A receding horizon Hoo control algorithm is presented for linear discrete time-delay system in the presence of constrained input and disturbances. Disturbance attenuation level is optimized at each time instant, and t...A receding horizon Hoo control algorithm is presented for linear discrete time-delay system in the presence of constrained input and disturbances. Disturbance attenuation level is optimized at each time instant, and the receding optimization problem includes several linear matrix inequality constraints. When the convex hull is applied to denote the saturating input, the algorithm has better performance. The numerical example can verify this result.展开更多
Receding horizon H∞ control scheme which can deal with both the H∞ disturbance attenuation and mean square stability is proposed for a class of discrete-time Markovian jump linear systems when minimizing a given qua...Receding horizon H∞ control scheme which can deal with both the H∞ disturbance attenuation and mean square stability is proposed for a class of discrete-time Markovian jump linear systems when minimizing a given quadratic performance criteria. First, a control law is established for jump systems based on pontryagin’s minimum principle and it can be constructed through numerical solution of iterative equations. The aim of this control strategy is to obtain an optimal control which can minimize the cost function under the worst disturbance at every sampling time. Due to the difficulty of the assurance of stability, then the above mentioned approach is improved by determining terminal weighting matrix which satisfies cost monotonicity condition. The control move which is calculated by using this type of terminal weighting matrix as boundary condition naturally guarantees the mean square stability of the closed-loop system. A sufficient condition for the existence of the terminal weighting matrix is presented in linear matrix inequality （LMI） form which can be solved efficiently by available software toolbox. Finally, a numerical example is given to illustrate the feasibility and effectiveness of the proposed method.展开更多
The purpose of this work is to propose a scheme to stabilize the predictive control systems in the practical stability sense. In the paper, the authors dealt with a general discrete predictive control system x j+1|t =...The purpose of this work is to propose a scheme to stabilize the predictive control systems in the practical stability sense. In the paper, the authors dealt with a general discrete predictive control system x j+1|t =f(x j|t , u j|t ) by using the Lyapunov direct method combining with receding horizon control technique, and presented a new condition to guarantee the practical stabilization of the systems. With the proposed results, one can design the optimal controllers easily to practically stabilize the predictive control systems.展开更多
For a class of discrete-time singular stochastic systems with multi-state delay,the stabilization problem of receding horizon control(RHC)is concerned.Due to the difficulty in solving the proposed optimization problem...For a class of discrete-time singular stochastic systems with multi-state delay,the stabilization problem of receding horizon control(RHC)is concerned.Due to the difficulty in solving the proposed optimization problem,the RHC stabilization for such systems has not been solved.By adopting the forward and backward equation technique,the optimization problem is solved completely.A sufficient and necessary condition for the optimization controller to have a unique solution is given when the regularization and pulse-free conditions are satisfied.Based on this controller,an RHC stabilization condition is derived,which is in the form of linear matrix inequality.It is proved that the singular stochastic system with multi-state delay is stable in the mean-square sense under appropriate assumptions when the terminal weighting matrix satisfies the given inequality.Numerical examples show that the proposed RHC method is effective in stabilizing singular stochastic systems with multi-state delay.展开更多
Multiple unmanned air vehicles(UAVs)/unmanned ground vehicles(UGVs) heterogeneous cooperation provides a new breakthrough for the effective application of UAV and UGV.On the basis of introduction of UAV/UGV mathematic...Multiple unmanned air vehicles(UAVs)/unmanned ground vehicles(UGVs) heterogeneous cooperation provides a new breakthrough for the effective application of UAV and UGV.On the basis of introduction of UAV/UGV mathematical model,the characteristics of heterogeneous flocking is analyzed in detail.Two key issues are considered in multi-UGV subgroups,which are Reynolds Rule and Virtual Leader(VL).Receding Horizon Control(RHC) with Particle Swarm Optimization(PSO) is proposed for multiple UGVs flocking,and velocity vector control approach is adopted for multiple UAVs flocking.Then,multiple UAVs and UGVs heterogeneous tracking can be achieved by these two approaches.The feasibility and effectiveness of our proposed method are verified by comparative experiments with artificial potential field method.展开更多
