Self-Tuning fuzzy controller for sun-tracker system using Gray Wolf Optimization (GWO) technique

Tripathi, Sandeep; Shrivastava, Ashish; Jana, Kartick Chandra

doi:10.1016/j.isatra.2020.01.012

Cited by 22 publications

(15 citation statements)

References 21 publications

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“…Equations ( 23) and ( 24) can ensure that the requirements of each shipping point are met. Equation (25) can ensure that the needs of each shipping point can only be met by one car. For each vehicle, equation (26) can ensure the total cargo volume of whole transportation path shall not exceed its own maximum capacity.…”

Section: Principle Of Cvrpmentioning

confidence: 99%

“…It has strong versatility and can solve various continuous problems as well as discrete problems. [3][4][5] Up to now, there have been many mature metaheuristic algorithms with good performance, such as Sparrow Search Algorithm (SSA), [6][7][8] Genetic Algorithm (GA), [9][10][11] Butterfly Optimization Algorithm (BOA), [12][13][14] Differential Evolution (DE), [15][16][17][18] Cuckoo Search (CS), [19][20][21] Harris Hawk Optimization (HHO), [22][23][24] Gray Wolf Optimization (GWO), [25][26][27][28] Fish Migration Optimization (FMO), 29,30 Particle Swarm Optimization (PSO), [31][32][33][34] Phasmatodea Population Evolution algorithm (PPE), 35,36 Cat Swarm Optimization (CSO), [37][38][39] and Ant Colony Optimization (ACO) [40][41][42][43] .…”

Section: Introductionmentioning

confidence: 99%

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A novel Fruit Fly Optimization Algorithm with quasi-affine transformation evolutionary for numerical optimization and application

Wang

et al. 2022

International Journal of Distributed Sensor Networks

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The Fruit Fly Optimization Algorithm is a swarm intelligence algorithm with strong versatility and high computational efficiency. However, when faced with complex multi-peak problems, Fruit Fly Optimization Algorithm tends to converge prematurely. In response to this situation, this article proposes a new optimized structure—Quasi-affine Transformation evolutionary for the Fruit fly Optimization Algorithm. The new algorithm uses the evolution matrix in QUasi-Affine TRansformation Evolution algorithm to update the position coordinates of particles. This strategy makes the movement of particles more scientific and the search space broader. In order to prove its effectiveness, we compare Quasi-affine Transformation evolutionary for the Fruit fly Optimization Algorithm with five other mature intelligent algorithms, and test them on 22 different types of benchmark functions. In order to observe the multi-faceted performance of Quasi-affine Transformation evolutionary for the Fruit fly Optimization Algorithm more intuitively, we also conduct experiments on algorithm convergence analysis, the Friedman test, the Wilcoxon signed-rank test, and running time comparison. Through the above several comparative experiments, Quasi-affine Transformation evolutionary for the Fruit fly Optimization Algorithm has indeed demonstrated its strong competitiveness. Finally, we apply it to Capacitated Vehicle Routing Problem. Through comparing with the contrast algorithms, it is confirmed that Quasi-affine Transformation evolutionary for the Fruit fly Optimization Algorithm can achieve better vehicle routes planning.

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Section: Principle Of Cvrpmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel Fruit Fly Optimization Algorithm with quasi-affine transformation evolutionary for numerical optimization and application

Wang

et al. 2022

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

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“…In that study, the integral time absolute error (ITAE) based optimization has shown better control performance than that obtained by integral square error (ISE). Tripathi et al [24] have utilized GWO for developing an optimal controller for the axis position of sun tracking system resulting in maximum output current. The obtained results have shown GWO effectiveness over WOA and PSO algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Tripathi et al. [24] have utilized GWO for developing an optimal controller for the axis position of sun tracking system resulting in maximum output current. The obtained results have shown GWO effectiveness over WOA and PSO algorithms.…”

Section: Introductionmentioning

confidence: 99%

Robust control for techno‐economic efficient energy management of fuel cell hybrid electric vehicles

Ramadan

Hassan

Alhelou

2022

IET Renewable Power Gen

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The design of an efficient techno‐economic autonomous fuel cell hybrid electric vehicle (FCHEV) is a crucial challenge. This paper investigates the design of a near optimal PI controller for an automated FCHEV, where autonomy is expressed as efficient and robust tracking of a given reference speed trajectory without driver's intervention. An impartial comparison is introduced to illustrate the effectiveness of the proposed metaheuristic‐based optimal controllers in enhancing the system dynamic performance. The comprehensive optimization performance indicator is considered as a function of the vehicle dynamic characteristics while determining the optimal controller gains. In this paper, the proposed effective up‐to‐date metaheuristic techniques are the grey wolf optimization (GWO) as well as the artificial bee colony (ABC). Using MATLABTM/Simulink, numerical simulations clearly illustrate the efficiency of near‐optimal gains in the optimized tuning methodologies and the fixed manual one in realizing adequate velocity tracking. The simulation results demonstrate the superiority of both ABC and GWO rather than the manual controller for driving cycles of high acceleration and deceleration levels. In absence of these latter, the manual defined gain controller is considered sufficient. Through a comprehensive sensitivity analysis, the robustness of both metaheuristic‐based controllers is verified under diverse driving cycles of different operation features and nature. Despite GWO results in better dynamic characteristics, the ABC provides more economical feature with about 1.5% compared to manual system in extra urban driving cycle. However, manual‐controller has the minimum fuel cost under the United States driving cycle developed by the environmental protection agency as a New York city cycle (US EPA NYCC) and urban driving cycle (ECE). Ecologically, electric vehicles have an environmentally friendly effect especially when driven with green hydrogen. Autonomous vehicles, involving velocity control systems, would raise car share and provide more comfort.

