By converting an optimal control problem for nonlinear systems to a Hamiltonian system,a symplecitc-preserving method is proposed.The state and costate variables are approximated by the Lagrange polynomial.The state v...By converting an optimal control problem for nonlinear systems to a Hamiltonian system,a symplecitc-preserving method is proposed.The state and costate variables are approximated by the Lagrange polynomial.The state variables at two ends of the time interval are taken as independent variables.Based on the dual variable principle,nonlinear optimal control problems are replaced with nonlinear equations.Furthermore,in the implementation of the symplectic algorithm,based on the 2N algorithm,a multilevel method is proposed.When the time grid is refined from low level to high level,the initial state and costate variables of the nonlinear equations can be obtained from the Lagrange interpolation at the low level grid to improve efficiency.Numerical simulations show the precision and the efficiency of the proposed algorithm in this paper.展开更多
The optimal control of nonlinear systems has been studied for years by many researchers. However, the application of optimal control problem to nonlinear non-affine systems needs more attention. In this paper we propo...The optimal control of nonlinear systems has been studied for years by many researchers. However, the application of optimal control problem to nonlinear non-affine systems needs more attention. In this paper we propose an optimal control design technique for a class of nonlinear and control non-affine equations. The dynamic equations of a flexible shaft supported by a pair of active magnetic bearings (AMBs) are used as the nonlinear control non-affine equations. Mathematical model for the flexible beam is chosen to be the well known Timoshenko beam model, which takes rotary inertia and shear deformations into account, and it is assumed that the shaft is supported by two frictionless bearings at the ends. The effective control of such systems is extremely important for very high angular velocity shafts which are a feature of many modern machines. The control must be able to cope with unbalanced masses and hence be very robust. We shall approach the problem by discretising the Timoshenko beam model and using standard difference formulae to develop a finite-dimensional model of the system. Then we use a recently developed technique for controlling nonlinear systems by reducing the problem to a sequence of linear time-varying (LTV) systems. An optimal control designed for each approximating linear, time-varying system and recent results show that this method will converge uniformly on compact time intervals to the optimal solution.展开更多
The article proposes a nonlinear optimal(H-infinity)control approach for the model of a tracked robotic vehicle.The kinematic model of such a tracked vehicle takes into account slippage effects due to the contact of t...The article proposes a nonlinear optimal(H-infinity)control approach for the model of a tracked robotic vehicle.The kinematic model of such a tracked vehicle takes into account slippage effects due to the contact of the tracks with the ground.To solve the related control problem,the dynamic model of the vehicle undergoes first approximate linearization around a temporary operating point which is updated at each iteration of the control algorithm.The linearization process relies on first-order Taylor series expansion and on the computation of the Jacobian matrices of the state-space model of the vehicle.For the approximately linearized description of the tracked vehicle a stabilizing H-infinity feedback controller is designed.To compute the controller’s feedback gains an algebraic Riccati equation is solved at each time-step of the control method.The stability properties of the control scheme are proven through Lyapunov analysis.It is also demonstrated that the control method retains the advantages of linear optimal control,that is fast and accurate tracking of reference setpoints under moderate variations of the control inputs.展开更多
The paper proposes a nonlinear optimal control approach for the model of the vertical take off and landing(VTOL)aircraft.This aerial drone receives as control input a directed thrust,as well as forces acting on its wi...The paper proposes a nonlinear optimal control approach for the model of the vertical take off and landing(VTOL)aircraft.This aerial drone receives as control input a directed thrust,as well as forces acting on its wing tips.The latter forces are not perpendicular to the body axis of the drone but are tilted by a small angle.The dynamic model of the VTOL undergoes ap-proximate linearization with the use of Taylor series expansion around a temporary operating point which is recomputed at each iteration of the control method.For the approximately linearized model,an H-infinity feedback controller is designed.The linearization procedure relies on the computation of the Jacobian matrices of the state-space model of the VTOL aircraft.The proposed control method stands for the solution of the optimal control problem for the nonlinear and multivariable dynamics of the aerial drone,under model uncertainties and external per-turbations.For the computation of the contollr's feedback gains,an algebraic Riccati equation is solved at each time-step of the control method.The new nonlinear optimal control approach achieves fast and accurate tracking for all state variables of the VTOL aircnaft,under moderate variations of the control inputs.The stability properties of the control scheme are proven through Lyapunov analysis.展开更多
