Sliding Mode Control Based on Genetic Algorithm for WSCC Systems Include of SVC

Köse, Ercan; Abacı, Kadir; Kizmaz, Hakan; Aksoy, Saadettin; Yalçın, Mehmet Ali

doi:10.5755/j01.eee.19.4.1580

Cited by 11 publications

(10 citation statements)

References 18 publications

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“…GA is an adaptive heuristic search algorithm that mimics the process of natural selection and uses biological evolution to develop a series of search space points toward an optimal solution. There are five components that are required to implement GA: representation, initialization, fitness function, genetic operators, and genetic parameters [ 39 ].…”

Section: Control Strategy Designmentioning

confidence: 99%

Robust Sliding Mode Control Based on GA Optimization and CMAC Compensation for Lower Limb Exoskeleton

Long

Wang

et al. 2016

Applied Bionics and Biomechanics

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A lower limb assistive exoskeleton is designed to help operators walk or carry payloads. The exoskeleton is required to shadow human motion intent accurately and compliantly to prevent incoordination. If the user's intention is estimated accurately, a precise position control strategy will improve collaboration between the user and the exoskeleton. In this paper, a hybrid position control scheme, combining sliding mode control (SMC) with a cerebellar model articulation controller (CMAC) neural network, is proposed to control the exoskeleton to react appropriately to human motion intent. A genetic algorithm (GA) is utilized to determine the optimal sliding surface and the sliding control law to improve performance of SMC. The proposed control strategy (SMC_GA_CMAC) is compared with three other types of approaches, that is, conventional SMC without optimization, optimal SMC with GA (SMC_GA), and SMC with CMAC compensation (SMC_CMAC), all of which are employed to track the desired joint angular position which is deduced from Clinical Gait Analysis (CGA) data. Position tracking performance is investigated with cosimulation using ADAMS and MATLAB/SIMULINK in two cases, of which the first case is without disturbances while the second case is with a bounded disturbance. The cosimulation results show the effectiveness of the proposed control strategy which can be employed in similar exoskeleton systems.

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Section: Control Strategy Designmentioning

confidence: 99%

Robust Sliding Mode Control Based on GA Optimization and CMAC Compensation for Lower Limb Exoskeleton

Long

Wang

et al. 2016

Applied Bionics and Biomechanics

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“…Other groups of power systems problems to solve using the GA are the energy consumption optimization tasks [14,15] and optimizations for forecasting purposes [16,17]. The nature of the problem under consideration is very similar to the optimal power flow [18], optimal automation devices [19] or distributed generation sources [20] allocation, optimal operating and scheduling of micro grids [21] and other problems [22,23] but with different objective functions. These are the entire tasks based on network operation mode calculations with the pre-defined non-linear constraints to find global extremum, i.e., the minimal losses, the minimal or the maximal flows, the minimal investments, or the worst-case error for the case under consideration.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of the Systematic Error of a Remote Method for a Wattmeter Adjustment Gain Estimation in Smart Grids

et al. 2018

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Smart energy meters supporting bidirectional data communication enable novel remote error monitoring applications. This research targets characterization of the systematic worst-case error of the previously published remote watthour meter’s gain estimation method based on the comparison of synchronous measurements by the reference and meter under test. To achieve the research aim a methodology based on global maximization of the systematic error objective function assuming the typical low voltage electrical distribution network operation parameters ranges as defined by the standard recommendations for network design. To cross verify the reliability of the assessed solutions the suggested error analysis methodology was implemented utilizing two stochastic global extremum search techniques (genetic algorithms, pattern search) and the third one utilizing nonlinear programming solver. It was determined that the wattmeter adjustment gain worst-case error does not exceed 0.5% if the remote wattmeter monitored load power factor is larger than 0.1 and a network is designed according to the recommendation of the acceptable voltage drop less than 5%. For a load exhibiting power factor larger than cos φ = 0.9 the worst-case error was found to be less than 0.1%. It is concluded therefore that considering the systematic worst-case error the previously suggested remote wattmeter adjustment gain estimation method is suitable for remote error monitoring of Class 2 and Class 1 wattmeters.

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“…In the last two decades, researchers have developed different types of optimization algorithms that may be used by scientists in control area. Bee [1], Firefly [2], Bat [3], Virus [4], Genetic [5], Cuckoo [6], Particle Swarm [7], Gravitation [8] and Biogeography [9] may be given for example.…”

Section: Introductionmentioning

confidence: 99%

FFANN Optimization by ABC for Controlling a 2nd Order SISO System’s Output with a Desired Settling Time

Mühürcü

2018

Processes

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In this study, a control strategy is aimed to ensure the settling time of a 2nd order system’s output value while its input reference value is changed. Here, Feed Forward Artificial Neural Network (FFANN) nonlinear structure has been chosen as a control algorithm. In order to implement the intended control strategy, FFANN’s normalization coefficient (K), learning coefficients (ŋ), momentum coefficients (μ) and the sampling time (Ts) were optimized by Artificial Bee Colony (ABC) but FFANN’s values of weights were chosen arbitrary on start time of control system. After optimization phase, the FFANN behaves as an adaptive optimal discrete time non-linear controller that forces the system output to take the same value with the input reference for a desired settling time (ts). The success of the optimization algorithm was proved with close loop feedback control simulations on Matlab’s Simulink platform based on 2nd order transfer functions. Also, the success was proved with a 2nd order physical system (buck converter) that was structured with power electronics elements on Simulink platform. Finally, the success of the control process was discussed by observing results.

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Sliding Mode Control Based on Genetic Algorithm for WSCC Systems Include of SVC

Cited by 11 publications

References 18 publications

Robust Sliding Mode Control Based on GA Optimization and CMAC Compensation for Lower Limb Exoskeleton

Robust Sliding Mode Control Based on GA Optimization and CMAC Compensation for Lower Limb Exoskeleton

An Analysis of the Systematic Error of a Remote Method for a Wattmeter Adjustment Gain Estimation in Smart Grids

FFANN Optimization by ABC for Controlling a 2nd Order SISO System’s Output with a Desired Settling Time

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