This paper proposes a distributed control method based on the differential flatness(DF) property of robot swarms. The swarm DF mapping is established for underactuated differentially flat dynamics, according to the co...This paper proposes a distributed control method based on the differential flatness(DF) property of robot swarms. The swarm DF mapping is established for underactuated differentially flat dynamics, according to the control objective. The DF mapping refers to the fact that the system state and input of each robot can be derived algebraically from the flat outputs of the leaders and the cooperative errors and their finite order derivatives. Based on the proposed swarm DF mapping, a distributed controller is designed. The distributed implementation of swarm DF mapping is achieved through observer design. The effectiveness of the proposed method is validated through a numerical simulation of quadrotor swarm synchronization.展开更多
This paper proposes a scheme of trajectory tracking control for the hovercraft.Since the model of the hovercraft is under-actuated,nonlinear,and strongly coupled,it is a great challenge for the controller design.To so...This paper proposes a scheme of trajectory tracking control for the hovercraft.Since the model of the hovercraft is under-actuated,nonlinear,and strongly coupled,it is a great challenge for the controller design.To solve this problem,the control scheme is divided into two parts.Firstly,we employ differential flatness method to find a set of flat outputs and consider part of the nonlinear terms as uncertainties.Consequently,we convert the under-actuated system into a full-actuated one.Secondly,a reinforcement learning-based active disturbance rejection controller(RL-ADRC)is designed.In this method,an extended state observer(ESO)is designed to estimate the uncertainties of the system,and an actorcritic-based reinforcement learning(RL)algorithm is used to approximate the optimal control strategy.Based on the output of the ESO,the RL-ADRC compensates for the total uncertainties in real-time,and simultaneously,generates the optimal control strategy by RL algorithm.Simulation results show that,compared with the traditional ADRC method,RL-ADRC does not need to manually tune the controller parameters,and the control strategy is more robust.展开更多
Underactuated mechanical system has less independent inputs than the degrees of freedom（DOF） of the mechanism. The energy efficiency of this class of mechanical systems is an essential problem in practice. On the ba...Underactuated mechanical system has less independent inputs than the degrees of freedom（DOF） of the mechanism. The energy efficiency of this class of mechanical systems is an essential problem in practice. On the basis of the sufficient and necessary condition that concludes a single input nonlinear system is differentially flat, it is shown that the flat output of the single input underactuated mechanical system can be obtained by finding a smooth output function such that the relative degree of the system equals to the dimension of the state space. If the flat output of the underactuated system can be solved explicitly, and by constructing a smooth curve with satisfying given boundary conditions in fiat output space, an energy efficiency optimization method is proposed for the motion planning of the differentially flat underactuated mechanical systems. The inertia wheel pendulum is used to verify the proposed optimization method, and some numerical simulations show that the presented optimal motion planning method can efficaciously reduce the energy cost for given control tasks.展开更多
Dynamic soaring,inspired by the wind-riding flight of birds such as albatrosses,is a biomimetic technique which leverages wind fields to enhance the endurance of unmanned aerial vehicles(UAVs).Achieving a precise soar...Dynamic soaring,inspired by the wind-riding flight of birds such as albatrosses,is a biomimetic technique which leverages wind fields to enhance the endurance of unmanned aerial vehicles(UAVs).Achieving a precise soaring trajectory is crucial for maximizing energy efficiency during flight.Existing nonlinear programming methods are heavily dependent on the choice of initial values which is hard to determine.Therefore,this paper introduces a deep reinforcement learning method based on a differentially flat model for dynamic soaring trajectory planning and optimization.Initially,the gliding trajectory is parameterized using Fourier basis functions,achieving a flexible trajectory representation with a minimal number of hyperparameters.Subsequently,the trajectory optimization problem is formulated as a dynamic interactive process of Markov decision-making.The hyperparameters of the trajectory are optimized using the Proximal Policy Optimization(PPO2)algorithm from deep reinforcement learning(DRL),reducing the strong reliance on initial value settings in the optimization process.Finally,a comparison between the proposed method and the nonlinear programming method reveals that the trajectory generated by the proposed approach is smoother while meeting the same performance requirements.Specifically,the proposed method achieves a 34%reduction in maximum thrust,a 39.4%decrease in maximum thrust difference,and a 33%reduction in maximum airspeed difference.展开更多