Attitude control and stabilization of micro-satellites is a nontrivial problem due to the highly nonlinear and multivariable structure of the satellites'state-space model.In this paper,a novel nonlinear optimal(H-...Attitude control and stabilization of micro-satellites is a nontrivial problem due to the highly nonlinear and multivariable structure of the satellites'state-space model.In this paper,a novel nonlinear optimal(H-infinity)control approach is developed for this control problem.The dynamic model of the satellite's attitude dynamics undergoesfirst approximate linearization around a temporary operating point which is updated at each iteration of the control algorithm.The linearization process relies on first-order Taylor series expansion and on the computation of the Jacobian matrices of the state-space model of the satellite's attitude dynamics.For the approximately linearized description of the satellite's attitude a stabilizing H-infinity feedback controller is designed.To compute the controller's feedback gains,an algebraic Riccati equation is solved at each time-step of the control method.The stability properties of the control scheme are proven through Lyapunov analysis.It is also demonstrated that the control method retains the advantages of linear optimal control that is fast and accurate tracking of the reference setpoints under moderate variations of the control inputs.展开更多
We investigate the use of an approximation method for obtaining near-optimal solutions to a kind of nonlinear continuous-time(CT) system. The approach derived from the Galerkin approximation is used to solve the gener...We investigate the use of an approximation method for obtaining near-optimal solutions to a kind of nonlinear continuous-time(CT) system. The approach derived from the Galerkin approximation is used to solve the generalized Hamilton-Jacobi-Bellman(GHJB) equations. The Galerkin approximation with Legendre polynomials(GALP) for GHJB equations has not been applied to nonlinear CT systems. The proposed GALP method solves the GHJB equations in CT systems on some well-defined region of attraction. The integrals that need to be computed are much fewer due to the orthogonal properties of Legendre polynomials, which is a significant advantage of this approach. The stabilization and convergence properties with regard to the iterative variable have been proved.Numerical examples show that the update control laws converge to the optimal control for nonlinear CT systems.展开更多
This paper presents a new approach for solving a class of infinite horizon nonlinear optimal control problems (OCPs).In this approach,a nonlinear two-point boundary value problem (TPBVP),derived from Pontryagin's ...This paper presents a new approach for solving a class of infinite horizon nonlinear optimal control problems (OCPs).In this approach,a nonlinear two-point boundary value problem (TPBVP),derived from Pontryagin's maximum principle,is transformed into a sequence of linear time-invariant TPBVPs.Solving the latter problems in a recursive manner provides the optimal control law and the optimal trajectory in the form of uniformly convergent series.Hence,to obtain the optimal solution,only the techniques for solving linear ordinary differential equations are employed.An efficient algorithm is also presented,which has low computational complexity and a fast convergence rate.Just a few iterations are required to find an accurate enough suboptimal trajectory-control pair for the nonlinear OCP.The results not only demonstrate the efficiency,simplicity,and high accuracy of the suggested approach,but also indicate its effectiveness in practical use.展开更多
A control algorithm for improving vehicle handling was proposed by applying right angle to the steering wheel,based on the nonlinear adaptive optimal control(NAOC).A nonlinear 4-DOF model was initially developed,then ...A control algorithm for improving vehicle handling was proposed by applying right angle to the steering wheel,based on the nonlinear adaptive optimal control(NAOC).A nonlinear 4-DOF model was initially developed,then it was simplified to a 2-DOF model with reasonable assumptions to design observer and optimal controllers.Then a simplified model was developed for steering system.The numerical simulations were carried out using vehicle parameters for standard maneuvers in dry and wet road conditions.Moreover,the hardware in the loop method was implemented to prove the controller ability in realistic conditions.Simulation results obviously show the effectiveness of NAOC on vehicle handling and reveal that the proposed controller can significantly improve vehicle handling during severe maneuvers.展开更多
