Nonlinear system identification and control using state transition algorithm

Zhou, Xiaojun; Yang, Chunhua; Gui, Weihua

doi:10.1016/j.amc.2013.09.055

Cited by 58 publications

(25 citation statements)

References 24 publications

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“…As a stochastic optimization algorithm, compared with classical GA and PSO, the STA has better performance in terms of global search ability and convergence accuracy . However, considering the requirements of actual AEP problems, we next propose a multi‐objective STA to solve the optimization problem arising in AEP.…”

Section: Constrained Multi‐objective State Transition Algorithm (Cmosta)mentioning

confidence: 99%

Optimization of both operating costs and energy efficiency in the alumina evaporation process by a multi‐objective state transition algorithm

Wang

Zhou

et al. 2015

Can J Chem Eng

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The alumina evaporation process (AEP) is an indispensable step for the reuse of sodium aluminate solution by evaporating excess water contained in the solution. The selection of optimal operating parameters is a complicated task because the process is influenced by many nonlinear factors when both the quality and quantity of the product are concerned. In this paper, we formulate a multi‐objective optimization model to maintain the balance of operating costs and energy efficiency in AEP, and a multi‐objective state transition algorithm (MOSTA) is proposed for solving this problem. With the aim of solving the constrained multi‐objective problem, a search archive strategy of elite populations and a novel infeasible solution replacement mechanism are integrated into STA. Some infeasible solutions with better performances are allowed to be saved and participate randomly in the evolution to select optimal solutions from all possible directions. A mutation operator is introduced into the evolutionary process to enhance the global search ability. Simulation results from some benchmark test problems show that the proposed method tends to converge quickly and effectively to the true Pareto frontier with better distribution. The proposed algorithm is successfully applied to solve the multi‐objective optimization problem arising in AEP. The optimal results show that operating costs and energy loss are considerably reduced, by approximately 13.63 % and 13.39 %, respectively.

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Section: Constrained Multi‐objective State Transition Algorithm (Cmosta)mentioning

confidence: 99%

Optimization of both operating costs and energy efficiency in the alumina evaporation process by a multi‐objective state transition algorithm

Wang

Zhou

et al. 2015

Can J Chem Eng

Self Cite

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Section: Introductionmentioning

confidence: 99%

“…Further, inferred that the results of the STA are found better than others in the list [8], [9]. In [9], Zhou et al, have signified that STA is a promising algorithm for the system identification and design of controllers. In this paper, the performance of STA is compared with PSO for the tuning of integer and fractional order PID controller to the Benchmark system by considering IAE as an objective function.…”

Section: Introductionmentioning

confidence: 99%

State Transition Algorithm (STA) based tuning of integer and fractional-order PID controller for benchmark system

Saravanakumar

Valarmathi²,

Rajasekaran

et al. 2015

2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)

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This paper proposes the application of State Transition Algorithm (STA) in the design of integer and fractional-Order PID controllers for SISO benchmark system. The PID controller parameters are obtained by minimizing the Integral of the Absolute Error (IAE). Recent popular algorithm namely Particle Swarm Optimization (PSO) is considered for comparing the performance of STA in PID controller design. The PID controllers obtained using STA and PSO are compared in terms of time domain performance. Simulation results show that STA based controllers are given better IAE value and improved time response specifications than PSO based controllers. Index Terms-PID controller, Fractional order PID, State Transition Algorithm (STA), Particle Swarm Optimization (PSO) 978-1-4799-7849-6/15/$31.00 ©2015 IEEE

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“…According to STA, a solution to an optimization problem is regarded as a state and the process of updating current solution is regarded as a state transition [12].…”

Section: Weights Allocation Based On Stamentioning

confidence: 99%

Temperature Prediction of Aluminum Alloy Work-Pieces in Aging Furnaces Based on Improved Case-Based Reasoning

Zhu¹,

Shen²,

Jiang³

et al. 2017

IJNM

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The temperature of aluminum alloy work-pieces in the aging furnace directly affects the quality of aluminum alloy products. Since the temperature of aluminum alloy work-pieces cannot be measured directly, a temperature prediction model based on improved case-based reasoning (CBR) method is established to realize the online measurement of the work-pieces temperature. More specifically, the model is constructed by an advanced case-based reasoning method in which a state transition algorithm (STA) is firstly used to optimize the weights of feature attributes. In other words, STA is utilized to find the suitable attribute weights of the CBR model that can improve the accuracy of the case retrieval process. Finally, the CBR model based on STA (STCBR) was applied to predict the temperature of aluminum alloy work-pieces in the aging furnace. The results of the experiments indicated that the developed model can realize high-accuracy prediction of work-pieces temperature and it has good application prospects in the industrial field.

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Nonlinear system identification and control using state transition algorithm

Cited by 58 publications

References 24 publications

Optimization of both operating costs and energy efficiency in the alumina evaporation process by a multi‐objective state transition algorithm

Optimization of both operating costs and energy efficiency in the alumina evaporation process by a multi‐objective state transition algorithm

State Transition Algorithm (STA) based tuning of integer and fractional-order PID controller for benchmark system

Temperature Prediction of Aluminum Alloy Work-Pieces in Aging Furnaces Based on Improved Case-Based Reasoning

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