Efficient speed controllers for dynamic driving tasks in autonomous vehicles are crucial for ensuring safety and reliability.This study proposes a novel approach for designing a fractional order proportional-integral-...Efficient speed controllers for dynamic driving tasks in autonomous vehicles are crucial for ensuring safety and reliability.This study proposes a novel approach for designing a fractional order proportional-integral-derivative(FOPID)controller that utilizes a modified elite opposition-based artificial hummingbird algorithm(m-AHA)for optimal parameter tuning.Our approach outperforms existing optimization techniques on benchmark functions,and we demonstrate its effectiveness in controlling cruise control systems with increased flexibility and precision.Our study contributes to the advancement of autonomous vehicle technology by introducing a novel and efficient method for FOPID controller design that can enhance the driving experience while ensuring safety and reliability.We highlight the significance of our findings by demonstrating how our approach can improve the performance,safety,and reliability of autonomous vehicles.This study’s contributions are particularly relevant in the context of the growing demand for autonomous vehicles and the need for advanced control techniques to ensure their safe operation.Our research provides a promising avenue for further research and development in this area.展开更多
Smart grids and their technologies transform the traditional electric grids to assure safe,secure,cost-effective,and reliable power transmission.Non-linear phenomena in power systems,such as voltage collapse and oscil...Smart grids and their technologies transform the traditional electric grids to assure safe,secure,cost-effective,and reliable power transmission.Non-linear phenomena in power systems,such as voltage collapse and oscillatory phenomena,can be investigated by chaos theory.Recently,renewable energy resources,such as wind turbines,and solar photovoltaic(PV)arrays,have been widely used for electric power generation.The design of the controller for the direct Current(DC)converter in a PV system is performed based on the linearized model at an appropriate operating point.However,these operating points are everchanging in a PV system,and the design of the controller is usually accomplished based on a low irradiance level.This study designs a fractional-order proportional-integrated-derivative(FOPID)controller using deep learning(DL)with quasi-oppositional Archimedes Optimization algorithm(FOPID-QOAOA)for cascaded DC-DC converters in micro-grid applications.The presented FOPIDQOAOA model is designed to enhance the overall efficiency of the cascaded DC-DC boost converter.In addition,the proposed model develops a FOPID controller using a stacked sparse autoencoder(SSAE)model to regulate the converter output voltage.To tune the hyper-parameters related to the SSAE model,the QOAOA is derived by the including of the quasi-oppositional based learning(QOBL)with traditional AOA.Moreover,an objective function with the including of the integral of time multiplied by squared error(ITSE)is considered in this study.For validating the efficiency of the FOPID-QOAOA method,a sequence of simulations was performed under distinct aspects.A comparative study on cascaded buck and boost converters is carried out to authenticate the effectiveness and performance of the designed techniques.展开更多
本文研究了在FOPID控制器控制下的广义Van Der Pol随机系统瞬态概率密度函数和可靠性函数变化情况.首先,引入广义谐和函数,将快变变量转换为慢变变量,并利用分数阶微积分的性质,获得了FOPID控制器在慢变变量形式下的新表达式.在此基础上...本文研究了在FOPID控制器控制下的广义Van Der Pol随机系统瞬态概率密度函数和可靠性函数变化情况.首先,引入广义谐和函数,将快变变量转换为慢变变量,并利用分数阶微积分的性质,获得了FOPID控制器在慢变变量形式下的新表达式.在此基础上,由于径向基神经网络具有准确性高,易于求解高维问题,求解速度快等优势,所以我们应用径向基神经网络分别对该随机系统所满足的前向和后向柯尔莫哥洛夫方程进行求解,得到随机系统的瞬态概率密度函数和可靠性函数.最后,通过分析控制器中分数阶导数和分数阶积分对Van Der Pol随机系统响应和可靠性的影响,我们得到结论,分数阶控制器一定程度上会增强系统的响应,并导致分岔.展开更多
This paper delves into the parameter tuning of fractional-order PID(FOPID)controllers.FOPID controllers,with additional integral and derivative orders compared to traditional PID controllers,possess enhanced capabilit...This paper delves into the parameter tuning of fractional-order PID(FOPID)controllers.FOPID controllers,with additional integral and derivative orders compared to traditional PID controllers,possess enhanced capabilities in handling complex systems.However,effective tuning of its five parameters is challenging.To address this,multiple intelligent algorithms are investigated.The improved sparrow search algorithm(ISSA)utilizes Chebyshev chaotic mapping initialization,adaptive t-distribution,and the firefly algorithm to overcome the limitations of the basic algorithm,showing high accuracy,speed,and robustness in multi-modal problems.The grey wolf optimizer(GWO),inspired by the hunting behavior of grey wolves,has procedures for encircling,hunting,and attacking but may encounter local optima,and several improvement methods have been proposed.The genetic algorithm,based on the survival of the fittest principle,involves encoding,decoding,and other operations.Taking vehicle ABS control as an example,the genetic algorithm-based FOPID controller outperforms the traditional PID controller.In conclusion,different algorithms have their own advantages in FOPID parameter tuning,and the selection depends on system characteristics and control requirements.Future research can focus on further algorithm improvement and hybrid methods to achieve better control performance,providing a valuable reference for FOPID applications in industry.展开更多
