Optimization Techniques In PID Controller On A Nonlinear Electro-Hydraulic Actuator System

The controller is an important component in the nonlinear control system, especially for the system that needs accuracy in position tracking. Electro-Hydraulic Actuator (EHA) system i s a popular nonlinear system that is used by researchers. Proportional- Integral-Derivative (PID) controller is the most popular controller that is normally used in the industry. This i s mainly because of i ts simplicity in the design process. However, there are three constants that need to be assigned in the PID controller, often we called thi s as the parameters s election process or the PID tuning process. In this paper, a comparison s tudy for the selection process of the PID parameters process will be conducted among Ziegler-Nichols tuning method, conventional Particle Swarm Optimization (PSO) technique and Priority-based Fitness Particle Swarm Optimization (PFPSO) technique. PFPSO is one of the improved versions of the conventional PSO technique. The s imulation study wi ll be conducted on a nonlinear Electro-Hydraulic Actuator (EHA) system. A simple robustness test on the PID controller will be evaluated in terms of actuator internal leakage. Results showed that the PID performed better whe n its controller's parameters are selected using PFPSO technique rather than the Ziegler-Nichols method and conventional PSO technique

Application of Group Hunting Search Optimized Cascade PD-Fractional Order PID Controller in Interconnected Thermal Power System

This paper is an endeavor to enhance the performance of the Automatic Generation Control (AGC) by adopting cascade PD-FOPID (Proportional Derivative - Fractional Order PID) controller in a two-area mutually connected thermal power plant with Generation Rate Constraint (GRC). The performance of the cascade PD-FOPID controller is validated by contrasting PID and FOPID controllers implemented in each area as AGC. The basic goal of the design of these controllers is to lessen the area control error (ACE) of corresponding area by conceding the frequency and tie-line power deviation. Group Hunting Search (GHS) algorithm is adopted to explore the gain parameters of the controllers to lessen the objective function (ITAE). A small step load transition of 0.01 p.u. is enforced in area-1 to investigate the controller performance. Cascade PD-FOPID controller optimized by GHS algorithm performs precisely better than PID and FOPID controller in the proposed system. Citation: Nayak, J. R., and Shaw, B. (2018). Application of Group Hunting Search Optimized Cascade PD-Fractional Order PID Controller in Interconnected Thermal Power System. Trends in Renewable Energy, 4, 22-33. DOI: 10.17737/tre.2018.4.3.004

Distributed model predictive control of steam/water loop in large scale ships

In modern steam power plants, the ever-increasing complexity requires great reliability and flexibility of the control system. Hence, in this paper, the feasibility of a distributed model predictive control (DiMPC) strategy with an extended prediction self-adaptive control (EPSAC) framework is studied, in which the multiple controllers allow each sub-loop to have its own requirement flexibility. Meanwhile, the model predictive control can guarantee a good performance for the system with constraints. The performance is compared against a decentralized model predictive control (DeMPC) and a centralized model predictive control (CMPC). In order to improve the computing speed, a multiple objective model predictive control (MOMPC) is proposed. For the stability of the control system, the convergence of the DiMPC is discussed. Simulation tests are performed on the five different sub-loops of steam/water loop. The results indicate that the DiMPC may achieve similar performance as CMPC while outperforming the DeMPC method

Fractional order fuzzy-PID control of a combined cycle power plant using Particle Swarm Optimization algorithm with an improved dynamic parameters selection

The effectiveness of the Particle Swarm Optimization (PSO) algorithm in solving any optimization problem is highly dependent on the right selection of tuning parameters. A better control parameter improves the flexibility and robustness of the algorithm. In this paper, a new PSO algorithm based on dynamic control parameters selection is presented in order to further enhance the algorithm's rate of convergence and the minimization of the fitness function. The powerful Dynamic PSO (DPSO) uses a new mechanism to dynamically select the best performing combinations of acceleration coefficients, inertia weight, and population size. A fractional order fuzzy-PID (fuzzy-FOPID) controller based on the DPSO algorithm is proposed to perform the optimization task of the controller gains and improve the performance of a single-shaft Combined Cycle Power Plant (CCPP). The proposed controller is used in speed control loop to improve the response during frequency drop or change in loading. The performance of the fuzzy-FOPID based DPSO is compared with those of the conventional PSO, Comprehensive Learning PSO (CLPSO), Heterogeneous CLPSO (HCLPSO), Genetic Algorithm (GA), Differential Evolution (DE), and Artificial Bee Colony (ABC) algorithm. The simulation results show the effectiveness and performance of the proposed method for frequency drop or change in loading. (C) 2017 Elsevier B.V. All rights reserved