A new adaptive quasi-sliding mode control algorithm is developed for a class of nonlinear discrete-time systems, which is especially useful for nonlinear systems with vaguely known dynamics. This design is model-free,...A new adaptive quasi-sliding mode control algorithm is developed for a class of nonlinear discrete-time systems, which is especially useful for nonlinear systems with vaguely known dynamics. This design is model-free, and is based directly on pseudo-partial-derivatives derived on-line from the input and output information of the system using an improved recursive projection type of identification algorithm. The theoretical analysis and simulation results show that the adaptive quasi-sliding mode control system is stable and convergent.展开更多
This paper presents an experimental study to compare the performance of model-free control strategies for pneumatic soft robots.Fabricated using soft materials,soft robots have gained much attention in academia and in...This paper presents an experimental study to compare the performance of model-free control strategies for pneumatic soft robots.Fabricated using soft materials,soft robots have gained much attention in academia and industry during recent years because of their inherent safety in human interaction.However,due to structural flexibility and compliance,mathematical models for these soft robots are nonlinear with an infinite degree of freedom(DOF).Therefore,accurate position(or orientation)control and optimization of their dynamic response remains a challenging task.Most existing soft robots currently employed in industrial and rehabilitation applications use model-free control algorithms such as PID.However,to the best of our knowledge,there has been no systematic study on the comparative performance of model-free control algorithms and their ability to optimize dynamic response,i.e.,reduce overshoot and settling time.In this paper,we present comparative performance of several variants of model-free PID-controllers based on extensive experimental results.Additionally,most of the existing work on modelfree control in pneumatic soft-robotic literature use manually tuned parameters,which is a time-consuming,labor-intensive task.We present a heuristic-based coordinate descent algorithm to tune the controller parameter automatically.We presented results for both manual tuning and automatic tuning using the Ziegler-Nichols method and proposed algorithm,respectively.We then used experimental results to statistically demonstrate that the presented automatic tuning algorithm results in high accuracy.The experiment results show that for soft robots,the PID-controller essentially reduces to the PI controller.This behavior was observed in both manual and automatic tuning experiments;we also discussed a rationale for removing the derivative term.展开更多
The rapid growth of distributed generator(DG)capacities has introduced additional controllable assets to improve the performance of distribution systems in terms of service restoration.Renewable DGs are of particular ...The rapid growth of distributed generator(DG)capacities has introduced additional controllable assets to improve the performance of distribution systems in terms of service restoration.Renewable DGs are of particular interest to utility companies,but the stochastic nature of intermittent renewable DGs could have a negative impact on the electric grid if they are not properly handled.In this study,we investigate distribution system service restoration using DGs as the primary power source,and we develop an effective approach to handle the uncertainty of renewable DGs under extreme conditions.The distribution system service restoration problem can be described as a mixed-integer second-order cone programming model by modifying the radial topology constraints and power flow equations.The uncertainty of renewable DGs will be modeled using a chance-constrained approach.Furthermore,the forecast errors and noises in real-time operation are solved using a novel model-free control algorithm that can automatically track the trajectory of real-time DG output.The proposed service restoration strategy and model-free control algorithm are validated using an IEEE 123-bus test system.展开更多
This paper introduces a model-free reinforcement learning technique that is used to solve a class of dynamic games known as dynamic graphical games. The graphical game results from to make all the agents synchronize t...This paper introduces a model-free reinforcement learning technique that is used to solve a class of dynamic games known as dynamic graphical games. The graphical game results from to make all the agents synchronize to the state of a command multi-agent dynamical systems, where pinning control is used generator or a leader agent. Novel coupled Bellman equations and Hamiltonian functions are developed for the dynamic graphical games. The Hamiltonian mechanics are used to derive the necessary conditions for optimality. The solution for the dynamic graphical game is given in terms of the solution to a set of coupled Hamilton-Jacobi-Bellman equations developed herein. Nash equilibrium solution for the graphical game is given in terms of the solution to the underlying coupled Hamilton-Jacobi-Bellman equations. An online model-free policy iteration algorithm is developed to learn the Nash solution for the dynamic graphical game. This algorithm does not require any knowledge of the agents＇ dynamics. A proof of convergence for this multi-agent learning algorithm is given under mild assumption about the inter-connectivity properties of the graph. A gradient descent technique with critic network structures is used to implement the policy iteration algorithm to solve the graphical game online in real-time.展开更多
