One of the issues of reliable performance in the power grid is the existence of electromechanical oscillations between interconnected generators. The number of generators participating in each electromechanical oscillation mode and the frequency oscillation depends on the structure and function of the power grid. In this paper, to improve the transient nature of the network and damping electromechanical fluctuations, a decentralized robust adaptive control method based on dynamic programming has been used to design a stabilizing power system and a complementary static var compensator (SVC) controller. By applying a single line to ground fault in the network, the robustness of the designed control systems is demonstrated. Also, the simulation results of the method used in this paper are compared with controllers whose parameters are adjusted using the PSO algorithm. The simulation results show the superiority of the decentralized robust adaptive control method based on dynamic programming for the stabilizing design of the power system and the complementary SVC controller. The performance of the control method is tested using the IEEE 16-machine, 68-bus, 5-area is verified with time domain simulation.

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.

Interactions between input and output variables are a prevalent challenge in the design of multi-loop controllers for multivariable processes, and they can be a major stumbling block to obtaining good overall performance of a multi loop control system. The deconstructed dynamic interaction analysis is proposed to solve this limitation by decomposing the multi loop control system into a series of n independent SISO systems, each with its own PID controller. The multivariable decoupler and multi loop PID controller is applied to Two Tank Conical Interacting System (TTCIS). This TTCIS is chosen as benchmark problem used by many researchers. Firstly, the Mathematical modelling of TTCIS is derived using First principal model. The non-linear system is linearized using Jacobian matrix and decomposed into multiple SISO systems. The controller design for the process is then obtained, and an RGA matrix is constructed to minimise the interaction effects. To demonstrate the efficiency of the suggested strategy, simulation results using TTCIS are provided.

This paper presents a robust decentralized model predictive control scheme for a class of discrete-time interconnected systems subject to state and input constraints. Each subsystem is composed of a nominal LTI part and an additive timevarying perturbation function which presents the interconnections and is generally uncertain and nonlinear, but it satisfies a quadratic bound. Using the dual-mode MPC stability theory and Lyapunov theory for discrete-time systems, a sufficient condition is constructed for synthesizing the decentralized MPC’ s stabilizing components; i. e. the local terminal cost function and the corresponding terminal set. To guarantee robust asymptotic stability, sufficient conditions for designing MPC stabilizing components are characterized in the form of an LMI optimization problem. The proposed control approach is applied to a system composed of five coupled inverted pendulums, which is a typical interconnected system, in a decentralized fashion. Simulation results show that the proposed robust MPC scheme is quite effective and has a remarkable performance.

This paper presents a new load frequency control (LFC) design in a multi area power system by using local observers. Firstly, sliding mode observers with unknown inputs are designed for each area to estimate the state variables locally. In this stage interconnections and load variations are assumed as unknown inputs. Then, local state feedback and output integral are used to attenuate the effect of load variations in each area. Analysis and simulation results for a three-area interconnected power system show improvements on closed loop performance in comparisons with other existing methods.

Multivariable liquid level control is necessary in process industries to ensure quality of the product and safety of the equipment. However, the significant problems of the control system include excessive time consumption and percentage overshoot, which result from ineffective performance of the tuning methods of the PID controllers used for the system. In this paper, fuzzy logic was used to tune the PID parameters to control a four-coupled-tank system in which liquid level in tanks 1 and 2 were controlled. Mass Balance equation was employed to generate the transfer function matrix for the system, while a Fuzzy Inference System (FIS) file is created and embedded in fuzzy logic controller blocks, making tuning rules for the PID. Matlab R2009b simulation of the system model shows that the rise time (RT), settling time (ST), peak value (PV) and percentage overshoot (PO) for the developed DF-PID controller were 1. 48 s, 4. 75 s, 15 cm and 0% respectively for tank-1; and 0. 86 s, 2. 62 s, 10 cm and 0% respectively for tank-2, which are the smallest and best values when compared with other PID tuning methods namely: Ziegler-Nichols, Cohen-Coon and Chien-Hrones-Reswick PID tuning methods.