This paper investigates a time-varying anti-disturbance formation problem for a group of quadrotor aircrafts with time-varying uncertainties and a directed interaction topology.A novel Finite-Time Convergent Extended ...This paper investigates a time-varying anti-disturbance formation problem for a group of quadrotor aircrafts with time-varying uncertainties and a directed interaction topology.A novel Finite-Time Convergent Extended State Observer(FTCESO)based fully-distributed formation control scheme is proposed to enhance the disturbance rejection and the formation tracking performances for networked quadrotors.By adopting the hierarchical control strategy,the multiquadrotor system is separated into two subsystems:the outer-loop cooperative subsystem and the inner-loop attitude subsystem.In the outer-loop subsystem,with the estimation of disturbing forces and uncertain dynamics from FTCESOs,an adaptive consensus theory based cooperative controller is exploited to ensure the multiple quadrotors form and maintain a time-varying pattern relying only on the positions of the neighboring aircrafts.In the inner-loop subsystem,the desired attitude generated by the cooperative control law is stably tracked under a FTCESO-based attitude controller in a finite time.Based on a detailed algorithm to specify the cooperative control protocol,the feasibility condition to achieve the time-varying anti-disturbance formation tracking is derived and the rigorous analysis of the whole closed-loop multi-quadrotor system is given.Some numerical examples are conducted to intuitively demonstrate the effectiveness and the improvements of the proposed control framework.展开更多
The attitude tracking control problem for a satellite with parameter uncertainties and external disturbances is considered in this paper. For this class of multi-input multi-output uncertain nonlinear systems, a desig...The attitude tracking control problem for a satellite with parameter uncertainties and external disturbances is considered in this paper. For this class of multi-input multi-output uncertain nonlinear systems, a design method of robust output tracking controllers is proposed based on the upper-bounds of the uncertainties. Using the input/output feedback linearization approach and Lyapunov method, a control law is designed, which guarantees that the system output exponentially tracks the given desired output. The proposed controller is easy to compute and complement. Simulation results show that, in the closed-loop system, precise attitude control is accomplished in spite of the uncertainties in the system.展开更多
This paper proposes a novel high-efficiency generation technique for photovoltaic(PV) system,named maximum power point capturing(MPPC) technique. This is an aperiodic perturbation MPPC technique compared to the conven...This paper proposes a novel high-efficiency generation technique for photovoltaic(PV) system,named maximum power point capturing(MPPC) technique. This is an aperiodic perturbation MPPC technique compared to the conventional periodic perturbation maximum power point tracking technique. Firstly, under a closed-loop circuit and an open-loop circuit,the complete I-V curves and P-V curves are defined. Secondly, the proposed MPPC technique is based on the complete I-V curves and a practical model of solar PV systems.The proposed method realizes that maximum power point(MPP) is captured online, and its control strategy is designed to set a steady operating area around MPP. The duty cycle keeps constant when the operating point is within the steady operating area, i.e., aperiodic perturbation, and when the operating point is outside the steady operating area, MPPC is triggered to capture a new MPP with an updated steady operating area. Simulation results demonstrate that no oscillations exist in steady-state;dynamic performances are improved;and only two perturbations are required to capture the new MPP. Using the proposed MPPC method,low voltage ride through and high voltage ride through can be prevented.展开更多
Artificial Intelligence (AI) is transforming organizational dynamics, and revolutionizing corporate leadership practices. This research paper delves into the question of how AI influences corporate leadership, examini...Artificial Intelligence (AI) is transforming organizational dynamics, and revolutionizing corporate leadership practices. This research paper delves into the question of how AI influences corporate leadership, examining both its advantages and disadvantages. Positive impacts of AI are evident in communication, feedback systems, tracking mechanisms, and decision-making processes within organizations. AI-powered communication tools, as exemplified by Slack, facilitate seamless collaboration, transcending geographical barriers. Feedback systems, like Adobe’s Performance Management System, employ AI algorithms to provide personalized development opportunities, enhancing employee growth. AI-based tracking systems optimize resource allocation, as exemplified by studies like “AI-Based Tracking Systems: Enhancing Efficiency and Accountability.” Additionally, AI-powered decision support, demonstrated during the COVID-19 pandemic, showcases the capability to navigate complex challenges and maintain resilience. However, AI adoption poses challenges in human resources, potentially leading to job displacement and necessitating upskilling efforts. Managing AI errors becomes crucial, as illustrated by instances like Amazon’s biased recruiting tool. Data privacy concerns also arise, emphasizing the need for robust security measures. The proposed solution suggests leveraging Local Machine Learning Models (LLMs) to address data privacy issues. Approaches such as federated learning, on-device learning, differential privacy, and homomorphic encryption offer promising strategies. By exploring the evolving dynamics of AI and leadership, this research advocates for responsible AI adoption and proposes LLMs as a potential solution, fostering a balanced integration of AI benefits while mitigating associated risks in corporate settings.展开更多
