Model predictive control(MPC)is a model-based optimal control strategy widely used in robot systems.In this work,the MPC controller tuning problem for the path tracking of the wheeled mobile robot is studied and a nov...Model predictive control(MPC)is a model-based optimal control strategy widely used in robot systems.In this work,the MPC controller tuning problem for the path tracking of the wheeled mobile robot is studied and a novel self-tuning approach is developed.First,two novel path tracking performance indices,i.e.,steadystate time ratio and steady-state distance ratio are proposed to more accurately reflect the control performance.Second,the mapping relationship between the proposed indices and the MPC parameters is established based on machine learning technique,and then a novel controller structure which can automatically tune the control parameters online is further designed.Finally,experimental verification with an actual wheeled mobile robot is conducted,which shows that the proposed method could outperform the existing method via achieving significant improvement in the rapidity,accuracy and adaptability of the robot path tracking.展开更多
Designing a stable and robust flight control system for an Unmanned Aerial Vehicle(UAV)is an arduous task.This paper addresses the trajectory tracking control problem of a Ducted Fan UAV(DFUAV)using offset-free Model ...Designing a stable and robust flight control system for an Unmanned Aerial Vehicle(UAV)is an arduous task.This paper addresses the trajectory tracking control problem of a Ducted Fan UAV(DFUAV)using offset-free Model Predictive Control(MPC)technique in the presence of various uncertainties and external disturbances.The designed strategy aims to ensure adequate flight robustness and stability while overcoming the effects of time delays,parametric uncertainties,and disturbances.The six degrees of freedom DFUAV model is divided into three flight modes based on its airspeed,namely the hover,transition,and cruise mode.The Dryden wind turbulence is applied to the DFUAV in the linear and angular velocity component.Moreover,different uncertainties such as parametric,time delays in state and input,are introduced in translational and rotational components.From the previous work,the Linear Quadratic Tracker with Integrator(LQTI)is used for comparison to corroborate the performance of the designed controller.Simulations are computed to investigate the control performance for the aforementioned modes and different flight phases including the autonomous flight to validate the performance of the designed strategy.Finally,discussions are provided to demonstrate the effectiveness of the given methodology.展开更多
The paper proposes a new swarm intelligence-based distributed Model Predictive Control(MPC)approach for coordination control of multiple Unmanned Aerial Vehicles(UAVs).First,a distributed MPC framework is designed and...The paper proposes a new swarm intelligence-based distributed Model Predictive Control(MPC)approach for coordination control of multiple Unmanned Aerial Vehicles(UAVs).First,a distributed MPC framework is designed and each member only shares the information with neighbors.The Chaotic Grey Wolf Optimization(CGWO)method is developed on the basis of chaotic initialization and chaotic search to solve the local Finite Horizon Optimal Control Problem(FHOCP).Then,the distributed cost function is designed and integrated into each FHOCP to achieve multi-UAV formation control and trajectory tracking with no-fly zone constraint.Further,an event-triggered strategy is proposed to reduce the computational burden for the distributed MPC approach,which considers the predicted state errors and the convergence of cost function.Simulation results show that the CGWO-based distributed MPC approach is more computationally efficient to achieve multi-UAV coordination control than traditional method.展开更多
Autonomous marine vehicles(AMVs)have received considerable attention in the past few decades,mainly because they play essential roles in broad marine applications such as environmental monitoring and resource explorat...Autonomous marine vehicles(AMVs)have received considerable attention in the past few decades,mainly because they play essential roles in broad marine applications such as environmental monitoring and resource exploration.Recent advances in the field of communication technologies,perception capability,computational power and advanced optimization algorithms have stimulated new interest in the development of AMVs.In order to deploy the constrained AMVs in the complex dynamic maritime environment,it is crucial to enhance the guidance and control capabilities through effective and practical planning,and control algorithms.Model predictive control(MPC)has been exceptionally successful in different fields due to its ability to systematically handle constraints while optimizing control performance.This paper aims to provide a review of recent progress in the context of motion planning and control for AMVs from the perceptive of MPC.Finally,future research trends and directions in this substantial research area of AMVs are highlighted.展开更多
In this paper, a complete industrial validation of a recently published scheme for on-line adaptation of the control updating period in model predictive control is proposed. The industrial process that serves in the v...In this paper, a complete industrial validation of a recently published scheme for on-line adaptation of the control updating period in model predictive control is proposed. The industrial process that serves in the validation is a cryogenic refrigerator that is used to cool the supra-conductors involved in particle accelerators or experimental nuclear reactors. Two recently predicted features are validated： the first states that it is sometimes better to use less efficient （per iteration） optimizer if the lack of efficiency is overmcompensated by an increase in the updating control frequency. The second is that for a given solver, it is worth monitoring the control updating period based on the on-line measured behavior of the cost function.展开更多
Feasible sets play an important role in model predictive control(MPC) optimal control problems(OCPs). This paper proposes a multi-parametric programming-based algorithm to compute the feasible set for OCP derived from...Feasible sets play an important role in model predictive control(MPC) optimal control problems(OCPs). This paper proposes a multi-parametric programming-based algorithm to compute the feasible set for OCP derived from MPC-based algorithms involving both spectrahedron(represented by linear matrix inequalities) and polyhedral(represented by a set of inequalities) constraints. According to the geometrical meaning of the inner product of vectors, the maximum length of the projection vector from the feasible set to a unit spherical coordinates vector is computed and the optimal solution has been proved to be one of the vertices of the feasible set. After computing the vertices,the convex hull of these vertices is determined which equals the feasible set. The simulation results show that the proposed method is especially efficient for low dimensional feasible set computation and avoids the non-unicity problem of optimizers as well as the memory consumption problem that encountered by projection algorithms.展开更多
基金the National Natural Science Foundation of China(No.61903291)the Key Research and Development Program of Shaanxi Province(No.2022NY-094)。
文摘Model predictive control(MPC)is a model-based optimal control strategy widely used in robot systems.In this work,the MPC controller tuning problem for the path tracking of the wheeled mobile robot is studied and a novel self-tuning approach is developed.First,two novel path tracking performance indices,i.e.,steadystate time ratio and steady-state distance ratio are proposed to more accurately reflect the control performance.Second,the mapping relationship between the proposed indices and the MPC parameters is established based on machine learning technique,and then a novel controller structure which can automatically tune the control parameters online is further designed.Finally,experimental verification with an actual wheeled mobile robot is conducted,which shows that the proposed method could outperform the existing method via achieving significant improvement in the rapidity,accuracy and adaptability of the robot path tracking.