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“…Such challenging optimization problems are those specific to the optimal (parameter) tuning of fuzzy (logic) controllers, where both the process and the controller are nonlinear and deterministic algorithms are not successful. The following metaheuristic algorithms have been applied most recently to the optimal tuning of fuzzy controllers in representative examples: adaptive weight Genetic Algorithm (GA) for gear shifting control [3], GA-based multiobjective optimization for electric vehicle powertrain control [4], GA for hybrid power systems control [5], engines control [6], energy management in hybrid vehicles [7], servo system control [2], wellhead back pressure control systems [8], micro-unmanned helicopter control [9], Particle Swarm Optimization (PSO) algorithm with compensating coefficient of inertia weight factor for filter time constant adaptation in hybrid energy storage systems control [10], set-based PSO algorithm with adaptive weights for optimal path planning of unmanned aerial vehicles [11], PSO algorithm for zinc production [12] and inverted pendulum control [13], hybrid PSO-Artificial Bee Colony algorithm for frequency regulation in microgrids [14], Imperialist Competitive Algorithm for human immunodeficiency control [15], Grey Wolf Optimizer (GWO) algorithms for sun-tracker systems [16] and servo system control [2], PSO, Cuckoo Search and Differential Evolution (DE) for gantry crane systems position control [17], Whale Optimization Algorithm (WOA) for vibration control of steel structures [18], Grasshopper Optimization Algorithm for load frequency control [19], DE for electro-hydraulic servo system control [20], Gravitational Search Algorithm (GSA) and Charged System Search (CSS) for servo system control [2].…”

Section: Introductionmentioning

confidence: 99%

Slime Mould Algorithm-Based Tuning of Cost-Effective Fuzzy Controllers for Servo Systems

Precup¹,

David²,

Roman³

et al. 2021

IJCIS

104

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This paper suggests five new contributions with respect to the state-of-the-art. First, the optimal tuning of cost-effective fuzzy controllers represented by Takagi-Sugeno-Kang proportional-integral fuzzy controllers (TSK PI-FCs) is carried out using a fresh metaheuristic algorithm, namely the Slime Mould Algorithm (SMA), and a fuzzy controller tuning approach is offered. Second, a relatively easily understandable formulation of SMA is offered. Third, a real-world application of SMA is given, focusing on the optimal tuning of TSK PI-FCs for nonlinear servo systems in terms of optimization problems that target the minimization of discrete-time cost functions defined as the sum of time multiplied by squared control error. Fourth, using the concept of improving the performance of metaheuristic algorithms with information feedback models, proposed by Wang and Tan, Improving metaheuristic algorithms with information feedback models, IEEE Trans. Cybern. 49 (2019), 542-555, Gu and Wang, Improving NSGA-III algorithms with information feedback models for large-scale many-objective optimization, Fut. Gen. Comput. Syst. 107 (2020), 49-69, and Zhang et al., Enhancing MOEA/D with information feedback models for large-scale many-objective optimization, Inf. Sci. 522 (2020), 1-16, new metaheuristic algorithms are introduced in terms of inserting the model F1 in SMA and other representative algorithms, namely Gravitational Search Algorithm (GSA), Charged System Search (CSS), Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA). Fifth, the real-time validation of the cost-effective fuzzy controllers and their tuning approach is performed in the framework of angular position control of laboratory servo system. The comparison with other metaheuristic algorithms that solve the same optimization problem for optimal parameter tuning of cost-effective fuzzy controllers suggestively highlights the superiority of SMA. Experimental results are included.

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Self-Tuning fuzzy controller for sun-tracker system using Gray Wolf Optimization (GWO) technique

Cited by 22 publications

References 21 publications

A novel Fruit Fly Optimization Algorithm with quasi-affine transformation evolutionary for numerical optimization and application

A novel Fruit Fly Optimization Algorithm with quasi-affine transformation evolutionary for numerical optimization and application

Robust control for techno‐economic efficient energy management of fuel cell hybrid electric vehicles

Slime Mould Algorithm-Based Tuning of Cost-Effective Fuzzy Controllers for Servo Systems

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