Intercepting high-maneuverability hypersonic targets in near-space environments poses significant challenges due to their extreme speeds and evasive capabilities.To address these challenges,this study presents an inte...Intercepting high-maneuverability hypersonic targets in near-space environments poses significant challenges due to their extreme speeds and evasive capabilities.To address these challenges,this study presents an integrated approach that combines a Three-Dimensional Finite-Time Optimal Cooperative Guidance Law(FTOC)with an Information Fusion Anti-saturation Predefined-time Observer(IFAPO).The proposed FTOC guidance law employs a nonlinear,non-quadratic finite-time optimal control strategy designed for rapid convergence within the limited timeframes of near-space interceptions,avoiding the need for remaining flight time estimation or linear decoupling inherent in traditional methods.To complement the guidance strategy,the IFAPO leverages multi-source information fusion theory and incorporates anti-saturation mechanisms to enhance target maneuver estimation.This method ensures accurate and real-time prediction of target acceleration while maintaining predefined convergence performance,even under complex interception conditions.By integrating the FTOC guidance law and IFAPO,the approach optimizes cooperative missile positioning,improves interception success rates,and minimizes fuel consumption,addressing practical constraints in military applications.Simulation results and comparative analyses confirm the effectiveness of the integrated approach,demonstrating its capability to achieve cooperative interception of highly maneuvering targets with enhanced efficiency and reduced economic costs,aligning with realistic combat scenarios.展开更多
This paper studies the problem of optimal parallel tracking control for continuous-time general nonlinear systems.Unlike existing optimal state feedback control,the control input of the optimal parallel control is int...This paper studies the problem of optimal parallel tracking control for continuous-time general nonlinear systems.Unlike existing optimal state feedback control,the control input of the optimal parallel control is introduced into the feedback system.However,due to the introduction of control input into the feedback system,the optimal state feedback control methods can not be applied directly.To address this problem,an augmented system and an augmented performance index function are proposed firstly.Thus,the general nonlinear system is transformed into an affine nonlinear system.The difference between the optimal parallel control and the optimal state feedback control is analyzed theoretically.It is proven that the optimal parallel control with the augmented performance index function can be seen as the suboptimal state feedback control with the traditional performance index function.Moreover,an adaptive dynamic programming(ADP)technique is utilized to implement the optimal parallel tracking control using a critic neural network(NN)to approximate the value function online.The stability analysis of the closed-loop system is performed using the Lyapunov theory,and the tracking error and NN weights errors are uniformly ultimately bounded(UUB).Also,the optimal parallel controller guarantees the continuity of the control input under the circumstance that there are finite jump discontinuities in the reference signals.Finally,the effectiveness of the developed optimal parallel control method is verified in two cases.展开更多
This paper proposes a discrete-time robust control technique for an uncertain nonlinear system. The uncertainty mainly affects the system dynamics due to mismatched parameter variation which is bounded by a predefined...This paper proposes a discrete-time robust control technique for an uncertain nonlinear system. The uncertainty mainly affects the system dynamics due to mismatched parameter variation which is bounded by a predefined known function. In order to compensate the effect of uncertainty, a robust control input is derived by formulating an equivalent optimal control problem for a virtual nominal system with a modified costfunctional. To derive the stabilizing control law for a mismatched system, this paper introduces another control input named as virtual input. This virtual input is not applied directly to stabilize the uncertain system, rather it is used to define a sufficient condition. To solve the nonlinear optimal control problem, a discretetime general Hamilton-Jacobi-Bellman(DT-GHJB) equation is considered and it is approximated numerically through a neural network(NN) implementation. The approximated solution of DTGHJB is used to compute the suboptimal control input for the virtual system. The suboptimal inputs for the virtual system ensure the asymptotic stability of the closed-loop uncertain system. A numerical example is illustrated with simulation results to prove the efficacy of the proposed control algorithm.展开更多
