Non-cooperative intelligent CONTROL agents (ICAs) with dedicated cost functions, can lead the system to poor performance and in some cases, closed-loop instability. A robust solution to this challenge is to place the ICAs at the feedback Nash equilibrium point (FNEP) of the differential game between them. This paper introduces the designation of a robust decentralized infinite horizon LQR CONTROL system based on the FNEP for a LINEAR time-invariant system. For this purpose, two CONTROL strategies are defined. The first one is a centralized infinite horizon LQR (CIHLQR) problem (i.e. a supervisory problem), and the second one is a decentralized CONTROL problem (i.e. an infinite horizon LINEAR-quadratic differential game). Then, while examining the optimal solution of each of the above strategies on the performance of the other, the necessary and sufficient conditions for the equivalence of the two problems are presented. In the absence of the conditions, by using the least-squares error criterion, an approximated CIHLQR CONTROLler is presented. It is shown that the theorems could be extended from a two-agent CONTROL system to a multi-agent system. Finally, the results are evaluated using the simulation results of a Two-Area non-reheat power system.

In this paper LINEAR Quadratic CONTROL (LQR) for 2-D SYSTEMS is considered .By transferring a 2-D system to 1-D Wave Advanced Model (WAM) the theory available for 1-D LQR problem will be extended to 2-D SYSTEMS. Then the final results will be held in the initial 2-D system's format. In this case some numerical difficulties will be discussed. As a secondary matter, results will be used for large scale 1-D SYSTEMS as well.

In this paper, the concept of stability for fuzzy CONTROL SYSTEMS is discussed from a mathematical point of view. Our main objective is to investigate the stability of fuzzy LINEAR SYSTEMS with bounded input and bounded output. Necessary and sufficient conditions for this kind of stability are presented by a theorem and it is illustrated by solving an example.

In this paper, a sub-optimal CONTROL for a class of uncertain LINEAR switching SYSTEMS is presented that it includes external disturbance, parametric uncertainty and bounded uncertain nonLINEAR term with order 2 or higher due to modeling error. Switching signal and CONTROL input are designed to minimize a given cost function. Dynamic programming has been efficiently applied to certain switching SYSTEMS as an optimal CONTROL strategy. There are difficulties to apply dynamic programming to uncertainty switching SYSTEMS. To overcome the problems, this paper presents an appropriate method. The uncertainties of uncertain LINEAR switching SYSTEMS are compensated by robust time-delay CONTROLler. Then Switching signal and CONTROL input are design by using dynamic programming. As one practical example, we apply the proposed CONTROL approach to CONTROL of Mass-spring-damper system. The Stability analysis and simulation verify effectiveness of the proposed CONTROL approach.

This study aims to report on the application of LINEAR Quadratic Integral (LQI) based global maximum power point tracker (GMPPT) method for transferring the available maximum power from photovoltaic (PV) SYSTEMS to load in unshaded and shaded conditions. For the maximum power transmission under varying the environmental conditions and partially shaded conditions, MPPT technologies are utilized in PV SYSTEMS. For the improvement of functioning MPPT, a new two-level CONTROL structure which decreases difficulty in the CONTROL process and efficiently deals with the uncertainties in the PV SYSTEMS is introduced. In the proposed approach, the reference voltage at the global maximum power point (GMPP) is estimated by a new scanning algorithm. The difference between the reference voltage and the voltage of the PV array is then used by LQI CONTROLler to generate the duty cycle for a boost converter. The design process of the proposed approach is explained as step by step. The benefits of the approach are quicker tracking capability, transferring maximum deliverable power and simple implementation. To verify the proposed method, several irradiation profiles that create several peaks in the P-V curve are used. The simulation results show that the proposed method causes PV SYSTEMS to track the GMPP immediately so that no oscillation around the GMPP is observed. Therefore, maximum efficiency can be derived from the PV system.

This paper deals with a globally convergent adaptive and sliding mode CONTROL of a cart-pole inverted pendulum for trajectory tracking in the presence of a bounded measurement noise and parameter uncertainty. Two kinds of CONTROLlers have been used for evaluation of tracking error in presence of a bounded noise; as a result, we want to compare that at what time we can see the convergence of tracking error and which CONTROLler can perform better? Simulation results on a cart-pole inverted pendulum are shown for trajectory tracking in presence of impulse disturbance.

In this paper, we study LINEAR fuzzy time-variant optimal CONTROL SYSTEMS using the generalized differentiability concept and we present the general form of optimal CONTROLs and states. Some examples are provided to illustrate our results.

The problem of solving optimal CONTROL of Singular problems in the classic method has a complexity that is solved by approximation of the equations in the problem with orthogonal bases instead of solving the dynamic equation system of a set of static problems. In return for a more relaxed solution, it will face some errors in the computation. however, it has an appropriate precision. Legendre and Fourier series are presented using the specifications of the Fourier transform of Legendre and Fourier series. In this algorithm, the state variables, and the state-derivative variables and the CONTROL vector are extended by the orthogonal basis of Legendre and Fourier series with unknown coefficients. in order to compute optimal CONTROL vector and optimal path of LINEAR Singular SYSTEMS with quadratic cost function, we are introduced by using the properties of orthogonal functions introduced by the coefficients and. using the proposed method, the system dynamics are converted into algebraic equations and the problem of dynamic optimization of dynamic space has been mapped to static space optimization problem with quadratic cost function and LINEAR constraints. First, it is used to solve the problem using an orthogonal basis of raw material and then the problem solving with orthogonal basis of Fourier series is repeated. Finally, the application and effectiveness of the proposed method are presented.