This paper reviews recent advancements in system identification methods for perturbed motorized systems,focusing on brushed DC motors,brushless DC motors,and permanent magnet synchronous motors.It examines data acquis...This paper reviews recent advancements in system identification methods for perturbed motorized systems,focusing on brushed DC motors,brushless DC motors,and permanent magnet synchronous motors.It examines data acquisition setups and evaluates conventional and metaheuristic optimization algorithms,highlighting their advantages,limitations,and applications.The paper explores emerging trends in model structures and parameter optimization techniques that address specific perturbations such as varying loads,noise,and friction.A comparative performance analysis is also included to assess several widely used optimization methods,including least squares(LS),particle swarm optimization(PSO),grey wolf optimizer(GWO),bat algorithm(BA),genetic algorithm(GA)and neural network for system identification of a specific case of a perturbed DC motor in both open-loop(OL)and closed-loop(CL)settings.Results show that GWO achieves the lowest error overall,excelling in OL scenarios,while PSO performs best in CL due to its synergy with feedback control.LS proves efficient in CL settings,whereas GA and BA rely heavily on feedback for improved performance.The paper also outlines potential research directions aimed at advancing motor modeling techniques,including integration of advanced machine learning methods,hybrid learning-based methods,and adaptive modeling techniques.These insights offer a foundation for advancing motor modeling techniques in real-world applications.展开更多
变量施肥是精准农业的重要组成部分,非线性、大惯性和参数时变性是影响水肥一体化控制系统精度和稳态性能的关键因素。PID控制算法因其简单方便而被人们广泛应用于工农业领域中,但往往很难达到理想的控制效果。灰狼优化算法(Gray Wolf O...变量施肥是精准农业的重要组成部分,非线性、大惯性和参数时变性是影响水肥一体化控制系统精度和稳态性能的关键因素。PID控制算法因其简单方便而被人们广泛应用于工农业领域中,但往往很难达到理想的控制效果。灰狼优化算法(Gray Wolf Optimization Algorithm, GWO)是一种参数设置少且收敛性能好的群体智能优化算法,但在迭代过程中容易陷入局部最优解。为此,通过在标准GWO算法中引入遗传交叉和变异算子,结合佳点集方法,提出一种改进的新型灰狼智能优化算法(Genetic–Grey Wolf Optimization algorithm, GGWO),并将改进的遗传-灰狼优化算法应用于水肥一体化控制系统的PID控制中。以液肥控制系统为研究对象,建立相应的负反馈控制系统数学模型,分别采用常规PID控制、基于GWO的PID控制以及基于GGWO的PID等3种不同控制方法并用MatLab对其进行仿真,并对比分析了各控制方法下的系统性能指标。仿真结果表明:基于GGWO的PID控制在系统的上升时间、调节时间和适应值等性能指标上都优于其它两种控制方法,在系统的精度、均匀性、鲁棒性和稳态性能上实现了更好的控制效果,不仅满足了精准农业的作业要求,而且为后续研究打下了基础。展开更多
In this paper,a hybrid of grey wolf optimization(GWO)and genetic algorithm(GA)has been implemented to minimize the annual cost of hybrid of wind and solar renewable energy system.It was named as hybrid of grey wolf op...In this paper,a hybrid of grey wolf optimization(GWO)and genetic algorithm(GA)has been implemented to minimize the annual cost of hybrid of wind and solar renewable energy system.It was named as hybrid of grey wolf optimization and genetic algorithm(HGWOGA).HGWOGA was applied to this hybrid problem through three procedures.First,the balance between the exploration and the exploitation process was done by grey wolf optimizer algorithm.Then,we divided the population into subpopulation and used the arithmetical crossover operator to utilize the dimension reduction and the population partitioning processes.At last,mutation operator was applied in the whole population in order to refrain from the premature convergence and trapping in local minima.MATLAB code was designed to implement the proposed methodology.The result of this algorithm is compared with the results of iteration method,GWO,GA,artificial bee colony(ABC)and particle swarm optimization(PSO)techniques.The results obtained by this algorithm are better when compared with those mentioned in the text.展开更多
高维函数优化一般是指维数超过100维的函数优化问题,由于"维数灾难"的存在,求解起来十分困难.针对灰狼算法迭代后期收敛速度慢,求解高维函数易陷入局部最优的缺点,在基本灰狼算法中引入3种遗传算子,提出一种遗传-灰狼混合算法...高维函数优化一般是指维数超过100维的函数优化问题,由于"维数灾难"的存在,求解起来十分困难.针对灰狼算法迭代后期收敛速度慢,求解高维函数易陷入局部最优的缺点,在基本灰狼算法中引入3种遗传算子,提出一种遗传-灰狼混合算法(hybrid genetic grey wolf algorithm,HGGWA).混合算法能够充分发挥两种算法各自的优势,提高算法的全局收敛性,针对精英个体的变异操作有效防止算法陷入局部最优值.通过13个标准测试函数和10个高维测试函数验证算法的性能,并将优化结果与PSO、GSA、GWO三种基本算法以及9种改进算法进行比较.仿真结果表明,所提算法在收敛精度方面得到了极大改进,验证了HGGWA算法求解高维函数的有效性.展开更多
基金supported by the Malaysia Ministry of Higher Education under Fundamental Research Grant Scheme with Project Code:FRGS/1/2024/TK07/USM/02/3.