A new stochastic optimal control strategy for randomly excited quasi-integrable Hamiltonian systems using magneto-rheological (MR) dampers is proposed. The dynamic be- havior of an MR damper is characterized by the ...A new stochastic optimal control strategy for randomly excited quasi-integrable Hamiltonian systems using magneto-rheological (MR) dampers is proposed. The dynamic be- havior of an MR damper is characterized by the Bouc-Wen hysteretic model. The control force produced by the MR damper is separated into a passive part incorporated in the uncontrolled system and a semi-active part to be determined. The system combining the Bouc-Wen hysteretic force is converted into an equivalent non-hysteretic nonlinear stochastic control system. Then It?o stochastic di?erential equations are derived from the equivalent system by using the stochastic averaging method. A dynamical programming equation for the controlled di?usion processes is established based on the stochastic dynamical programming principle. The non-clipping nonlin- ear optimal control law is obtained for a certain performance index by minimizing the dynamical programming equation. Finally, an example is given to illustrate the application and e?ectiveness of the proposed control strategy.展开更多
Safety-critical control enables intelligent robots to have better secure operation and resistance to adverse environments, hence greatly enhancing their interaction and adaptability to the environment. In this paper, ...Safety-critical control enables intelligent robots to have better secure operation and resistance to adverse environments, hence greatly enhancing their interaction and adaptability to the environment. In this paper, we propose a safety-critical control scheme for robotic systems using an adaptive error elimination algorithm and optimization-based nonlinear optimal predictive control(NOPC) framework. The novelty of the proposed work lies in that an adaptive error elimination controller is designed to deal with the problem of stabilization of walking gait, which ensures that robot joint trajectory can compensate for the limitation of the template model. In order to be independent of system parameters and disturbances, a sliding mode controller is further designed under an uncertain environment. This approach takes into account simultaneously with foot position and orientation based on NOPC optimization. It tracks the modified trajectories constrained with the centroidal momentum dynamics. Finally, simulations is utilized to verify the efectiveness of the mentioned methods. The results indicate that the tracking efect of joint trajectory is better safety-critical nonlinear optimal predictive control with adaptive error elimination algorithm.展开更多
This paper presents a method to predict the pilot workload in helicopter landing after one engine failure.The landing procedure is simulated numerically via applying nonlinear optimal control method in the form of per...This paper presents a method to predict the pilot workload in helicopter landing after one engine failure.The landing procedure is simulated numerically via applying nonlinear optimal control method in the form of performance index,path constraints and boundary conditions based on an augmented six-degree-of-freedom rigid-body flight dynamics model,solved by collocation and numerical optimization method.UH-60 A helicopter is taken as the sample for the demonstration of landing after one engine failure.The numerical simulation was conducted to find the trajectory of helicopter and the controls from pilot for landing after one engine failure with different performance index considering the factor of pilot workload.The reasonable performance index and corresponding landing trajectory and controls are obtained by making a comparison with those from the flight test data.Furthermore,the pilot workload is evaluated based on wavelet transform analysis of the pilot control activities.The workloads of pilot control activities for collective control,longitudinal and lateral cyclic controls and pedal control during the helicopter landing after one engine failure are examined and compared with those of flight test.The results show that when the performance index considers the factor of pilot workload properly,the characteristics of amplitudes and constituent frequencies of pilot control inputs in the optimal solution are consistent with those of the pilot control inputs in the flight test.Therefore,the proposed method provides a tool of predicting the pilot workload in helicopter landing after one engine failure.展开更多
Using object mathematical model of traditional control theory can not solve the forecasting problem of the chemical components of sintered ore.In order to control complicated chemical components in the manufacturing p...Using object mathematical model of traditional control theory can not solve the forecasting problem of the chemical components of sintered ore.In order to control complicated chemical components in the manufacturing process of sintered ore,some key techniques for intelligent forecasting of the chemical components of sintered ore are studied in this paper.A new intelligent forecasting system based on SVM is proposed and realized.The results show that the accuracy of predictive value of every component is more than 90%.The application of our system in related companies is for more than one year and has shown satisfactory results.展开更多