Aiming at the robustness issue in high-speed trains(HSTs)operation control,this article proposes a model-free adaptive control(MFAC)scheme to suppress disturbance.Firstly,the dynamic linearization data model of train ...Aiming at the robustness issue in high-speed trains(HSTs)operation control,this article proposes a model-free adaptive control(MFAC)scheme to suppress disturbance.Firstly,the dynamic linearization data model of train system under the action of measurement disturbance is given,and the Kalman filter(KF)based on this model is derived under the minimum variance estimation criterion.Then,according to the KF,an anti-interference MFAC scheme is designed.This scheme only needs the input and output data of the controlled system to realize the MFAC of the train under strong disturbance.Finally,the simulation experiment of CRH380A HSTs is carried out and compared with the traditional MFAC and the MFAC with attenuation factor.The proposed control algorithm can effectively suppress the measurement disturbance,and obtain smaller tracking error and larger signal to noise ratio with better applicability.展开更多
Efficient thermal management of lithium-ion battery,working under extremely rapid charging-discharging,is of widespread interest to avoid the battery degradation due to temperature rise,resulting in the enhanced lifes...Efficient thermal management of lithium-ion battery,working under extremely rapid charging-discharging,is of widespread interest to avoid the battery degradation due to temperature rise,resulting in the enhanced lifespan.Herein,thermal management of lithium-ion battery has been performed via a liquid cooling theoretical model integrated with thermoelectric model of battery packs and single-phase heat transfer.Aiming to alleviate the battery temperature fluctuation by automatically manipulating the flow rate of working fluid,a nominal model-free controller,i.e.,fuzzy logic controller is designed.An optimized on-off controller based on pump speed optimization is introduced to serve as the comparative controller.Thermal control simulations are conducted under regular operating and extreme operating conditions,and two controllers are applied to control battery temperature with proper intervals which is conducive to enhance the battery charge-discharge efficiency.The results indicate that,for any operating condition,the fuzzy logic controller shows excellence in terms of the tracking accuracy of set-point of battery temperature.Thanks to the establishment of fuzzy set and fuzzy behavioral rules,the battery temperature has been throughout maintained near the set point,and the temperature fluctuation amplitude is highly reduced,with better temperature control accuracy of~0.2℃(regular condition)and~0.5℃(extreme condition)compared with~1.1℃(regular condition)and~1.6℃(extreme condition)of optimized on-off controller.While in the case of extreme operating condition,the proposed optimized on-off controller manifests the hysteresis in temperature fluctuation,which is ascribed to the set of dead-band for the feedback temperature.The simulation results cast new light on the utilization and development of model-free temperature controller for the thermal management of lithium-ion battery.展开更多
In this paper,the distributed optimization problem is investigated for a class of general nonlinear model-free multi-agent systems.The dynamical model of each agent is unknown and only the input/output data are availa...In this paper,the distributed optimization problem is investigated for a class of general nonlinear model-free multi-agent systems.The dynamical model of each agent is unknown and only the input/output data are available.A model-free adaptive control method is employed,by which the original unknown nonlinear system is equivalently converted into a dynamic linearized model.An event-triggered consensus scheme is developed to guarantee that the consensus error of the outputs of all agents is convergent.Then,by means of the distributed gradient descent method,a novel event-triggered model-free adaptive distributed optimization algorithm is put forward.Sufficient conditions are established to ensure the consensus and optimality of the addressed system.Finally,simulation results are provided to validate the effectiveness of the proposed approach.展开更多
文摘A new adaptive quasi-sliding mode control algorithm is developed for a class of nonlinear discrete-time systems, which is especially useful for nonlinear systems with vaguely known dynamics. This design is model-free, and is based directly on pseudo-partial-derivatives derived on-line from the input and output information of the system using an improved recursive projection type of identification algorithm. The theoretical analysis and simulation results show that the adaptive quasi-sliding mode control system is stable and convergent.
