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Öğe DISTURBANCE REJECTION FOPID CONTROL OF ROTOR BY MULTI-OBJECTIVE BB-BC OPTIMIZATION ALGORITHM(Amer Soc Mechanical Engineers, 2017) Ates, Abdullah; Alagoz, Baris Baykant; Yeroglu, Celaleddin; Yuan, Jie; Chen, YangQuanThis paper presents a FOPID tuning method for disturbance reject control by using multi-objective BB-BC optimization algorithm. Proposed method allows multi-objective optimization of set-point performance and disturbance rejection performances of FOPID control system. The objective function to be minimized is composed of the weighted sum of MSE for set-point performance and RDR for disturbance rejection improvement. The proposed optimization performs maximization of RDR and minimization of MSE and it can deal with the tradeoff between RDR performance and step-point performance. Application of the method is shown for auto tuning of FOPID controller that is employed for control of TRMS model. We observed that low-frequency RDR indices can be used to improve disturbance rejection performance in multi-objective controller tuning problems. Particularly, for flight control application, disturbance reject control is very substantial to robust performance of propulsion systems.Öğe FRACTIONAL ORDER CHAOTIC MODEL BASED ENHANCED EQUILIBRIUM OPTIMIZATION ALGORITHM FOR CONTROLLER DESIGN OF 3 DOF HOVER FLIGHT SYSTEM(Amer Soc Mechanical Engineers, 2021) Ates, Abdullah; Chen, YangQuanIn this study, the K feedback gain vector parameters that are used for the control of three degree of freedom four rotor quadcopter system (3 DOF Hover) are optimized with the Enhanced Equilibrium Optimization Algorithm ((EO)-O-2). The (EO)-O-2 algorithm is proposed with using parameters obtained from fractional order chaotic oscillator models instead of random variables. The Basic EO algorithm is inspired by volume- mass balance. In EO algorithm, each particle is called a motion that searches a parameter vector space. However, random coefficients derived from uniform distribution are used in the parameters updating process or in the generation of the initial population. The (EO)-O-2 algorithm was proposed by using vectors obtained from fractional order chaotic oscillators instead of stochastic coefficients in the basic Equilibrium optimization algorithm. Genesio Tesi, Rossler, Lotka Volterra fractional-order chaotic oscillator models were used in the (EO)-O-2 algorithm to optimize K feedback gain vector of 3 DOF Hover. The order and initial conditions the fractional chaotic oscillator models were experimentally adjusted for the control of 3 DOF problem. Thus, suitable fractional-order chaotic models for the problem were obtained. The (EO)-O-2 algorithm results are compared with the Stochastic Multi Parameter Optimization (SMDO) and Discreet Stochastic Optimization (DSO) algorithms for the system's pitch, roll and yaw angles.Öğe FRACTIONAL ORDER FILTER DISCRETIZATION WITH MARINE PREDATORS ALGORITHM(Amer Soc Mechanical Engineers, 2021) Ates, Abdullah; Chen, YangQuanIn this study, discrete time models of continuous time fractional order filters are obtained by using the Marine Predators Algorithm (MPA). Marine Predators optimization algorithm is a population-based heuristic method. This method is inspired by the hunting behavior of marine predators. The algorithm works on three basic phases. These phases occur according to the difference or equality of the velocity of the prey and the predator. As it is known, uniform distribution is generally used in stochastic based optimization algorithms. However, in the MPA method, Brownian and Levy distributions are also used as well as uniform distribution. First, continuous time frequency responses of fractional order filters are generated. Then, fourth order discrete time filters are designed that can give similar responses with generated continues time filter frequency responses. Ten parameters were optimized for the design of fourth order discrete time filters numerator and denominator. The Marine Predators method's results are compared with the results of the Fractional order Darwinian Particle Swarm Optimization (FODPSO) algorithm, from which discrete time filters are obtained for two fractional order continuous time filter models. In this way, it has been shown comparatively that the Marine Predators Algorithm can be used in real engineering problems and can do filter discretization better.