To improve the transient stability of multimachine power systems,observational linearization and tracking objective excitation control laws were derived from the phasor measurement unit(PMU),observational linearizatio...To improve the transient stability of multimachine power systems,observational linearization and tracking objective excitation control laws were derived from the phasor measurement unit(PMU),observational linearization,and tracking objective control theory based on synchronized coordinates and reference generator coordinates.The control strategies utilized real-time state variables obtained by PMU to linearize the state equations of the system,and then the linear optimal control strategy was used to design excitation controllers.The inaccuracy of the local linearization method and the complexity of the system models designed in the exact linearization method for nonlinear systems were avoided.Therefore,the control strategies were applied in real time.Simulation results show that the proposed method can improve the transient stability of power systems more efficiently than nonlinear optimal excitation control.展开更多
基金supported by the National Natural Science Foundation of China(Nos.10632030,10902020,and 10721062)the Research Fund for the Doctoral Program of Higher Education of China(No.20070141067)+2 种基金the Doctoral Fund of Liaoning Province(No.20081091)the Key Laboratory Fund of Liaoning Province of China(No.2009S018)the Young Researcher Funds of Dalian University of Technology(No.SFDUT07002)
文摘By converting an optimal control problem for nonlinear systems to a Hamiltonian system,a symplecitc-preserving method is proposed.The state and costate variables are approximated by the Lagrange polynomial.The state variables at two ends of the time interval are taken as independent variables.Based on the dual variable principle,nonlinear optimal control problems are replaced with nonlinear equations.Furthermore,in the implementation of the symplectic algorithm,based on the 2N algorithm,a multilevel method is proposed.When the time grid is refined from low level to high level,the initial state and costate variables of the nonlinear equations can be obtained from the Lagrange interpolation at the low level grid to improve efficiency.Numerical simulations show the precision and the efficiency of the proposed algorithm in this paper.
文摘The optimal control of nonlinear systems has been studied for years by many researchers. However, the application of optimal control problem to nonlinear non-affine systems needs more attention. In this paper we propose an optimal control design technique for a class of nonlinear and control non-affine equations. The dynamic equations of a flexible shaft supported by a pair of active magnetic bearings (AMBs) are used as the nonlinear control non-affine equations. Mathematical model for the flexible beam is chosen to be the well known Timoshenko beam model, which takes rotary inertia and shear deformations into account, and it is assumed that the shaft is supported by two frictionless bearings at the ends. The effective control of such systems is extremely important for very high angular velocity shafts which are a feature of many modern machines. The control must be able to cope with unbalanced masses and hence be very robust. We shall approach the problem by discretising the Timoshenko beam model and using standard difference formulae to develop a finite-dimensional model of the system. Then we use a recently developed technique for controlling nonlinear systems by reducing the problem to a sequence of linear time-varying (LTV) systems. An optimal control designed for each approximating linear, time-varying system and recent results show that this method will converge uniformly on compact time intervals to the optimal solution.
基金supported by the Research“Advances in Applied Nonlinear Optimal Control”under Grant No.6065。
文摘The article proposes a nonlinear optimal(H-infinity)control approach for the model of a tracked robotic vehicle.The kinematic model of such a tracked vehicle takes into account slippage effects due to the contact of the tracks with the ground.To solve the related control problem,the dynamic model of the vehicle undergoes first approximate linearization around a temporary operating point which is updated at each iteration of the control algorithm.The linearization process relies on first-order Taylor series expansion and on the computation of the Jacobian matrices of the state-space model of the vehicle.For the approximately linearized description of the tracked vehicle a stabilizing H-infinity feedback controller is designed.To compute the controller’s feedback gains an algebraic Riccati equation is solved at each time-step of the control method.The stability properties of the control scheme are proven through Lyapunov analysis.It is also demonstrated that the control method retains the advantages of linear optimal control,that is fast and accurate tracking of reference setpoints under moderate variations of the control inputs.