文摘This paper reviews recent advancements in system identification methods for perturbed motorized systems,focusing on brushed DC motors,brushless DC motors,and permanent magnet synchronous motors.It examines data acquisition setups and evaluates conventional and metaheuristic optimization algorithms,highlighting their advantages,limitations,and applications.The paper explores emerging trends in model structures and parameter optimization techniques that address specific perturbations such as varying loads,noise,and friction.A comparative performance analysis is also included to assess several widely used optimization methods,including least squares(LS),particle swarm optimization(PSO),grey wolf optimizer(GWO),bat algorithm(BA),genetic algorithm(GA)and neural network for system identification of a specific case of a perturbed DC motor in both open-loop(OL)and closed-loop(CL)settings.Results show that GWO achieves the lowest error overall,excelling in OL scenarios,while PSO performs best in CL due to its synergy with feedback control.LS proves efficient in CL settings,whereas GA and BA rely heavily on feedback for improved performance.The paper also outlines potential research directions aimed at advancing motor modeling techniques,including integration of advanced machine learning methods,hybrid learning-based methods,and adaptive modeling techniques.These insights offer a foundation for advancing motor modeling techniques in real-world applications.
文摘变量施肥是精准农业的重要组成部分,非线性、大惯性和参数时变性是影响水肥一体化控制系统精度和稳态性能的关键因素。PID控制算法因其简单方便而被人们广泛应用于工农业领域中,但往往很难达到理想的控制效果。灰狼优化算法(Gray Wolf Optimization Algorithm, GWO)是一种参数设置少且收敛性能好的群体智能优化算法,但在迭代过程中容易陷入局部最优解。为此,通过在标准GWO算法中引入遗传交叉和变异算子,结合佳点集方法,提出一种改进的新型灰狼智能优化算法(Genetic–Grey Wolf Optimization algorithm, GGWO),并将改进的遗传-灰狼优化算法应用于水肥一体化控制系统的PID控制中。以液肥控制系统为研究对象,建立相应的负反馈控制系统数学模型,分别采用常规PID控制、基于GWO的PID控制以及基于GGWO的PID等3种不同控制方法并用MatLab对其进行仿真,并对比分析了各控制方法下的系统性能指标。仿真结果表明:基于GGWO的PID控制在系统的上升时间、调节时间和适应值等性能指标上都优于其它两种控制方法,在系统的精度、均匀性、鲁棒性和稳态性能上实现了更好的控制效果,不仅满足了精准农业的作业要求,而且为后续研究打下了基础。
文摘In this paper,a hybrid of grey wolf optimization(GWO)and genetic algorithm(GA)has been implemented to minimize the annual cost of hybrid of wind and solar renewable energy system.It was named as hybrid of grey wolf optimization and genetic algorithm(HGWOGA).HGWOGA was applied to this hybrid problem through three procedures.First,the balance between the exploration and the exploitation process was done by grey wolf optimizer algorithm.Then,we divided the population into subpopulation and used the arithmetical crossover operator to utilize the dimension reduction and the population partitioning processes.At last,mutation operator was applied in the whole population in order to refrain from the premature convergence and trapping in local minima.MATLAB code was designed to implement the proposed methodology.The result of this algorithm is compared with the results of iteration method,GWO,GA,artificial bee colony(ABC)and particle swarm optimization(PSO)techniques.The results obtained by this algorithm are better when compared with those mentioned in the text.
文摘高维函数优化一般是指维数超过100维的函数优化问题,由于"维数灾难"的存在,求解起来十分困难.针对灰狼算法迭代后期收敛速度慢,求解高维函数易陷入局部最优的缺点,在基本灰狼算法中引入3种遗传算子,提出一种遗传-灰狼混合算法(hybrid genetic grey wolf algorithm,HGGWA).混合算法能够充分发挥两种算法各自的优势,提高算法的全局收敛性,针对精英个体的变异操作有效防止算法陷入局部最优值.通过13个标准测试函数和10个高维测试函数验证算法的性能,并将优化结果与PSO、GSA、GWO三种基本算法以及9种改进算法进行比较.仿真结果表明,所提算法在收敛精度方面得到了极大改进,验证了HGGWA算法求解高维函数的有效性.