Öğe PID2018 Benchmark Challenge: learning feedforward control(Elsevier, 2018) Zhao, Yang; Dehghan, Sina; Ates, Abdullah; Yuan, Jie; Zhou, Fengyu; Li, Yan; Chen, YangQuanThe design and application of learning feedforward controllers (LFFC) for the one staged refrigeration cycle model described in the PID2018 Benchmark Challenge is presented, and its effectiveness is evaluated. The control system consists of two components: 1) a preset PID component and 2) a learning feedforward component which is a function approximator that is adapted on the basis of the feedback signal. A B-spline network based LFFC and a low-pass filter based LFFC are designed to track the desired outlet temperature of evaporator secondary flux and the superheating degree of refrigerant at evaporator outlet. Encouraging simulation results are included. Qualitative and quantitative comparison results evaluations show that, with little effort, a high-performance control system can be obtained with this approach. Our initial simple attempt of low-pass filter based LFFC and B-spline network based LFFC give J=0.4902 and J=0.6536 relative to the decentralized PID controller, respectively. Besides, the initial attempt of a combination controller of our optimized PI controller and low-pass filter LFFC gives J=0.6947 relative to the multi-variable PID controller. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.Öğe PID2018 Benchmark Challenge: Model Predictive Control With Conditional Integral Control Using A General Purpose Optimal Control Problem Solver - RIOTS(Elsevier, 2018) Dehghan, Sina; Zhao, Tiebiao; Zhao, Yang; Yuan, Jie; Ates, Abdullah; Chen, YangQuanThis paper presents a multi-variable Model Predictive Control (MPC) based controller for the one-staged refrigeration cycle model described in the PID2018 Benchmark Challenge. This model represents a two-input, two-output system with strong nonlinearities and high coupling between its variables. A general purpose optimal control problem (OCP) solver Matlab toolbox called RIOTS is used as the OCP solver for the proposed MPC scheme which allows for straightforward implementation of the method and for solving a wide range of constrained linear and nonlinear optimal control problems. A conditional integral (CI) compensator is embedded in the controller to compensate for the small steady state errors. This method shows significant improvements in performance compared to both discrete decentralized control (C1) and multi-variable PID controller (C2) originally given in PID2018 Benchmark Challenge as a baseline. Our solution is introduced in detail in this paper and our final results using the overall relative index, J, are 0.2 over Cl and 0.3 over C2, respectively. In other words, we achieved 80% improvement over Cl and 70% improvement over C2. We expect to achieve further improvements when some optimized searching efforts are used for MPC and CI parameter tuning. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.Öğe PID2018 Benchmark Challenge: Model-based Feedforward Compensator with A Conditional Integrator(Elsevier, 2018) Yuan, Jie; Ates, Abdullah; Dehghan, Sina; Zhao, Yang; Fei, Shumin; Chen, YangQuanSince proportional-integral-derivative (PID) controllers absolutely dominate the control engineering, numbers of different control structures and theories have been developed to enhance the efficiency of PID controllers. Thus, it is essential and inspiring to operate different PID control strategies to the PID2018 Benchmark Challenge. In this paper, a novel control strategy is designed for this refrigeration system, where a feedforward compensator and a conditional integrator are utilized to compensate the disturbances and remove the steady-state error in the benchmark problem, respectively. The simulation results given in the benchmark problem show the straightforward effectiveness of the proposed control structure compared with the existing control methods. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.Öğe PID2018 Benchmark Challenge: Multi-Objective Stochastic Optimization Algorithm(Elsevier, 2018) Ates, Abdullah; Yuan, Jie; Dehghan, Sina; Zhao, Yang; Yeroglu, Celaleddin; Chen, YangQuanThis paper presents a multi-objective stochastic optimization method for tuning of the controller parameters of Refrigeration Systems based on Vapour Compression. Stochastic Multi Parameter Divergence Optimization (SMDO) algorithm is modified for minimization of the Multi Objective function for optimization process. System control performance is improved by tuning of the PI controller parameters according to discrete time model of the refrigeration system with multi objective function by adding conditional integral structure that is preferred to reduce the steady state error of the system. Simulations are compared with existing results via many graphical and numerical solutions. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