文摘The paper proposes a nonlinear optimal control approach for the model of the vertical take off and landing(VTOL)aircraft.This aerial drone receives as control input a directed thrust,as well as forces acting on its wing tips.The latter forces are not perpendicular to the body axis of the drone but are tilted by a small angle.The dynamic model of the VTOL undergoes ap-proximate linearization with the use of Taylor series expansion around a temporary operating point which is recomputed at each iteration of the control method.For the approximately linearized model,an H-infinity feedback controller is designed.The linearization procedure relies on the computation of the Jacobian matrices of the state-space model of the VTOL aircraft.The proposed control method stands for the solution of the optimal control problem for the nonlinear and multivariable dynamics of the aerial drone,under model uncertainties and external per-turbations.For the computation of the contollr's feedback gains,an algebraic Riccati equation is solved at each time-step of the control method.The new nonlinear optimal control approach achieves fast and accurate tracking for all state variables of the VTOL aircnaft,under moderate variations of the control inputs.The stability properties of the control scheme are proven through Lyapunov analysis.
文摘Attitude control and stabilization of micro-satellites is a nontrivial problem due to the highly nonlinear and multivariable structure of the satellites'state-space model.In this paper,a novel nonlinear optimal(H-infinity)control approach is developed for this control problem.The dynamic model of the satellite's attitude dynamics undergoesfirst approximate linearization around a temporary operating point which is updated at each iteration of the control algorithm.The linearization process relies on first-order Taylor series expansion and on the computation of the Jacobian matrices of the state-space model of the satellite's attitude dynamics.For the approximately linearized description of the satellite's attitude a stabilizing H-infinity feedback controller is designed.To compute the controller's feedback gains,an algebraic Riccati equation is solved at each time-step of the control method.The stability properties of the control scheme are proven through Lyapunov analysis.It is also demonstrated that the control method retains the advantages of linear optimal control that is fast and accurate tracking of the reference setpoints under moderate variations of the control inputs.
基金supported by the China Postdoctoral Science Foundation(No.2013M531842)the Natural Science Foundation of Guangdong Province,China(No.2015A030313497)the Science and Technology Program of Guangzhou,China(No.2014KP000039)
文摘We investigate the use of an approximation method for obtaining near-optimal solutions to a kind of nonlinear continuous-time(CT) system. The approach derived from the Galerkin approximation is used to solve the generalized Hamilton-Jacobi-Bellman(GHJB) equations. The Galerkin approximation with Legendre polynomials(GALP) for GHJB equations has not been applied to nonlinear CT systems. The proposed GALP method solves the GHJB equations in CT systems on some well-defined region of attraction. The integrals that need to be computed are much fewer due to the orthogonal properties of Legendre polynomials, which is a significant advantage of this approach. The stabilization and convergence properties with regard to the iterative variable have been proved.Numerical examples show that the update control laws converge to the optimal control for nonlinear CT systems.
文摘This paper presents a new approach for solving a class of infinite horizon nonlinear optimal control problems (OCPs).In this approach,a nonlinear two-point boundary value problem (TPBVP),derived from Pontryagin's maximum principle,is transformed into a sequence of linear time-invariant TPBVPs.Solving the latter problems in a recursive manner provides the optimal control law and the optimal trajectory in the form of uniformly convergent series.Hence,to obtain the optimal solution,only the techniques for solving linear ordinary differential equations are employed.An efficient algorithm is also presented,which has low computational complexity and a fast convergence rate.Just a few iterations are required to find an accurate enough suboptimal trajectory-control pair for the nonlinear OCP.The results not only demonstrate the efficiency,simplicity,and high accuracy of the suggested approach,but also indicate its effectiveness in practical use.
文摘A control algorithm for improving vehicle handling was proposed by applying right angle to the steering wheel,based on the nonlinear adaptive optimal control(NAOC).A nonlinear 4-DOF model was initially developed,then it was simplified to a 2-DOF model with reasonable assumptions to design observer and optimal controllers.Then a simplified model was developed for steering system.The numerical simulations were carried out using vehicle parameters for standard maneuvers in dry and wet road conditions.Moreover,the hardware in the loop method was implemented to prove the controller ability in realistic conditions.Simulation results obviously show the effectiveness of NAOC on vehicle handling and reveal that the proposed controller can significantly improve vehicle handling during severe maneuvers.
基金supported by the National Natural Science Foundation of China(Grant No.61773142).