The on line computational burden related to model predictive control (MPC) of large scale constrained systems hampers its real time applications and limits it to slow dynamic process with moderate number of inputs....The on line computational burden related to model predictive control (MPC) of large scale constrained systems hampers its real time applications and limits it to slow dynamic process with moderate number of inputs. To avoid this, an efficient and fast algorithm based on aggregation optimization is proposed in this paper. It only optimizes the current control action at time instant k , while other future control sequences in the optimization horizon are approximated off line by the linear feedback control sequence, so the on line optimization can be converted into a low dimensional quadratic programming problem. Input constraints can be well handled in this scheme. The comparable performance is achieved with existing standard model predictive control algorithm. Simulation results well demonstrate its effectiveness.展开更多
The receding horizon control（RHC） problem is considered for nonlinear Markov jump systems which can be represented by Takagi-Sugeno fuzzy models subject to constraints both on control inputs and on observe outputs.I...The receding horizon control（RHC） problem is considered for nonlinear Markov jump systems which can be represented by Takagi-Sugeno fuzzy models subject to constraints both on control inputs and on observe outputs.In the given receding horizon,for each mode sequence of the T-S modeled nonlinear system with Markov jump parameter,the cost function is optimized by constraints on state trajectories,so that the optimization control input sequences are obtained in order to make the state into a terminal invariant set.Out of the receding horizon,the stability is guaranteed by searching a state feedback control law.Based on such stability analysis,a linear matrix inequality approach for designing receding horizon predictive controller for nonlinear systems subject to constraints both on the inputs and on the outputs is developed.The simulation shows the validity of this method.展开更多
An efficient algorithm is proposed for computing the solution to the constrained finite time optimal control （CFTOC） problem for discrete-time piecewise affine （PWA） systems with a quadratic performance index. The...An efficient algorithm is proposed for computing the solution to the constrained finite time optimal control （CFTOC） problem for discrete-time piecewise affine （PWA） systems with a quadratic performance index. The maximal positively invariant terminal set, which is feasible and invariant with respect to a feedback control law, is computed as terminal target set and an associated Lyapunov function is chosen as terminal cost. The combination of these two components guarantees constraint satisfaction and closed-loop stability for all time. The proposed algorithm combines a dynamic programming strategy with a multi-parametric quadratic programming solver and basic polyhedral manipulation. A numerical example shows that a larger stabilizable set of states can be obtained by the proposed algorithm than precious work.展开更多
Highly Active Antiretroviral Therapy (HAART) has changed the course of human immunodeficiency virus (HIV) treatments since its introduction. However, for many patients, long term continuous HAART is expensive and can ...Highly Active Antiretroviral Therapy (HAART) has changed the course of human immunodeficiency virus (HIV) treatments since its introduction. However, for many patients, long term continuous HAART is expensive and can include problems with drug toxicity and side effects, as well as increased drug resistance. Because of these reasons, some HIV infected patients will voluntarily terminate HAART. Some of these patients will also interrupt the continuous prescribed therapies for short or long periods. After discontinuing HAART, patients will usually experience a rapid increase in viral load coupled with an immediate decline in CD4+ counts. The canonical example of a patient undergoing unsupervised breaks in HAART is that of the “Berlin patient”. In this case, the patient was able to control viral load in the absence of treatment by cycling HAART on and off due to non-related infections. Due to this patient, interest in the use of structured treatment interruptions (STI) as a mechanism to regulate an HIV infection piqued. This paper describes an optimal control approach to determine STI regimen for HIV patients. The optimal STI was implemented in the context of the receding horizon control (RHC) using a mathematical model for the in-vivo dynamics of an HIV type 1 infection. Using available clinical data, we calibrate the model by estimating on a patient specific basis, a best estimable set of parameters using sensitivity analysis and subset selection. We demonstrate how customized STI protocols can be designed through the variation of control parameters on a patient specific basis.展开更多
文摘A receding horizon Hoo control algorithm is presented for linear discrete time-delay system in the presence of constrained input and disturbances. Disturbance attenuation level is optimized at each time instant, and the receding optimization problem includes several linear matrix inequality constraints. When the convex hull is applied to denote the saturating input, the algorithm has better performance. The numerical example can verify this result.