A nonlinear optimal(H-infinity)control method is developed for a wind power unit that comprises twin turbines,permanent magnet synchronous generators(PMSGs)and AC/DC converters.By proving differential flatness propert...A nonlinear optimal(H-infinity)control method is developed for a wind power unit that comprises twin turbines,permanent magnet synchronous generators(PMSGs)and AC/DC converters.By proving differential flatness properties for this system the associated setpoints definition problem is solved.The dynamic model of the wind power unit being initially expressed in a nonlinear and multivariable state-space form,undergoes approximate linearisation around a temporary operating point that is recomputed at each time-step of the control method.The linearisation relies on first-order Taylor series expansion and on the computation of the associated Jacobian matrices.For the linearised state-space model of the wind power unit,a stabilising optimal(H-infinity)feedback controller is designed.This controller stands for the solution to the nonlinear optimal control problem of the wind power unit under model uncertainty and external perturbations.To compute the controller's feedback gains an algebraic Riccati equation is repetitively solved at each iteration of the control algorithm.The global stability properties of the control method are proven through Lyapunov analysis.Finally,to implement state estimationbased control of the wind power unit,without the need to measure its entire state vector,the H-infinity Kalman Filter is used as a robust state estimator.展开更多
The control problem for the multivariable and nonlinear dynamics of unmanned aerial vehicles and micro-satellites is solved with the use of a flatness-based control approach which is implemented in successive loops.Th...The control problem for the multivariable and nonlinear dynamics of unmanned aerial vehicles and micro-satellites is solved with the use of a flatness-based control approach which is implemented in successive loops.The state-space model of(i)unmanned aerial vehicles and(ii)micro-satellites is separated into two subsystems,which are connected between them in cascading loops.Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input–output linearized flat systems.The state variables of the second subsystem become virtual control inputs for the first subsystem.In turn,exogenous control inputs are applied to the first subsystem.The whole control method is implemented in two successive loops and its global stability properties are also proven through Lyapunov stability analysis.The validity of the control method is confirmed in two case studies:(a)control and trajectories tracking for the autonomous octocopter,(ii)control of the attitude dynamics of micro-satellites.展开更多
The overuse and misuse of antibiotics has become a major problem for public health.People become resistant to antibiotics and because of this the anticipated therapeutic effect is never reached.In-hospital infections ...The overuse and misuse of antibiotics has become a major problem for public health.People become resistant to antibiotics and because of this the anticipated therapeutic effect is never reached.In-hospital infections are often aggravated and large amounts of money are spent for treating complications in the patients'condition.In this paper a nonlinear optimal(H-infinity)control method is developed for the dynamic model of bacterial infections exhibiting resistance to antibiotics.First,differential flatness properties are proven for the associated state-space model.Next,the state-space description undergoes approximate linearization with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrices.The linearization process takes place at each sampling instance around a time-varying operating point which is defined by the present value of the system's state vector and by the last sampled value of the control inputs vector.For the approximately linearized model of the system a stabilizing H-infinity feedback controller is designed.To compute the controller's gains an algebraic Riccati equation has to be repetitively solved at each time-step of the control algorithm.The global stability properties of the control scheme are proven through Lyapunov analysis.The proposed method achieves stabilization and remedy for the bacterial infection under moderate use of antibiotics.展开更多
基金Project supported by the National Natural Science Foundation of China (Nos. 62373025, 12332004,62003013, and 11932003)。
文摘This paper proposes a distributed control method based on the differential flatness(DF) property of robot swarms. The swarm DF mapping is established for underactuated differentially flat dynamics, according to the control objective. The DF mapping refers to the fact that the system state and input of each robot can be derived algebraically from the flat outputs of the leaders and the cooperative errors and their finite order derivatives. Based on the proposed swarm DF mapping, a distributed controller is designed. The distributed implementation of swarm DF mapping is achieved through observer design. The effectiveness of the proposed method is validated through a numerical simulation of quadrotor swarm synchronization.
基金This paper was supported by the National Natural Science Foundation of China under Grant No.61720106010.
文摘This paper proposes a scheme of trajectory tracking control for the hovercraft.Since the model of the hovercraft is under-actuated,nonlinear,and strongly coupled,it is a great challenge for the controller design.To solve this problem,the control scheme is divided into two parts.Firstly,we employ differential flatness method to find a set of flat outputs and consider part of the nonlinear terms as uncertainties.Consequently,we convert the under-actuated system into a full-actuated one.Secondly,a reinforcement learning-based active disturbance rejection controller(RL-ADRC)is designed.In this method,an extended state observer(ESO)is designed to estimate the uncertainties of the system,and an actorcritic-based reinforcement learning(RL)algorithm is used to approximate the optimal control strategy.Based on the output of the ESO,the RL-ADRC compensates for the total uncertainties in real-time,and simultaneously,generates the optimal control strategy by RL algorithm.Simulation results show that,compared with the traditional ADRC method,RL-ADRC does not need to manually tune the controller parameters,and the control strategy is more robust.
基金supported by National Natural Science Foundation of China (Grant No. 50475177)Beijing Municipal Natural Science Foundation, China (Grant No. 3062009)Funding Project for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality, China (Grant No. PHR200906107).
