For formation control of multiple vehicle, the typical method is feedback linearization, wherein followers track leader within a constant distance. Uncertainties and noises arising from varying external environments may easily lead to tracking errors, however. In this paper, leader-follower error model built an adaptive controller designed address uncertain relative distance using dynamic estimation Further, robust developed tackle inevitable white noise in follower velocity detection....

The performance of the induction machine driving system with indirect vector control can easily be influenced by effects parameter mismatch and eddy current, which result that magnetizing component stator current can't align rotor flux vector, especially in range high speed weakening. In order to achieve accurate orientation, a novel strategy based on voltage controller is proposed this paper. relation between phase accuracy field orientation analyzed used eliminate angle d-axis due...

In this paper, we analyze the convergence properties of a stochastic augmented Lagrangian method for solving convex programming problems with inequality constraints.Approximation models are constructed from observations real objective and constraint functions.Based on relations between solutions primal problem dual problem, it is proved that algorithm perspective problem.Without assumptions how these random generated, when estimates merely sufficiently accurate to functions high enough, but...

In this paper, the edge-based and node-based adaptive algorithms are established, respectively, to solve distribution convex optimization problem. The based on multiagent systems with general linear dynamics; each agent uses only local information cooperatively reaches minimizer. Compared existing results, a damping term in law is introduced for algorithms, which makes more robust. Under some sufficient conditions, all agents asymptotically converge consensus value minimizes cost function....

This paper focuses on a distributed coupled constrained convex optimization problem over directed unblanced and time-varying multi-agent networks, where the global objective function is sum of all agents' private local functions, decisions agents are subject to equality inequality constraints compact subset. In each agent exchanges information with other neighboring agents. Finally, reach consensus decisions, meanwhile achieve goal minimizing under given constraint conditions. We first...

This paper focuses on a distributed coupled constrained convex optimization problem over directed unbalanced and time-varying multi-agent networks, where the global objective function is sum of all agents' private local functions, decisions agents are subject to equality inequality constraints compact subset. In each agent exchanges information with other neighboring agents. Finally, reach consensus decisions, meanwhile achieving goal minimizing under given constraint conditions. For purpose...

This paper focuses on a class of inclusion problems maximal monotone operators in multi-agent network, where each agent is characterized by an operator that not available to any other agents, but the agents can cooperate exchanging information with their neighbors according given communication topology. All aim at finding common decision vector solution sum agents' operators. motivated distributed convex optimization coupled constraints. In this paper, we propose proximal point method...

In this paper, a decentralized proximal method of multipliers (DPMM) is proposed to solve constrained convex optimization problems over multi-agent networks, where the local objective each agent general closed function, and constraints are coupled equalities inequalities. This algorithm strategically integrates dual decomposition point algorithm. One advantage DPMM that subproblems can be solved inexactly in parallel by agents at iteration, which relaxes restriction requiring exact solutions...

To solve the individualized data collection requirement of different users, a system based on SaaS architecture is constructed in paper. The implements primary services and core business for supporting collection. By thread pool scheduling cloud storage technology, are completed efficiently. this way supplied as server can be shared more widely. Users customize their own flexibly demand efficient storage. It greatly reduces users' research development costs improves efficiency.

In this paper, we consider online convex optimization (OCO) with time-varying loss and constraint functions. Specifically, the decision maker chooses sequential decisions based only on past information, meantime functions are revealed over time. We first develop a class of model-based augmented Lagrangian methods (MALM) for functional constrained OCO (without feedback delay). Under standard assumptions, establish sublinear regret violation MALM. Furthermore, extend MALM to deal delayed...