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Design of Digital PID Control Systems Based on Sensitivity Analysis and Genetic Algorithms
Jau-Woei Perng,Shan-Chang Hsieh 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.7
Digital PID controllers design method in a parameter space is proposed in this article. Sensitivity analysisis processed to meet specifications in gain margin and phase margin. The stability boundary is plotted based on theproposed method in this article. The genetic algorithm is used for integral absolute error, integral time-weightedabsolute error, integral square error, and integral time-weighted square error. A design procedure is proposed in thisarticle. The design procedure is applied for a model of a boiler and a model with time delay. Computer simulationresults show that the proposed method is effective.
Design of the PID Controller for Hydro-turbines Based on Optimization Algorithms
Jau-Woei Perng,Yi-Chang Kuo,Kuan-Chung Lu 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.7
In this study, multiple objective particle swarm optimization (MOPSO), genetic algorithm, bees, and reinforcement learning (RL) are used to calculate the rise time (tr), integral square-error, integral of time-multipliedsquared-error, integral absolute error, and integral of time multiplied by absolute error of the system transfer function and then we use a fuzzy algorithm on MOPSO, GA, bees, and RL based on the frequency sensitivity margin of a water turbine governor to optimize the proportional gain (kp) and integral gain (ki) and calculate the relative collapsing frequency response values. The MOPSO algorithm returned the optimal result. The radial basis function (RBF) neural network curve is obtained from the MOPSO algorithm with three variables (i.e., kp, ki, kd = 0.6 and grid frequency deviations values), and finally we identify and predict three variable values near the RBF neural network curve through deep learning. The result of the grid frequency deviation is close to 0, and the gain response time is better for damping the frequency oscillations in different operating conditions.
Dynamical Control for the Parametric Uncertain Cancer Systems
Yi-Horng Lai,Lan-Yuen Guo,Kun-Ching Wang,Jau-Woei Perng 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.9
In this study, we consider a parametric uncertain Lotka–Volterra cancer model including three interacting cell populations of tumor cells, healthy host cells and immune effector cells. The biological parameter (i.e., cell growth rate) is described as a form of the triangular fuzzy number. By using grade mean value conversion, the imprecise fuzzy parameter is translated into the degree of optimism (λ-integral value λ ∈ [0,1]) interval. We derive the sufficient conditions for the existence of the region of asymptotic stability (RAS) in the fuzzy cancer model. The boundary crisis of transient chaos and properties of RAS are investigated under fuzzy environment. We present a dynamical perturbation control to avoid uncontrolled tumor cell growth and prevent healthy cell extinction.