文摘Intercepting high-maneuverability hypersonic targets in near-space environments poses significant challenges due to their extreme speeds and evasive capabilities.To address these challenges,this study presents an integrated approach that combines a Three-Dimensional Finite-Time Optimal Cooperative Guidance Law(FTOC)with an Information Fusion Anti-saturation Predefined-time Observer(IFAPO).The proposed FTOC guidance law employs a nonlinear,non-quadratic finite-time optimal control strategy designed for rapid convergence within the limited timeframes of near-space interceptions,avoiding the need for remaining flight time estimation or linear decoupling inherent in traditional methods.To complement the guidance strategy,the IFAPO leverages multi-source information fusion theory and incorporates anti-saturation mechanisms to enhance target maneuver estimation.This method ensures accurate and real-time prediction of target acceleration while maintaining predefined convergence performance,even under complex interception conditions.By integrating the FTOC guidance law and IFAPO,the approach optimizes cooperative missile positioning,improves interception success rates,and minimizes fuel consumption,addressing practical constraints in military applications.Simulation results and comparative analyses confirm the effectiveness of the integrated approach,demonstrating its capability to achieve cooperative interception of highly maneuvering targets with enhanced efficiency and reduced economic costs,aligning with realistic combat scenarios.
基金supported in part by the National Key Reseanch and Development Program of China(2018AAA0101502,2018YFB1702300)in part by the National Natural Science Foundation of China(61722312,61533019,U1811463,61533017)in part by the Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles。
文摘This paper studies the problem of optimal parallel tracking control for continuous-time general nonlinear systems.Unlike existing optimal state feedback control,the control input of the optimal parallel control is introduced into the feedback system.However,due to the introduction of control input into the feedback system,the optimal state feedback control methods can not be applied directly.To address this problem,an augmented system and an augmented performance index function are proposed firstly.Thus,the general nonlinear system is transformed into an affine nonlinear system.The difference between the optimal parallel control and the optimal state feedback control is analyzed theoretically.It is proven that the optimal parallel control with the augmented performance index function can be seen as the suboptimal state feedback control with the traditional performance index function.Moreover,an adaptive dynamic programming(ADP)technique is utilized to implement the optimal parallel tracking control using a critic neural network(NN)to approximate the value function online.The stability analysis of the closed-loop system is performed using the Lyapunov theory,and the tracking error and NN weights errors are uniformly ultimately bounded(UUB).Also,the optimal parallel controller guarantees the continuity of the control input under the circumstance that there are finite jump discontinuities in the reference signals.Finally,the effectiveness of the developed optimal parallel control method is verified in two cases.
文摘This paper proposes a discrete-time robust control technique for an uncertain nonlinear system. The uncertainty mainly affects the system dynamics due to mismatched parameter variation which is bounded by a predefined known function. In order to compensate the effect of uncertainty, a robust control input is derived by formulating an equivalent optimal control problem for a virtual nominal system with a modified costfunctional. To derive the stabilizing control law for a mismatched system, this paper introduces another control input named as virtual input. This virtual input is not applied directly to stabilize the uncertain system, rather it is used to define a sufficient condition. To solve the nonlinear optimal control problem, a discretetime general Hamilton-Jacobi-Bellman(DT-GHJB) equation is considered and it is approximated numerically through a neural network(NN) implementation. The approximated solution of DTGHJB is used to compute the suboptimal control input for the virtual system. The suboptimal inputs for the virtual system ensure the asymptotic stability of the closed-loop uncertain system. A numerical example is illustrated with simulation results to prove the efficacy of the proposed control algorithm.
基金Project supported by the Zhejiang Provincial Natural Sciences Foundation (No. 101046) and the foundation fromHong Kong RGC (No. PolyU 5051/02E).