基金co-supported by the National Natural Science Foundation of China(Nos.61225015,61105092,61422102,and 61703040)the Beijing Natural Science Foundation,China(No.4161001)the China Postdoctoral Science Foundation(No.2017M620640)。
文摘Designing a stable and robust flight control system for an Unmanned Aerial Vehicle(UAV)is an arduous task.This paper addresses the trajectory tracking control problem of a Ducted Fan UAV(DFUAV)using offset-free Model Predictive Control(MPC)technique in the presence of various uncertainties and external disturbances.The designed strategy aims to ensure adequate flight robustness and stability while overcoming the effects of time delays,parametric uncertainties,and disturbances.The six degrees of freedom DFUAV model is divided into three flight modes based on its airspeed,namely the hover,transition,and cruise mode.The Dryden wind turbulence is applied to the DFUAV in the linear and angular velocity component.Moreover,different uncertainties such as parametric,time delays in state and input,are introduced in translational and rotational components.From the previous work,the Linear Quadratic Tracker with Integrator(LQTI)is used for comparison to corroborate the performance of the designed controller.Simulations are computed to investigate the control performance for the aforementioned modes and different flight phases including the autonomous flight to validate the performance of the designed strategy.Finally,discussions are provided to demonstrate the effectiveness of the given methodology.
基金co-supported by the National Natural Science Foundation of China(Nos.61803009,61903084)Fundamental Research Funds for the Central Universities of China(No.YWF-20-BJ-J-542)Aeronautical Science Foundation of China(No.20175851032)。
文摘The paper proposes a new swarm intelligence-based distributed Model Predictive Control(MPC)approach for coordination control of multiple Unmanned Aerial Vehicles(UAVs).First,a distributed MPC framework is designed and each member only shares the information with neighbors.The Chaotic Grey Wolf Optimization(CGWO)method is developed on the basis of chaotic initialization and chaotic search to solve the local Finite Horizon Optimal Control Problem(FHOCP).Then,the distributed cost function is designed and integrated into each FHOCP to achieve multi-UAV formation control and trajectory tracking with no-fly zone constraint.Further,an event-triggered strategy is proposed to reduce the computational burden for the distributed MPC approach,which considers the predicted state errors and the convergence of cost function.Simulation results show that the CGWO-based distributed MPC approach is more computationally efficient to achieve multi-UAV coordination control than traditional method.
基金supported by the Natural Sciences and Engineering Research Council of Canada(NSERC)。
文摘Autonomous marine vehicles(AMVs)have received considerable attention in the past few decades,mainly because they play essential roles in broad marine applications such as environmental monitoring and resource exploration.Recent advances in the field of communication technologies,perception capability,computational power and advanced optimization algorithms have stimulated new interest in the development of AMVs.In order to deploy the constrained AMVs in the complex dynamic maritime environment,it is crucial to enhance the guidance and control capabilities through effective and practical planning,and control algorithms.Model predictive control(MPC)has been exceptionally successful in different fields due to its ability to systematically handle constraints while optimizing control performance.This paper aims to provide a review of recent progress in the context of motion planning and control for AMVs from the perceptive of MPC.Finally,future research trends and directions in this substantial research area of AMVs are highlighted.
文摘In this paper, a complete industrial validation of a recently published scheme for on-line adaptation of the control updating period in model predictive control is proposed. The industrial process that serves in the validation is a cryogenic refrigerator that is used to cool the supra-conductors involved in particle accelerators or experimental nuclear reactors. Two recently predicted features are validated： the first states that it is sometimes better to use less efficient （per iteration） optimizer if the lack of efficiency is overmcompensated by an increase in the updating control frequency. The second is that for a given solver, it is worth monitoring the control updating period based on the on-line measured behavior of the cost function.
基金supported by the Natural Science Foundation of Zhejiang Province(LR17F030002)the Science Fund for Creative Research Groups of the National Natural Science Foundation of China(61621002)
文摘Feasible sets play an important role in model predictive control(MPC) optimal control problems(OCPs). This paper proposes a multi-parametric programming-based algorithm to compute the feasible set for OCP derived from MPC-based algorithms involving both spectrahedron(represented by linear matrix inequalities) and polyhedral(represented by a set of inequalities) constraints. According to the geometrical meaning of the inner product of vectors, the maximum length of the projection vector from the feasible set to a unit spherical coordinates vector is computed and the optimal solution has been proved to be one of the vertices of the feasible set. After computing the vertices,the convex hull of these vertices is determined which equals the feasible set. The simulation results show that the proposed method is especially efficient for low dimensional feasible set computation and avoids the non-unicity problem of optimizers as well as the memory consumption problem that encountered by projection algorithms.