文摘This paper presents an experimental study to compare the performance of model-free control strategies for pneumatic soft robots.Fabricated using soft materials,soft robots have gained much attention in academia and industry during recent years because of their inherent safety in human interaction.However,due to structural flexibility and compliance,mathematical models for these soft robots are nonlinear with an infinite degree of freedom(DOF).Therefore,accurate position(or orientation)control and optimization of their dynamic response remains a challenging task.Most existing soft robots currently employed in industrial and rehabilitation applications use model-free control algorithms such as PID.However,to the best of our knowledge,there has been no systematic study on the comparative performance of model-free control algorithms and their ability to optimize dynamic response,i.e.,reduce overshoot and settling time.In this paper,we present comparative performance of several variants of model-free PID-controllers based on extensive experimental results.Additionally,most of the existing work on modelfree control in pneumatic soft-robotic literature use manually tuned parameters,which is a time-consuming,labor-intensive task.We present a heuristic-based coordinate descent algorithm to tune the controller parameter automatically.We presented results for both manual tuning and automatic tuning using the Ziegler-Nichols method and proposed algorithm,respectively.We then used experimental results to statistically demonstrate that the presented automatic tuning algorithm results in high accuracy.The experiment results show that for soft robots,the PID-controller essentially reduces to the PI controller.This behavior was observed in both manual and automatic tuning experiments;we also discussed a rationale for removing the derivative term.
基金the National Renewable Energy Laboratory(NREL)operated by Alliance for Sustainable Energy,LLC,for the U.S.Department of Energy(DOE)under Contract No.DE-AC36-08GO28308the U.S.Department of Energy Office of Electricity AOP Distribution Grid Resilience Project.The views expressed in the article do not necessarily represent the views of the DOE or the U.S.Government.The U.S.Government retains and the publisher,by accepting the article for publication,acknowledges that the U.S.Government retains a nonexclusive,paid-up,irrevocable,worldwide license to publish or reproduce the published form of this work,or allow others to do so,for U.S.Government purposes.
文摘The rapid growth of distributed generator(DG)capacities has introduced additional controllable assets to improve the performance of distribution systems in terms of service restoration.Renewable DGs are of particular interest to utility companies,but the stochastic nature of intermittent renewable DGs could have a negative impact on the electric grid if they are not properly handled.In this study,we investigate distribution system service restoration using DGs as the primary power source,and we develop an effective approach to handle the uncertainty of renewable DGs under extreme conditions.The distribution system service restoration problem can be described as a mixed-integer second-order cone programming model by modifying the radial topology constraints and power flow equations.The uncertainty of renewable DGs will be modeled using a chance-constrained approach.Furthermore,the forecast errors and noises in real-time operation are solved using a novel model-free control algorithm that can automatically track the trajectory of real-time DG output.The proposed service restoration strategy and model-free control algorithm are validated using an IEEE 123-bus test system.
基金supported by the Deanship of Scientific Research at King Fahd University of Petroleum & Minerals Project(No.JF141002)the National Science Foundation(No.ECCS-1405173)+3 种基金the Office of Naval Research(Nos.N000141310562,N000141410718)the U.S. Army Research Office(No.W911NF-11-D-0001)the National Natural Science Foundation of China(No.61120106011)the Project 111 from the Ministry of Education of China(No.B08015)
文摘This paper introduces a model-free reinforcement learning technique that is used to solve a class of dynamic games known as dynamic graphical games. The graphical game results from to make all the agents synchronize to the state of a command multi-agent dynamical systems, where pinning control is used generator or a leader agent. Novel coupled Bellman equations and Hamiltonian functions are developed for the dynamic graphical games. The Hamiltonian mechanics are used to derive the necessary conditions for optimality. The solution for the dynamic graphical game is given in terms of the solution to a set of coupled Hamilton-Jacobi-Bellman equations developed herein. Nash equilibrium solution for the graphical game is given in terms of the solution to the underlying coupled Hamilton-Jacobi-Bellman equations. An online model-free policy iteration algorithm is developed to learn the Nash solution for the dynamic graphical game. This algorithm does not require any knowledge of the agents＇ dynamics. A proof of convergence for this multi-agent learning algorithm is given under mild assumption about the inter-connectivity properties of the graph. A gradient descent technique with critic network structures is used to implement the policy iteration algorithm to solve the graphical game online in real-time.