文摘A new stochastic optimal control strategy for randomly excited quasi-integrable Hamiltonian systems using magneto-rheological (MR) dampers is proposed. The dynamic be- havior of an MR damper is characterized by the Bouc-Wen hysteretic model. The control force produced by the MR damper is separated into a passive part incorporated in the uncontrolled system and a semi-active part to be determined. The system combining the Bouc-Wen hysteretic force is converted into an equivalent non-hysteretic nonlinear stochastic control system. Then It?o stochastic di?erential equations are derived from the equivalent system by using the stochastic averaging method. A dynamical programming equation for the controlled di?usion processes is established based on the stochastic dynamical programming principle. The non-clipping nonlin- ear optimal control law is obtained for a certain performance index by minimizing the dynamical programming equation. Finally, an example is given to illustrate the application and e?ectiveness of the proposed control strategy.
基金supported by the National Natural Science Foundation of China (No. 61573260,No. 62073245,No. U1713211)
文摘Safety-critical control enables intelligent robots to have better secure operation and resistance to adverse environments, hence greatly enhancing their interaction and adaptability to the environment. In this paper, we propose a safety-critical control scheme for robotic systems using an adaptive error elimination algorithm and optimization-based nonlinear optimal predictive control(NOPC) framework. The novelty of the proposed work lies in that an adaptive error elimination controller is designed to deal with the problem of stabilization of walking gait, which ensures that robot joint trajectory can compensate for the limitation of the template model. In order to be independent of system parameters and disturbances, a sliding mode controller is further designed under an uncertain environment. This approach takes into account simultaneously with foot position and orientation based on NOPC optimization. It tracks the modified trajectories constrained with the centroidal momentum dynamics. Finally, simulations is utilized to verify the efectiveness of the mentioned methods. The results indicate that the tracking efect of joint trajectory is better safety-critical nonlinear optimal predictive control with adaptive error elimination algorithm.
基金supported by the National Natural Science Foundation of China(No.11672128)。
文摘This paper presents a method to predict the pilot workload in helicopter landing after one engine failure.The landing procedure is simulated numerically via applying nonlinear optimal control method in the form of performance index,path constraints and boundary conditions based on an augmented six-degree-of-freedom rigid-body flight dynamics model,solved by collocation and numerical optimization method.UH-60 A helicopter is taken as the sample for the demonstration of landing after one engine failure.The numerical simulation was conducted to find the trajectory of helicopter and the controls from pilot for landing after one engine failure with different performance index considering the factor of pilot workload.The reasonable performance index and corresponding landing trajectory and controls are obtained by making a comparison with those from the flight test data.Furthermore,the pilot workload is evaluated based on wavelet transform analysis of the pilot control activities.The workloads of pilot control activities for collective control,longitudinal and lateral cyclic controls and pedal control during the helicopter landing after one engine failure are examined and compared with those of flight test.The results show that when the performance index considers the factor of pilot workload properly,the characteristics of amplitudes and constituent frequencies of pilot control inputs in the optimal solution are consistent with those of the pilot control inputs in the flight test.Therefore,the proposed method provides a tool of predicting the pilot workload in helicopter landing after one engine failure.
基金Supported by Key Science and Technology Project of Wuhan(No. 20106062327)Self-determined and Innovative Research Funds of WUT (No.2010-YB-20)
文摘Using object mathematical model of traditional control theory can not solve the forecasting problem of the chemical components of sintered ore.In order to control complicated chemical components in the manufacturing process of sintered ore,some key techniques for intelligent forecasting of the chemical components of sintered ore are studied in this paper.A new intelligent forecasting system based on SVM is proposed and realized.The results show that the accuracy of predictive value of every component is more than 90%.The application of our system in related companies is for more than one year and has shown satisfactory results.
基金supported by the National Natural Science Foundation of China(Grant No.50595410).
文摘To improve the transient stability of multimachine power systems,observational linearization and tracking objective excitation control laws were derived from the phasor measurement unit(PMU),observational linearization,and tracking objective control theory based on synchronized coordinates and reference generator coordinates.The control strategies utilized real-time state variables obtained by PMU to linearize the state equations of the system,and then the linear optimal control strategy was used to design excitation controllers.The inaccuracy of the local linearization method and the complexity of the system models designed in the exact linearization method for nonlinear systems were avoided.Therefore,the control strategies were applied in real time.Simulation results show that the proposed method can improve the transient stability of power systems more efficiently than nonlinear optimal excitation control.