基金supported by the National Natural Science Foundation of China (60974001)Jiangsu "Six Personnel Peak" Talent-Funded Projects
文摘Receding horizon H∞ control scheme which can deal with both the H∞ disturbance attenuation and mean square stability is proposed for a class of discrete-time Markovian jump linear systems when minimizing a given quadratic performance criteria. First, a control law is established for jump systems based on pontryagin’s minimum principle and it can be constructed through numerical solution of iterative equations. The aim of this control strategy is to obtain an optimal control which can minimize the cost function under the worst disturbance at every sampling time. Due to the difficulty of the assurance of stability, then the above mentioned approach is improved by determining terminal weighting matrix which satisfies cost monotonicity condition. The control move which is calculated by using this type of terminal weighting matrix as boundary condition naturally guarantees the mean square stability of the closed-loop system. A sufficient condition for the existence of the terminal weighting matrix is presented in linear matrix inequality （LMI） form which can be solved efficiently by available software toolbox. Finally, a numerical example is given to illustrate the feasibility and effectiveness of the proposed method.
文摘The purpose of this work is to propose a scheme to stabilize the predictive control systems in the practical stability sense. In the paper, the authors dealt with a general discrete predictive control system x j+1|t =f(x j|t , u j|t ) by using the Lyapunov direct method combining with receding horizon control technique, and presented a new condition to guarantee the practical stabilization of the systems. With the proposed results, one can design the optimal controllers easily to practically stabilize the predictive control systems.
基金the Natural Science Foundation of Shandong Province (No.ZR2020MF063)the National Natural Science Foundation of China (No.61873332)。
文摘For a class of discrete-time singular stochastic systems with multi-state delay,the stabilization problem of receding horizon control(RHC)is concerned.Due to the difficulty in solving the proposed optimization problem,the RHC stabilization for such systems has not been solved.By adopting the forward and backward equation technique,the optimization problem is solved completely.A sufficient and necessary condition for the optimization controller to have a unique solution is given when the regularization and pulse-free conditions are satisfied.Based on this controller,an RHC stabilization condition is derived,which is in the form of linear matrix inequality.It is proved that the singular stochastic system with multi-state delay is stable in the mean-square sense under appropriate assumptions when the terminal weighting matrix satisfies the given inequality.Numerical examples show that the proposed RHC method is effective in stabilizing singular stochastic systems with multi-state delay.
基金supported by the National Natural Science Foundation of China (Grant Nos. 60975072 and 60604009)Aeronautical Science Foundation of China (Grant No. 2008ZC01006)+4 种基金Program for New Century Excellent Talents in University of China (Grant No. NCET-10-0021)the Fundamental Research Funds for the Central Universities of China (Grant No. YWF-10-01-A18)Beijing NOVA Program Foundation (Grant No. 2007A017)open Fund of the State Key Laboratory of Virtual Reality Technology and SystemsOpen Fund of the Provincial Key Laboratory for Information Processing Technology, Suzhou University, China (Grant No. KJS1020)
文摘Multiple unmanned air vehicles(UAVs)/unmanned ground vehicles(UGVs) heterogeneous cooperation provides a new breakthrough for the effective application of UAV and UGV.On the basis of introduction of UAV/UGV mathematical model,the characteristics of heterogeneous flocking is analyzed in detail.Two key issues are considered in multi-UGV subgroups,which are Reynolds Rule and Virtual Leader(VL).Receding Horizon Control(RHC) with Particle Swarm Optimization(PSO) is proposed for multiple UGVs flocking,and velocity vector control approach is adopted for multiple UAVs flocking.Then,multiple UAVs and UGVs heterogeneous tracking can be achieved by these two approaches.The feasibility and effectiveness of our proposed method are verified by comparative experiments with artificial potential field method.
