– The problem of designing of analog network for a minimal computer time has been formulated as the functional minimization problem of the control theory. The design process in this case is formulated as the controllable dynamic system. The optimal sequence of the control vector switch points was determined as a principal characteristic of the minimal-time system design algorithm. The conceptio...

In this paper, we present a neural-network (NN) based approach to represent a nonlinear Tagagi-Sugeno (T-S) system. A linear differential inclusion (LDI) state-space representation is utilized to deal with the NN models. Taking advantage of the LDI representation, the stability conditions and controller design are derived for a class of nonlinear structural systems. Moreover, the concept of uti...

A numerical method for solving the H∞ synthesis problem is presented. The problem is posed as an unconstrained, nonsmooth, nonconvex minimization problem. The optimization variables consist solely of the entries of the output feedback matrix. No additional variables, such as Lyapunov variables, need to be introduced. The main part of the optimization procedure uses a line search mechanism where...

In this chapter, we present a framework for online control customization that makes use of finite-horizon, optimal control combined with real-time trajectory generation and optimization. The results are based on a novel formulation of receding-horizon optimal control that replaces the traditional terminal constraints with a control Lyapunov function-based terminal cost. This formulation leads t...

In this paper we use optimization-based methods to design output-feedback controllers for a class of one-dimensional parabolic partial differential equations. The output may be distributed or pointmeasurements. The input may be distributed or boundary actuation. We use Lyapunov operators, duality, and the Luenberger observer framework to reformulate the synthesis problem as a convex optimizatio...

This paper describes a hybrid steepest descent method to decrease over time any given convex cost function while keeping the optimization variables into any given convex set. The method takes advantage of properties of hybrid systems to avoid the computation of projections or of a dual optimum. The convergence to a global optimum is analyzed using Lyapunov stability arguments. A discretized imp...

In this paper, the problem of stability of uncertain cellular neural networks with discrete and distribute time-varying delays is considered. Based on the Lyapunov function method and convex optimization approach, a new delay-dependent stability criterion of the system is derived in terms of LMI (linear matrix inequality). In order to solve effectively the LMI as a convex optimization problem, ...

This study proposes a combination of a fuzzy sliding mode controller (FSMC) with integral-proportion-Derivative switching surface based superconducting magnetic energy storage (SMES) and PID tuned by a multi-objective optimization algorithm to solve the load frequency control in power systems. The goal of design is to improve the dynamic response of power systems after load demand changes. In t...

This paper focuses on the logistics aspect of supply chain management. It proposes a randomized flow management algorithm for a time-varying, random, supply chain network. A constrained stochastic optimization problem that maximizes the profit function in terms of the long-run, time-average of the flows in the supply chain is formulated. The algorithm is distributed and based on queueing theory...