基金The authors thank the anonymous reviewers for their valuable suggestions.This work is supported by funds National Natural Science Foundation of China(Grants No.52162048,61991404 and 62003138)National Key Research and Development Program of China(Grant No.2020YFB1713703)Jiangxi Graduate Innovation Fund Project(Grant No.YC2021-S446).
文摘Aiming at the robustness issue in high-speed trains(HSTs)operation control,this article proposes a model-free adaptive control(MFAC)scheme to suppress disturbance.Firstly,the dynamic linearization data model of train system under the action of measurement disturbance is given,and the Kalman filter(KF)based on this model is derived under the minimum variance estimation criterion.Then,according to the KF,an anti-interference MFAC scheme is designed.This scheme only needs the input and output data of the controlled system to realize the MFAC of the train under strong disturbance.Finally,the simulation experiment of CRH380A HSTs is carried out and compared with the traditional MFAC and the MFAC with attenuation factor.The proposed control algorithm can effectively suppress the measurement disturbance,and obtain smaller tracking error and larger signal to noise ratio with better applicability.
基金supported by the National Key R&D Program of China(2021YFB3803200)the National Natural Science Foundation of China(Grant No.U2241253)。
文摘Efficient thermal management of lithium-ion battery,working under extremely rapid charging-discharging,is of widespread interest to avoid the battery degradation due to temperature rise,resulting in the enhanced lifespan.Herein,thermal management of lithium-ion battery has been performed via a liquid cooling theoretical model integrated with thermoelectric model of battery packs and single-phase heat transfer.Aiming to alleviate the battery temperature fluctuation by automatically manipulating the flow rate of working fluid,a nominal model-free controller,i.e.,fuzzy logic controller is designed.An optimized on-off controller based on pump speed optimization is introduced to serve as the comparative controller.Thermal control simulations are conducted under regular operating and extreme operating conditions,and two controllers are applied to control battery temperature with proper intervals which is conducive to enhance the battery charge-discharge efficiency.The results indicate that,for any operating condition,the fuzzy logic controller shows excellence in terms of the tracking accuracy of set-point of battery temperature.Thanks to the establishment of fuzzy set and fuzzy behavioral rules,the battery temperature has been throughout maintained near the set point,and the temperature fluctuation amplitude is highly reduced,with better temperature control accuracy of~0.2℃(regular condition)and~0.5℃(extreme condition)compared with~1.1℃(regular condition)and~1.6℃(extreme condition)of optimized on-off controller.While in the case of extreme operating condition,the proposed optimized on-off controller manifests the hysteresis in temperature fluctuation,which is ascribed to the set of dead-band for the feedback temperature.The simulation results cast new light on the utilization and development of model-free temperature controller for the thermal management of lithium-ion battery.
基金Project supported by the National Natural Science Foundation of China(No.62003213)。
文摘In this paper,the distributed optimization problem is investigated for a class of general nonlinear model-free multi-agent systems.The dynamical model of each agent is unknown and only the input/output data are available.A model-free adaptive control method is employed,by which the original unknown nonlinear system is equivalently converted into a dynamic linearized model.An event-triggered consensus scheme is developed to guarantee that the consensus error of the outputs of all agents is convergent.Then,by means of the distributed gradient descent method,a novel event-triggered model-free adaptive distributed optimization algorithm is put forward.Sufficient conditions are established to ensure the consensus and optimality of the addressed system.Finally,simulation results are provided to validate the effectiveness of the proposed approach.