A robust delay compensator has been developed for a class of uncertain nonlinear systems with an unknown constant input delay.The control law consists of feedback terms based on the integral of past control values and...A robust delay compensator has been developed for a class of uncertain nonlinear systems with an unknown constant input delay.The control law consists of feedback terms based on the integral of past control values and a novel filtered tracking error,capable of compensating for input delays.Suitable Lyapunov-Krasovskii functionals are used to prove global uniformly ultimately bounded(GUUB)tracking,provided certain sufficient gain conditions,dependent on the bound of the delay,are satisfied.Simulation results illustrate the performance and robustness of the controller for different values of input delay.展开更多
Energy production from renewable sources offers an efficient alternative non-polluting and sustainable solution. Among renewable energies, solar energy represents the most important source, the most efficient and the ...Energy production from renewable sources offers an efficient alternative non-polluting and sustainable solution. Among renewable energies, solar energy represents the most important source, the most efficient and the least expensive compared to other renewable sources. Electric power generation systems from the sun’s energy typically characterized by their low efficiency. However, it is known that photovoltaic pumping systems are the most economical solution especially in rural areas. This work deals with the modeling and the vector control of a solar photovoltaic (PV) pumping system. The main objective of this study is to improve optimization techniques that maximize the overall efficiency of the pumping system. In order to optimize their energy efficiency whatever, the weather conditions, we inserted between the inverter and the photovoltaic generator (GPV) a maximum power point adapter known as Maximum Power Point Tracking (MPPT). Among the various MPPT techniques presented in the literature, we adopted the adaptive neuro-fuzzy controller (ANFIS). In addition, the performance of the sliding vector control associated with the neural network was developed and evaluated. Finally, simulation work under Matlab / Simulink was achieved to examine the performance of a photovoltaic conversion chain intended for pumping and to verify the effectiveness of the speed control under various instructions applied to the system. According to the study, we have done on the improvement of sliding mode control with neural network. Note that the sliding-neuron control provides better results compared to other techniques in terms of improved chattering phenomenon and less deviation from its reference.展开更多
文摘This paper investigates a time-varying anti-disturbance formation problem for a group of quadrotor aircrafts with time-varying uncertainties and a directed interaction topology.A novel Finite-Time Convergent Extended State Observer(FTCESO)based fully-distributed formation control scheme is proposed to enhance the disturbance rejection and the formation tracking performances for networked quadrotors.By adopting the hierarchical control strategy,the multiquadrotor system is separated into two subsystems:the outer-loop cooperative subsystem and the inner-loop attitude subsystem.In the outer-loop subsystem,with the estimation of disturbing forces and uncertain dynamics from FTCESOs,an adaptive consensus theory based cooperative controller is exploited to ensure the multiple quadrotors form and maintain a time-varying pattern relying only on the positions of the neighboring aircrafts.In the inner-loop subsystem,the desired attitude generated by the cooperative control law is stably tracked under a FTCESO-based attitude controller in a finite time.Based on a detailed algorithm to specify the cooperative control protocol,the feasibility condition to achieve the time-varying anti-disturbance formation tracking is derived and the rigorous analysis of the whole closed-loop multi-quadrotor system is given.Some numerical examples are conducted to intuitively demonstrate the effectiveness and the improvements of the proposed control framework.
文摘The attitude tracking control problem for a satellite with parameter uncertainties and external disturbances is considered in this paper. For this class of multi-input multi-output uncertain nonlinear systems, a design method of robust output tracking controllers is proposed based on the upper-bounds of the uncertainties. Using the input/output feedback linearization approach and Lyapunov method, a control law is designed, which guarantees that the system output exponentially tracks the given desired output. The proposed controller is easy to compute and complement. Simulation results show that, in the closed-loop system, precise attitude control is accomplished in spite of the uncertainties in the system.
