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Inspired by potential application in power systems and a fully distributed primal-dual method, we investigate how a novel distributed optimization algorithm can be used to solve the dispath response problem in power systems with a distributed manner, whose main target is to minimize the generation cost of the whole network. The problem we study here can be formulated as a kind of optimization problem,...
Correntropy induced metric (CIM) criterion has been extensively studied for measuring the sparsity property of the in-nature sparse signals. In this paper, a CIM constrained l2–lp (CIM-L2LP) adaptive filtering algorithm is proposed and its convergence analysis is given in detail. By using a CIM penalty, the CIM-L2LP algorithm achieves improved convergence speed while it maintains a lower steady state...
This paper considers the depth control problem of autonomous underwater vehicles (AUVs) in discrete time. A neural-network-based deterministic policy gradient (NNDPG) controller is proposed by combining the deterministic policy gradient theorem with neural networks. Two networks, evaluation network and policy network, are designed to respectively approximate the long-term cost function and policy...
This paper provides a review of some main works and research progress in distributed optimal consensus problems of multi-agent systems. Classical and common methods in this area are summarized, such as Riccati design, adaptive dynamic programming and inverse optimal control. This paper detailedly addresses the design schemes of optimal consensus protocols by reviewing several typical literatures....
This paper deals with demand adjustment problem of each consumer having appliances by an aggregator based on the optimal pricing problem in a day-ahead electricity market. In this paper, we model consumer, generator, aggregator and design a market mechanism and they act to maximize their own profit based on the power price and decide the electricity supply and demand. The dual decomposition is applied...
The distributed optimal power flow problem is addressed. No assumptions on the problem cost function, and network topology are needed to solve the optimization problem. A distributed particle swarm optimization algorithm is proposed, based on Deb's rule to handle hard constraints. Moreover, the approach enables to treat a class of distributed optimization problems in which the agents share a common...
In this paper we consider an algebraic Riccati equation arising from discrete linear quadratic optimal control problem. We discuss the solution of the discrete algebraic Riccati equation using modification of Newton's method. This modification consists of Exact Line search and Double Newton step. This method is applied to find the maximal symmetric solution of a discrete algebraic Riccati equation...
A sparsity-aware proportionate normalized maximum correntropy criterion (PNMCC) algorithm with lp-norm penalty, which is named as lp-norm constraint PNMCC (LP-PNMCC), is proposed and its crucial parameters, convergence speed rate and steady-state performance are discussed via estimating a typical sparse multipath channel and an typical echo channel. The LP-PNMCC algorithm is realized by integrating...
In this paper, we propose sparsity-aware data-selective adaptive filtering algorithms with adjustable penalties. Prior work incorporates a penalty function into the cost function used in the optimization that originates the algorithms to improve their performance by exploiting sparsity. However, the strength of the penalty function is controlled by a scalar that is often a fixed parameter. In contrast...
Economic Dispatch (ED) is an important aspect in any power system. The conventional methods for solving ED include Lambda-Iterative, Newton-Raphson, Quadratic programming (QP), etc. However, conventional method cannot solved non quadratic function. The input-output characteristics of a generator produced highly non-linear leading so its challenging non-convex and non-smooth optimization problem. In...
This paper investigates the average infinite horizon optimal control problem for Boolean control networks (BCNs). Based on the semi-tensor product of matrices and Jordan decomposition technique, an optimality equation for the average infinite horizon problem of BCNs is presented. By resorting to Laurent series expression, a policy iteration algorithm, which can find the optimal solution in finite...
Recently, rapidly-exploring random trees(RRT) is widely used in path planning for its nature of single-query. The optimized algorithm RRT∗ extends RRT algorithm to find the optimal path, but it needs to search every state from the initial state to the global scope asymptotically. This method is not only inefficient, but also contrary to the single-query of RRT. In this paper, a new variant of RRT∗-Gb...
This paper proposes an “out-in degree” Laplacian matrix to dispose the distributed optimization problem for both the continuous-time and discrete-time multiagent systems with the first-order dynamics over a general strongly connected digraph. By making use of the out-degree and in-degree Laplacian matrices of the directed graph, we establish the parameter matrix which possesses some properties similar...
Many computer vision problems are formulated as the optimization of a cost function. This approach faces two main challenges: (1) designing a cost function with a local optimum at an acceptable solution, and (2) developing an efficient numerical method to search for one (or multiple) of these local optima. While designing such functions is feasible in the noiseless case, the stability and location...
This paper proposes a Newton extremum seeking algorithm based accurate global task coordinate frame (Newton-AGTCF) for precision contouring motion control of biaxial systems under complicated free-form contouring tasks. Specifically, a cost function is defined based on the reference contour and the current position. The point on the reference contour, where the minimal value of the cost function can...
In this paper, we propose a new consensus protocol in the form of distributed model predictive control (DMPC) in order to deal with the consensus problem of first-order discrete-time multi-agent systems. Equivalent form of general numerical solution of DMPC is presented by solving optimization problem, and necessary and sufficient convergence conditions of our proposed DMPC are obtained by using the...
The present paper develops a distributed protocol solving the distributed optimization problem for multi-agent systems with the discrete-time dynamics under Markovian switching topologies. Both the completely known probabilities and partially unknown probabilities in the transition matrices are taken into account. Through the proper coordination of transformation, the optimization under consideration...
In the distributed optimization, multiple agents aim to minimize the average of all local cost functions corresponding to one decision variable. Recently, the resilient algorithms for distributed optimization against attacks have received some attention, where it is assumed that the maximum number of tolerable attacks is strictly limited by the network connectivity. To relax this assumption, in this...
In this paper, we investigate a distributed Nash equilibrium seeking problem for a class of aggregative games that the strategic interaction is characterized by a sum of nonlinear mapping of heterogeneous local decisions. We consider non-quadratic local cost functions and constrained strategy sets. We propose a novel continuous-time distributed algorithm for equilibrium seeking based on dynamic average...
In this paper, we attack the estimation problem in Kalman filtering when the measurements are contaminated by outliers. We employ the Laplace distribution to model the underlying non-Gaussian measurement process. The maximum posterior estimation is solved by the majorization minimization (MM) approach. This yields an MM based robust filter, where the intractable ℓ1 norm problem is converted into an...
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