文摘Underactuated mechanical system has less independent inputs than the degrees of freedom（DOF） of the mechanism. The energy efficiency of this class of mechanical systems is an essential problem in practice. On the basis of the sufficient and necessary condition that concludes a single input nonlinear system is differentially flat, it is shown that the flat output of the single input underactuated mechanical system can be obtained by finding a smooth output function such that the relative degree of the system equals to the dimension of the state space. If the flat output of the underactuated system can be solved explicitly, and by constructing a smooth curve with satisfying given boundary conditions in fiat output space, an energy efficiency optimization method is proposed for the motion planning of the differentially flat underactuated mechanical systems. The inertia wheel pendulum is used to verify the proposed optimization method, and some numerical simulations show that the presented optimal motion planning method can efficaciously reduce the energy cost for given control tasks.
基金support received by the National Natural Science Foundation of China(Grant Nos.52372398&62003272).
文摘Dynamic soaring,inspired by the wind-riding flight of birds such as albatrosses,is a biomimetic technique which leverages wind fields to enhance the endurance of unmanned aerial vehicles(UAVs).Achieving a precise soaring trajectory is crucial for maximizing energy efficiency during flight.Existing nonlinear programming methods are heavily dependent on the choice of initial values which is hard to determine.Therefore,this paper introduces a deep reinforcement learning method based on a differentially flat model for dynamic soaring trajectory planning and optimization.Initially,the gliding trajectory is parameterized using Fourier basis functions,achieving a flexible trajectory representation with a minimal number of hyperparameters.Subsequently,the trajectory optimization problem is formulated as a dynamic interactive process of Markov decision-making.The hyperparameters of the trajectory are optimized using the Proximal Policy Optimization(PPO2)algorithm from deep reinforcement learning(DRL),reducing the strong reliance on initial value settings in the optimization process.Finally,a comparison between the proposed method and the nonlinear programming method reveals that the trajectory generated by the proposed approach is smoother while meeting the same performance requirements.Specifically,the proposed method achieves a 34%reduction in maximum thrust,a 39.4%decrease in maximum thrust difference,and a 33%reduction in maximum airspeed difference.
文摘A nonlinear optimal(H-infinity)control method is developed for a wind power unit that comprises twin turbines,permanent magnet synchronous generators(PMSGs)and AC/DC converters.By proving differential flatness properties for this system the associated setpoints definition problem is solved.The dynamic model of the wind power unit being initially expressed in a nonlinear and multivariable state-space form,undergoes approximate linearisation around a temporary operating point that is recomputed at each time-step of the control method.The linearisation relies on first-order Taylor series expansion and on the computation of the associated Jacobian matrices.For the linearised state-space model of the wind power unit,a stabilising optimal(H-infinity)feedback controller is designed.This controller stands for the solution to the nonlinear optimal control problem of the wind power unit under model uncertainty and external perturbations.To compute the controller's feedback gains an algebraic Riccati equation is repetitively solved at each iteration of the control algorithm.The global stability properties of the control method are proven through Lyapunov analysis.Finally,to implement state estimationbased control of the wind power unit,without the need to measure its entire state vector,the H-infinity Kalman Filter is used as a robust state estimator.
文摘The control problem for the multivariable and nonlinear dynamics of unmanned aerial vehicles and micro-satellites is solved with the use of a flatness-based control approach which is implemented in successive loops.The state-space model of(i)unmanned aerial vehicles and(ii)micro-satellites is separated into two subsystems,which are connected between them in cascading loops.Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input–output linearized flat systems.The state variables of the second subsystem become virtual control inputs for the first subsystem.In turn,exogenous control inputs are applied to the first subsystem.The whole control method is implemented in two successive loops and its global stability properties are also proven through Lyapunov stability analysis.The validity of the control method is confirmed in two case studies:(a)control and trajectories tracking for the autonomous octocopter,(ii)control of the attitude dynamics of micro-satellites.
基金supported by the Unit of Industrial AutomationIndustrial Systems Institute under Grant No.Ref.301022,Greece and RSP2024R150 of King Saud University,Riyadh,Saudi Arabia。
文摘The overuse and misuse of antibiotics has become a major problem for public health.People become resistant to antibiotics and because of this the anticipated therapeutic effect is never reached.In-hospital infections are often aggravated and large amounts of money are spent for treating complications in the patients'condition.In this paper a nonlinear optimal(H-infinity)control method is developed for the dynamic model of bacterial infections exhibiting resistance to antibiotics.First,differential flatness properties are proven for the associated state-space model.Next,the state-space description undergoes approximate linearization with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrices.The linearization process takes place at each sampling instance around a time-varying operating point which is defined by the present value of the system's state vector and by the last sampled value of the control inputs vector.For the approximately linearized model of the system a stabilizing H-infinity feedback controller is designed.To compute the controller's gains an algebraic Riccati equation has to be repetitively solved at each time-step of the control algorithm.The global stability properties of the control scheme are proven through Lyapunov analysis.The proposed method achieves stabilization and remedy for the bacterial infection under moderate use of antibiotics.