基金supported in part by Australian Research Council (ARC) Discovery Project (No. DP170104426)
文摘This paper proposes a novel high-efficiency generation technique for photovoltaic(PV) system,named maximum power point capturing(MPPC) technique. This is an aperiodic perturbation MPPC technique compared to the conventional periodic perturbation maximum power point tracking technique. Firstly, under a closed-loop circuit and an open-loop circuit,the complete I-V curves and P-V curves are defined. Secondly, the proposed MPPC technique is based on the complete I-V curves and a practical model of solar PV systems.The proposed method realizes that maximum power point(MPP) is captured online, and its control strategy is designed to set a steady operating area around MPP. The duty cycle keeps constant when the operating point is within the steady operating area, i.e., aperiodic perturbation, and when the operating point is outside the steady operating area, MPPC is triggered to capture a new MPP with an updated steady operating area. Simulation results demonstrate that no oscillations exist in steady-state;dynamic performances are improved;and only two perturbations are required to capture the new MPP. Using the proposed MPPC method,low voltage ride through and high voltage ride through can be prevented.
文摘Artificial Intelligence (AI) is transforming organizational dynamics, and revolutionizing corporate leadership practices. This research paper delves into the question of how AI influences corporate leadership, examining both its advantages and disadvantages. Positive impacts of AI are evident in communication, feedback systems, tracking mechanisms, and decision-making processes within organizations. AI-powered communication tools, as exemplified by Slack, facilitate seamless collaboration, transcending geographical barriers. Feedback systems, like Adobe’s Performance Management System, employ AI algorithms to provide personalized development opportunities, enhancing employee growth. AI-based tracking systems optimize resource allocation, as exemplified by studies like “AI-Based Tracking Systems: Enhancing Efficiency and Accountability.” Additionally, AI-powered decision support, demonstrated during the COVID-19 pandemic, showcases the capability to navigate complex challenges and maintain resilience. However, AI adoption poses challenges in human resources, potentially leading to job displacement and necessitating upskilling efforts. Managing AI errors becomes crucial, as illustrated by instances like Amazon’s biased recruiting tool. Data privacy concerns also arise, emphasizing the need for robust security measures. The proposed solution suggests leveraging Local Machine Learning Models (LLMs) to address data privacy issues. Approaches such as federated learning, on-device learning, differential privacy, and homomorphic encryption offer promising strategies. By exploring the evolving dynamics of AI and leadership, this research advocates for responsible AI adoption and proposes LLMs as a potential solution, fostering a balanced integration of AI benefits while mitigating associated risks in corporate settings.
文摘A robust delay compensator has been developed for a class of uncertain nonlinear systems with an unknown constant input delay.The control law consists of feedback terms based on the integral of past control values and a novel filtered tracking error,capable of compensating for input delays.Suitable Lyapunov-Krasovskii functionals are used to prove global uniformly ultimately bounded(GUUB)tracking,provided certain sufficient gain conditions,dependent on the bound of the delay,are satisfied.Simulation results illustrate the performance and robustness of the controller for different values of input delay.
文摘Energy production from renewable sources offers an efficient alternative non-polluting and sustainable solution. Among renewable energies, solar energy represents the most important source, the most efficient and the least expensive compared to other renewable sources. Electric power generation systems from the sun’s energy typically characterized by their low efficiency. However, it is known that photovoltaic pumping systems are the most economical solution especially in rural areas. This work deals with the modeling and the vector control of a solar photovoltaic (PV) pumping system. The main objective of this study is to improve optimization techniques that maximize the overall efficiency of the pumping system. In order to optimize their energy efficiency whatever, the weather conditions, we inserted between the inverter and the photovoltaic generator (GPV) a maximum power point adapter known as Maximum Power Point Tracking (MPPT). Among the various MPPT techniques presented in the literature, we adopted the adaptive neuro-fuzzy controller (ANFIS). In addition, the performance of the sliding vector control associated with the neural network was developed and evaluated. Finally, simulation work under Matlab / Simulink was achieved to examine the performance of a photovoltaic conversion chain intended for pumping and to verify the effectiveness of the speed control under various instructions applied to the system. According to the study, we have done on the improvement of sliding mode control with neural network. Note that the sliding-neuron control provides better results compared to other techniques in terms of improved chattering phenomenon and less deviation from its reference.
