Parameter identification of a nonlinear model of hydraulic turbine governing system with an elastic water hammer based on a modified gravitational search algorithm

Li, Chaoshun; Li, Chang; Huang, Zhengjun; Liu, Yi; Zhang, Nan

doi:10.1016/j.engappai.2015.12.016

Cited by 41 publications

(20 citation statements)

References 35 publications

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“…The position of prey (or the global optimum) is considered to be X ! P , and the vector update for the wolves is given by Equations (1) and (2).…”

Section: Encircling Preymentioning

confidence: 99%

“…This implies that the above-mentioned estimation problem is posed as an optimization problem by defining a proper objective function that relates the process dynamics and the corresponding stochastic optimization technique. 2 Many successful optimization techniques are natureinspired algorithms that mimic the behaviour or execution of a process, based on the laws defined by nature. The most familiar and successful evolution-based algorithm is the genetic algorithm 3 whose improved versions [4][5][6] and other similar algorithms such as biogeography-based optimization 7,8 are applied for parameter estimation.…”

Section: Introductionmentioning

confidence: 99%

“…Some of the commonly used physicsbased algorithms for parameter identification are simulated annealing [9][10][11] and gravitational search algorithm (GSA). 2 Both work on the principle of Newton's law of gravity. The modified versions of the existing GSA are termed as improved GSA and modified GSA.…”

Section: Introductionmentioning

confidence: 99%

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Metaheuristic‐based approach for state and process parameter prediction using hybrid grey wolf optimization

Sankaranarayanan

Sivakumaran

Radhakrishnan

et al. 2018

Asia-Pacific J Chem Eng

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Metaheuristic-based optimization algorithms can be used to solve the complexities in estimating the parameters and states of a complex nonlinear process. In this work, a hybridized version of grey wolf optimizer is employed to solve the intricacies of a complex linear process using hybrid grey wolf optimization. The proposed algorithm is executed simultaneously in 2 steps: (a) predicting the parameters and states using grey wolf optimizer and (b) updating the predicted parameters using static modified Kalman-Bucy mechanism. The significance of the hybridization is that it improves the convergence rate towards an optimal solution and explores global optimum effectively over a complex search space. The performance of the proposed algorithm is tested through 10 typical benchmark functions and is compared with the parent algorithm, grey wolf optimizer, and conventional particle swarm optimization. Furthermore, the findings of the hybrid grey wolf optimization are compared with the outcomes of the conventional algorithms to establish the effectiveness of the proposed hybrid grey wolf optimization in process parameter estimation.

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“…The position of prey (or the global optimum) is considered to be X ! P , and the vector update for the wolves is given by Equations (1) and (2).…”

Section: Encircling Preymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Metaheuristic‐based approach for state and process parameter prediction using hybrid grey wolf optimization

Sankaranarayanan

Sivakumaran

Radhakrishnan

et al. 2018

Asia-Pacific J Chem Eng

View full text Add to dashboard Cite

show abstract

“…Nowadays, nature inspired algorithms have been widely applied in system identification [9][10][11]. It offers an alternative strategy that works with computational methods inspired by nature and natural ecosystem.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Damped Compound Pendulum Using Bat Algorithm

et al. 2016

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Abstract. In this study, the parameter identification of the damped compound pendulum system is proposed using one of the most promising nature inspired algorithms which is Bat Algorithm (BA). The procedure used to achieve the parameter identification of the experimental system consists of input-output data collection, ARX model order selection and parameter estimation using bat algorithm (BA) method. PRBS signal is used as an input signal to regulate the motor speed. Whereas, the output signal is taken from position sensor. Both, input and output data is used to estimate the parameter of the autoregressive with exogenous input (ARX) model. The performance of the model is validated using mean squares error (MSE) between the actual and predicted output responses of the models. Finally, comparative study is conducted between BA and the conventional estimation method (i.e. Least Square). Based on the results obtained, MSE produce from Bat Algorithm (BA) is outperformed the Least Square (LS) method.

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“…In practical application, it can be used as a value added block to strengthen the feed-forward control capability [2]. ILC has been widely used in industries for control of repetitive motions, such as those of robotic manipulators, hard disk drives, chemical plants, and so on [3][4][5]. ILC algorithms can improve the trajectory tracking capability to reach better tracking results with an improving the numbers of iterations by utilizing the repetitive characteristics of the training process.…”

Section: Introductionmentioning

confidence: 99%

Forgetting Factor Nonlinear Functional Analysis for Iterative Learning System with Time-Varying Disturbances and Unknown Uncertain

Zheng¹

2017

ACIS

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This paper focuses on the iterative learning tracking control problem for a class of nonlinear system with time-varying disturbances. First, because of the mismatches in time-varying disturbances functions, a high-order feed-forward iterative learning control (ILC) is employed to change the original system into an iterative system. Secondly, a variable forgetting factor is developed to stabilize the system. Based on the feed-forward iterative learning controller, a memory controller is constructed for the nonlinear system. By choosing a new variable forgetting factor, we show that the designed continuous adaptive controller makes the solutions of the closed-loop system convergent to a ball exponentially. Finally, a numerical example is given to show the feasibility and effectiveness of the proposed method.

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Parameter identification of a nonlinear model of hydraulic turbine governing system with an elastic water hammer based on a modified gravitational search algorithm

Cited by 41 publications

References 35 publications

Metaheuristic‐based approach for state and process parameter prediction using hybrid grey wolf optimization

Metaheuristic‐based approach for state and process parameter prediction using hybrid grey wolf optimization

Parameter Estimation of Damped Compound Pendulum Using Bat Algorithm

Forgetting Factor Nonlinear Functional Analysis for Iterative Learning System with Time-Varying Disturbances and Unknown Uncertain

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