文摘The on line computational burden related to model predictive control (MPC) of large scale constrained systems hampers its real time applications and limits it to slow dynamic process with moderate number of inputs. To avoid this, an efficient and fast algorithm based on aggregation optimization is proposed in this paper. It only optimizes the current control action at time instant k , while other future control sequences in the optimization horizon are approximated off line by the linear feedback control sequence, so the on line optimization can be converted into a low dimensional quadratic programming problem. Input constraints can be well handled in this scheme. The comparable performance is achieved with existing standard model predictive control algorithm. Simulation results well demonstrate its effectiveness.
基金supported by the National Natural Science Foundation of China (6097400160904045)+1 种基金National Natural Science Foundation of Jiangsu Province (BK2009068)Six Projects Sponsoring Talent Summits of Jiangsu Province
文摘The receding horizon control（RHC） problem is considered for nonlinear Markov jump systems which can be represented by Takagi-Sugeno fuzzy models subject to constraints both on control inputs and on observe outputs.In the given receding horizon,for each mode sequence of the T-S modeled nonlinear system with Markov jump parameter,the cost function is optimized by constraints on state trajectories,so that the optimization control input sequences are obtained in order to make the state into a terminal invariant set.Out of the receding horizon,the stability is guaranteed by searching a state feedback control law.Based on such stability analysis,a linear matrix inequality approach for designing receding horizon predictive controller for nonlinear systems subject to constraints both on the inputs and on the outputs is developed.The simulation shows the validity of this method.
基金supported by the National Natural Science Foundation of China (60702033)Natural Science Foundation of Zhe-jiang Province (Y107440)
文摘An efficient algorithm is proposed for computing the solution to the constrained finite time optimal control （CFTOC） problem for discrete-time piecewise affine （PWA） systems with a quadratic performance index. The maximal positively invariant terminal set, which is feasible and invariant with respect to a feedback control law, is computed as terminal target set and an associated Lyapunov function is chosen as terminal cost. The combination of these two components guarantees constraint satisfaction and closed-loop stability for all time. The proposed algorithm combines a dynamic programming strategy with a multi-parametric quadratic programming solver and basic polyhedral manipulation. A numerical example shows that a larger stabilizable set of states can be obtained by the proposed algorithm than precious work.
文摘Highly Active Antiretroviral Therapy (HAART) has changed the course of human immunodeficiency virus (HIV) treatments since its introduction. However, for many patients, long term continuous HAART is expensive and can include problems with drug toxicity and side effects, as well as increased drug resistance. Because of these reasons, some HIV infected patients will voluntarily terminate HAART. Some of these patients will also interrupt the continuous prescribed therapies for short or long periods. After discontinuing HAART, patients will usually experience a rapid increase in viral load coupled with an immediate decline in CD4+ counts. The canonical example of a patient undergoing unsupervised breaks in HAART is that of the “Berlin patient”. In this case, the patient was able to control viral load in the absence of treatment by cycling HAART on and off due to non-related infections. Due to this patient, interest in the use of structured treatment interruptions (STI) as a mechanism to regulate an HIV infection piqued. This paper describes an optimal control approach to determine STI regimen for HIV patients. The optimal STI was implemented in the context of the receding horizon control (RHC) using a mathematical model for the in-vivo dynamics of an HIV type 1 infection. Using available clinical data, we calibrate the model by estimating on a patient specific basis, a best estimable set of parameters using sensitivity analysis and subset selection. We demonstrate how customized STI protocols can be designed through the variation of control parameters on a patient specific basis.
