Design of a fractional order PID controller for hydraulic turbine regulating system using chaotic non-dominated sorting genetic algorithm II

Chen, Zhihuan; Yuan, Xiao; Ji, Bin; Wang, Pengtao; Tian, Hao

doi:10.1016/j.enconman.2014.04.052

Cited by 203 publications

(92 citation statements)

References 37 publications

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“…Design of FOPID controller using genetic algorithms (GA) which is a stochastic global search method uses the process of natural evolution. It is one of the methods used for optimization and successfully applied in [47,48]. GA have to be initialized before the algorithm can proceed.…”

Section: Controllers (Gafopid)mentioning

confidence: 99%

Chaos Control in Fractional Order Smart Grid with Adaptive Sliding Mode Control and Genetically Optimized PID Control and Its FPGA Implementation

Karthikeyan

Rajagopal

2017

Complexity

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We investigate a specific smart grid system and its nonlinear properties. Lyapunov exponents are derived to prove the existence of chaos and bifurcation and bicoherence contours are investigated to show the parameter dependence and existence of quadratic nonlinearities, respectively. A fractional order model of the smart grid system (FOSG) is then derived and bifurcation of the FOSG system with variation in the commensurate fractional order of the system is investigated to show that largest Lyapunov exponent of the system exists in fractional order. Hence we proposed two different control methods to suppress the chaotic oscillations. In the first method we derive fractional order adaptive sliding mode control (FOASMC) algorithm to control chaotic oscillations and in the second method we used genetically optimized fractional order PID controllers (GAFOPID) for chaos control. Numerical simulations are conducted to show the effectiveness of the controllers and also to prove that GAFOPID controllers are more effective than FOASMC controllers for fractional order systems. The GAFOPID controllers are then realized in FPGA to show that the proposed methodology is hardware realizable.

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Section: Controllers (Gafopid)mentioning

confidence: 99%

Chaos Control in Fractional Order Smart Grid with Adaptive Sliding Mode Control and Genetically Optimized PID Control and Its FPGA Implementation

Karthikeyan

Rajagopal

2017

Complexity

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“…In the previous works, there are many type of control techniques have been reported, which can be utilized to control the tracking capability of a nonlinear electrohydraulic actuator system. Each of the control techniques required a proper tuning technique and some of the advanced tuning techniques have been reported recently such as Particle Swarm Optimization (PSO) [13][14][15][16][17][18], Genetic Algorithm (GA) [19][20][21], and Differential Evolution (DE) [22,23].…”

Section: Introductionmentioning

confidence: 99%

An Electro-Hydraulic Servo with Intelligent Control Strategy

Mohammed

Soon

Ghazali

et al. 2018

MATEC Web of Conferences

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Abstract. Versatile engineering applications have been developed to assist, reduce, and avoid human being from any heavy or harmful manufacturing processes. The gradually increased demand in force and position controls have simultaneously increased the usage of Electro-Hydraulic Servo (EHS) system. However, the time varying characteristics such as high-speed, outburst starting and stopping dynamic have led the EHS system to suffer from uncertainties and nonlinearities effects. Therefore, in order to enhance the performance of an EHS to surmount the uncertain and nonlinear effects, a hybrid Fuzzy-PID control strategy is developed which particularly improve the accuracy of the system by enhancing the control performance during the positioning tracking. By measuring the performance of the proposed control approach, the transient response and steady-state analysis will be performed which taking linear and intelligent control strategies as the references in the assessment process. The finding indicates the capability of a hybrid Fuzzy-PID controller in reducing the control effort applied to the EHS system.

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“…To promote the quantity application of wind energy, electricity generated from wind power needs to be connected to the grid so as to be transferred to areas that consume lots of electric energy [2,3]. Furthermore, during the process of wind energy being transformed into electricity, accurate control should be conducted so that higher generation efficiency can be obtained [4,5]. However, the intermittency and randomness nature characteristics of wind speed caused great challenges to integration of wind power and the control operation of wind turbines.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

Kang

Tan

Yuan

et al. 2017

Advances in Meteorology

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Hybrid Ensemble Empirical Mode Decomposition (EEMD) and Least Square Support Vector Machine (LSSVM) is proposed to improve short-term wind speed forecasting precision. The EEMD is firstly utilized to decompose the original wind speed time series into a set of subseries. Then the LSSVM models are established to forecast these subseries. Partial autocorrelation function is adopted to analyze the inner relationships between the historical wind speed series in order to determine input variables of LSSVM models for prediction of every subseries. Finally, the superposition principle is employed to sum the predicted values of every subseries as the final wind speed prediction. The performance of hybrid model is evaluated based on six metrics. Compared with LSSVM, Back Propagation Neural Networks (BP), Auto-Regressive Integrated Moving Average (ARIMA), combination of Empirical Mode Decomposition (EMD) with LSSVM, and hybrid EEMD with ARIMA models, the wind speed forecasting results show that the proposed hybrid model outperforms these models in terms of six metrics. Furthermore, the scatter diagrams of predicted versus actual wind speed and histograms of prediction errors are presented to verify the superiority of the hybrid model in short-term wind speed prediction.

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Design of a fractional order PID controller for hydraulic turbine regulating system using chaotic non-dominated sorting genetic algorithm II

Cited by 203 publications

References 37 publications

Chaos Control in Fractional Order Smart Grid with Adaptive Sliding Mode Control and Genetically Optimized PID Control and Its FPGA Implementation

Chaos Control in Fractional Order Smart Grid with Adaptive Sliding Mode Control and Genetically Optimized PID Control and Its FPGA Implementation

An Electro-Hydraulic Servo with Intelligent Control Strategy

Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

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