This paper demonstrates the application of optimization techniques,namely the Dung Beetle Optimizer(DBO)and the Ant-Lion Optimizer(ALO),to enhance the performance of cascaded Proportional Integral Derivative(PID)and F...This paper demonstrates the application of optimization techniques,namely the Dung Beetle Optimizer(DBO)and the Ant-Lion Optimizer(ALO),to enhance the performance of cascaded Proportional Integral Derivative(PID)and Fractional Order PID(FOPID)controllers at the edge of an industrial network for Switched Reluctance Motor(SRM)speed control and torque ripple reduction.These techniques present notable advantages in terms of faster convergence and reduced computational complexity compared to existing optimization methods.Our research employs PID and FOPID controllers to regulate the speed and torque of the SRM,with a comparative analysis of other optimization approaches.In the domain of SRM control,we highlight the significance of the hysteresis band block in mitigating sudden state transitions,especially crucial for ensuring stable operation in the presence of noisy or slightly variable input signals requiring precise control.The results underscore the superior performance of the proposed optimization strategies,particularly showcasing the DBO-based cascaded PID and FOPID controllers,which exhibit reduced torque and current ripples along with improved speed response.Our investigation encompasses diverse loading conditions and is substantiated through time-domain simulations performed using MATLAB/SIMULINK.展开更多
为了提高光伏电池转换效率、降低能量损失,有必要研究最大功率点跟踪(maximum power point tracking,MPPT)方法。针对传统扰动观察法(perturbation observation method,P&O)存在无法兼顾跟踪速度与稳态精度、在光照度发生较大变化...为了提高光伏电池转换效率、降低能量损失,有必要研究最大功率点跟踪(maximum power point tracking,MPPT)方法。针对传统扰动观察法(perturbation observation method,P&O)存在无法兼顾跟踪速度与稳态精度、在光照度发生较大变化时会产生误判现象的问题,文中提出一种能适应环境变化的变步长P&O控制策略。首先,利用光伏电池刚启动时类似恒流源的特性获取当前光照度下的短路电流,通过固定电流法推导出最大功率点(maximum power point,MPP)的参考电压;其次,当光照度突变时,提出功率修正方法,并给出突变时的变步长调整策略;最后,设计基于线性扩张状态观测器(linear extended state observer,LESO)的分数阶比例积分微分(fractional order proportion integration differentiation,FOPID)控制器,可以对算法输出的参考电压进一步进行跟踪补偿。仿真结果表明,所提控制策略可以提高稳态精度和跟踪速度,有效提高光伏电池的输出功率。展开更多
文摘Efficient speed controllers for dynamic driving tasks in autonomous vehicles are crucial for ensuring safety and reliability.This study proposes a novel approach for designing a fractional order proportional-integral-derivative(FOPID)controller that utilizes a modified elite opposition-based artificial hummingbird algorithm(m-AHA)for optimal parameter tuning.Our approach outperforms existing optimization techniques on benchmark functions,and we demonstrate its effectiveness in controlling cruise control systems with increased flexibility and precision.Our study contributes to the advancement of autonomous vehicle technology by introducing a novel and efficient method for FOPID controller design that can enhance the driving experience while ensuring safety and reliability.We highlight the significance of our findings by demonstrating how our approach can improve the performance,safety,and reliability of autonomous vehicles.This study’s contributions are particularly relevant in the context of the growing demand for autonomous vehicles and the need for advanced control techniques to ensure their safe operation.Our research provides a promising avenue for further research and development in this area.
文摘Smart grids and their technologies transform the traditional electric grids to assure safe,secure,cost-effective,and reliable power transmission.Non-linear phenomena in power systems,such as voltage collapse and oscillatory phenomena,can be investigated by chaos theory.Recently,renewable energy resources,such as wind turbines,and solar photovoltaic(PV)arrays,have been widely used for electric power generation.The design of the controller for the direct Current(DC)converter in a PV system is performed based on the linearized model at an appropriate operating point.However,these operating points are everchanging in a PV system,and the design of the controller is usually accomplished based on a low irradiance level.This study designs a fractional-order proportional-integrated-derivative(FOPID)controller using deep learning(DL)with quasi-oppositional Archimedes Optimization algorithm(FOPID-QOAOA)for cascaded DC-DC converters in micro-grid applications.The presented FOPIDQOAOA model is designed to enhance the overall efficiency of the cascaded DC-DC boost converter.In addition,the proposed model develops a FOPID controller using a stacked sparse autoencoder(SSAE)model to regulate the converter output voltage.To tune the hyper-parameters related to the SSAE model,the QOAOA is derived by the including of the quasi-oppositional based learning(QOBL)with traditional AOA.Moreover,an objective function with the including of the integral of time multiplied by squared error(ITSE)is considered in this study.For validating the efficiency of the FOPID-QOAOA method,a sequence of simulations was performed under distinct aspects.A comparative study on cascaded buck and boost converters is carried out to authenticate the effectiveness and performance of the designed techniques.
