Predefined-Time Formation Control for Multiagent Systems-Based on Distributed Optimization.

This article presents a solution to the leaderless formation control problem for first-order multiagent systems, which minimizes a global function composed of a sum of local strongly convex functions for each agent under weighted undirected graphs within a predefined time. The proposed distributed optimization process consists of two steps: 1) the controller initially leads each agent to the minimizer of its local function and 2) then guides all agents toward achieving leaderless formation and reaching the global function's minimizer. The proposed scheme requires fewer adjustable parameters than most existing methods in the literature without the need for auxiliary variables or time-variable gains. Additionally, one can consider highly nonlinear multivalued strongly convex cost functions, while the agents do not share the gradients and Hessians. Extensive simulations and comparisons with state-of-the-art algorithms demonstrate the effectiveness of our approach.

Particle swarm optimization for discrete-time inverse optimal control of a doubly fed induction generator.

In this paper, the authors propose a particle swarm optimization (PSO) for a discrete-time inverse optimal control scheme of a doubly fed induction generator (DFIG). For the inverse optimal scheme, a control Lyapunov function (CLF) is proposed to obtain an inverse optimal control law in order to achieve trajectory tracking. A posteriori, it is established that this control law minimizes a meaningful cost function. The CLFs depend on matrix selection in order to achieve the control objectives; this matrix is determined by two mechanisms: initially, fixed parameters are proposed for this matrix by a trial-and-error method and then by using the PSO algorithm. The inverse optimal control scheme is illustrated via simulations for the DFIG, including the comparison between both mechanisms.

Discrete-time neural inverse optimal control for nonlinear systems via passivation.

This paper presents a discrete-time inverse optimal neural controller, which is constituted by combination of two techniques: 1) inverse optimal control to avoid solving the Hamilton-Jacobi-Bellman equation associated with nonlinear system optimal control and 2) on-line neural identification, using a recurrent neural network trained with an extended Kalman filter, in order to build a model of the assumed unknown nonlinear system. The inverse optimal controller is based on passivity theory. The applicability of the proposed approach is illustrated via simulations for an unstable nonlinear system and a planar robot.

Real-time recurrent neural state estimation.

A nonlinear discrete-time neural observer for discrete-time unknown nonlinear systems in presence of external disturbances and parameter uncertainties is presented. It is based on a discrete-time recurrent high-order neural network trained with an extended Kalman-filter based algorithm. This brief includes the stability proof based on the Lyapunov approach. The applicability of the proposed scheme is illustrated by real-time implementation for a three phase induction motor.

Discrete-time adaptive backstepping nonlinear control via high-order neural networks.

This paper deals with adaptive tracking for discrete-time multiple-input-multiple-output (MIMO) nonlinear systems in presence of bounded disturbances. In this paper, a high-order neural network (HONN) structure is used to approximate a control law designed by the backstepping technique, applied to a block strict feedback form (BSFF). This paper also includes the respective stability analysis, on the basis of the Lyapunov approach, for the whole controlled system, including the extended Kalman filter (EKF)-based NN learning algorithm. Applicability of the scheme is illustrated via simulation for a discrete-time nonlinear model of an electric induction motor.