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
应用于孤岛型微电网以实现频率控制功能的传统控制器多为分数阶PID(fractional order PID,FOPID)控制器及模糊分数阶PID(fuzzy fractional order PID,FFOPID)控制器,二者的控制性能均存在局限性。针对这一问题,设计了一种变论域混合FFO...应用于孤岛型微电网以实现频率控制功能的传统控制器多为分数阶PID(fractional order PID,FOPID)控制器及模糊分数阶PID(fuzzy fractional order PID,FFOPID)控制器,二者的控制性能均存在局限性。针对这一问题,设计了一种变论域混合FFOPID控制器,用于提高孤岛微电网的频率控制性能。通过对比FOPID、FFOPID以及变论域混合FFOPID3种控制器作用时的不同效果,证明了变论域混合FFOPID控制器相比其他控制器对于孤岛微电网的频率控制有着更好的控制性能。同时考虑了反馈信号受到测量噪声干扰时对控制器的控制性能产生影响进而使得孤岛微电网频率波动增大的情况,并针对此问题使用了动态数据校正(dynamic datareconciliation,DDR)滤波技术。通过对比时域仿真中FOPID、FFOPID以及变论域混合FFOPID控制器各自作用时孤岛微电网频率偏差的输出结果,验证了DDR滤波技术对孤岛微电网的频率控制的显著效果。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.
文摘应用于孤岛型微电网以实现频率控制功能的传统控制器多为分数阶PID(fractional order PID,FOPID)控制器及模糊分数阶PID(fuzzy fractional order PID,FFOPID)控制器,二者的控制性能均存在局限性。针对这一问题,设计了一种变论域混合FFOPID控制器,用于提高孤岛微电网的频率控制性能。通过对比FOPID、FFOPID以及变论域混合FFOPID3种控制器作用时的不同效果,证明了变论域混合FFOPID控制器相比其他控制器对于孤岛微电网的频率控制有着更好的控制性能。同时考虑了反馈信号受到测量噪声干扰时对控制器的控制性能产生影响进而使得孤岛微电网频率波动增大的情况,并针对此问题使用了动态数据校正(dynamic datareconciliation,DDR)滤波技术。通过对比时域仿真中FOPID、FFOPID以及变论域混合FFOPID控制器各自作用时孤岛微电网频率偏差的输出结果,验证了DDR滤波技术对孤岛微电网的频率控制的显著效果。
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。