文摘本文研究了在FOPID控制器控制下的广义Van Der Pol随机系统瞬态概率密度函数和可靠性函数变化情况.首先,引入广义谐和函数,将快变变量转换为慢变变量,并利用分数阶微积分的性质,获得了FOPID控制器在慢变变量形式下的新表达式.在此基础上,由于径向基神经网络具有准确性高,易于求解高维问题,求解速度快等优势,所以我们应用径向基神经网络分别对该随机系统所满足的前向和后向柯尔莫哥洛夫方程进行求解,得到随机系统的瞬态概率密度函数和可靠性函数.最后,通过分析控制器中分数阶导数和分数阶积分对Van Der Pol随机系统响应和可靠性的影响,我们得到结论,分数阶控制器一定程度上会增强系统的响应,并导致分岔.
文摘This paper delves into the parameter tuning of fractional-order PID(FOPID)controllers.FOPID controllers,with additional integral and derivative orders compared to traditional PID controllers,possess enhanced capabilities in handling complex systems.However,effective tuning of its five parameters is challenging.To address this,multiple intelligent algorithms are investigated.The improved sparrow search algorithm(ISSA)utilizes Chebyshev chaotic mapping initialization,adaptive t-distribution,and the firefly algorithm to overcome the limitations of the basic algorithm,showing high accuracy,speed,and robustness in multi-modal problems.The grey wolf optimizer(GWO),inspired by the hunting behavior of grey wolves,has procedures for encircling,hunting,and attacking but may encounter local optima,and several improvement methods have been proposed.The genetic algorithm,based on the survival of the fittest principle,involves encoding,decoding,and other operations.Taking vehicle ABS control as an example,the genetic algorithm-based FOPID controller outperforms the traditional PID controller.In conclusion,different algorithms have their own advantages in FOPID parameter tuning,and the selection depends on system characteristics and control requirements.Future research can focus on further algorithm improvement and hybrid methods to achieve better control performance,providing a valuable reference for FOPID applications in industry.
文摘This paper demonstrates the application of optimization techniques,namely the Dung Beetle Optimizer(DBO)and the Ant-Lion Optimizer(ALO),to enhance the performance of cascaded Proportional Integral Derivative(PID)and Fractional Order PID(FOPID)controllers at the edge of an industrial network for Switched Reluctance Motor(SRM)speed control and torque ripple reduction.These techniques present notable advantages in terms of faster convergence and reduced computational complexity compared to existing optimization methods.Our research employs PID and FOPID controllers to regulate the speed and torque of the SRM,with a comparative analysis of other optimization approaches.In the domain of SRM control,we highlight the significance of the hysteresis band block in mitigating sudden state transitions,especially crucial for ensuring stable operation in the presence of noisy or slightly variable input signals requiring precise control.The results underscore the superior performance of the proposed optimization strategies,particularly showcasing the DBO-based cascaded PID and FOPID controllers,which exhibit reduced torque and current ripples along with improved speed response.Our investigation encompasses diverse loading conditions and is substantiated through time-domain simulations performed using MATLAB/SIMULINK.
文摘为了提高光伏电池转换效率、降低能量损失,有必要研究最大功率点跟踪(maximum power point tracking,MPPT)方法。针对传统扰动观察法(perturbation observation method,P&O)存在无法兼顾跟踪速度与稳态精度、在光照度发生较大变化时会产生误判现象的问题,文中提出一种能适应环境变化的变步长P&O控制策略。首先,利用光伏电池刚启动时类似恒流源的特性获取当前光照度下的短路电流,通过固定电流法推导出最大功率点(maximum power point,MPP)的参考电压;其次,当光照度突变时,提出功率修正方法,并给出突变时的变步长调整策略;最后,设计基于线性扩张状态观测器(linear extended state observer,LESO)的分数阶比例积分微分(fractional order proportion integration differentiation,FOPID)控制器,可以对算法输出的参考电压进一步进行跟踪补偿。仿真结果表明,所提控制策略可以提高稳态精度和跟踪速度,有效提高光伏电池的输出功率。
