shown that both objective sequential convergence and iterate sequence convergence are satisfied by the proposed algorithm. Simulation results generated by CarSim show that the proposed CMPC has better performance than conventional MPC in path tracking when noise and outliers exist.     

基于协同进化遗传算法的主题信息采集研究

利用改进的协同进化遗传算法,使全局搜索与局部搜索、全局收敛性与收敛速度有机地统一起来,并将之应用到主题信息采集的研究中,实例结果表明具有较好的分类预测能力.