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Baocang Ding - One of the best experts on this subject based on the ideXlab platform.
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one step ahead robust mpc for lpv model with Bounded Disturbance
European Journal of Control, 2020Co-Authors: Jianchen Hu, Baocang DingAbstract:Abstract The on-line model predictive control (MPC) approach usually assumes that with the system information been collected at each sampling instant, the control action can be calculated instantaneously. However, the practicality of this approach is limited by its ability to solve the optimization problem in real-time. In this paper, an improved on-line approach is proposed, where the controller parameters optimized from the previous sampling interval is utilized to calculate the current control action, i.e., the control action is implemented in a one-step ahead fashion. This one-step ahead approach solves the optimization problem during the sampling interval, which means that the controller and the real system are running in a concurrent manner. We first introduce the one-step ahead approach to state measurable case. Then, we extend this approach to state unmeasurable case, where the system output is utilized to estimate the system state. Furthermore, the recursive feasibility and stability are guaranteed for both cases. A numerical example is given to show the effectiveness of the proposed approach.
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a summary of dynamic output feedback robust mpc for linear polytopic uncertainty model with Bounded Disturbance
Mathematical Problems in Engineering, 2020Co-Authors: Baocang Ding, Xiaoming Tang, Jianchen HuAbstract:This paper is the summary and enhancement of the previous studies on dynamic output feedback robust model predictive control (MPC) for the linear parameter varying model (described in a polytope) with additive Bounded Disturbance. When the state is measurable and there is no Bounded Disturbance, the robust MPC has been developed with several paradigms and seems becoming mature. For the output feedback case for the LPV model with Bounded Disturbance, we have published a series of works. Anyway, it lacks a unification of these publications. This paper summarizes the existing results and exposes the ideas in a unified framework. Indeed there is a long way to go for the output feedback case for the LPV model with Bounded Disturbance. This paper can pave the way for further research on output feedback MPC.
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dynamic output feedback predictive control with one free control move for the takagi sugeno model with Bounded Disturbance
IEEE Transactions on Fuzzy Systems, 2019Co-Authors: Jianchen Hu, Baocang DingAbstract:In this paper, the fuzzy model-predictive control for a nonlinear system represented by a discrete-time Takagi–Sugeno model with norm-Bounded Disturbance is studied. An output feedback algorithm is proposed by parameterizing the infinite horizon control moves and estimated states into one free control move and one free estimated state followed by a dynamic output feedback law. Since the introduced free control move and free estimated state are decision variables that bring more degrees of freedom for the optimization, a larger feasibility region and better control performance can be achieved. By properly designing the constraints in the optimization problem, the recursive feasibility and the convergence of the closed-loop system to the neighborhood of the equilibrium are guaranteed. A numerical example is given to illustrate the effectiveness of the proposed algorithm.
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output feedback robust mpc using general polyhedral and ellipsoidal true state bounds for lpv model with Bounded Disturbance
International Journal of Systems Science, 2019Co-Authors: Baocang Ding, Jie Dong, Jianchen HuAbstract:This paper considers the dynamic output feedback robust model predictive control (MPC) for a system with both polytopic model parametric uncertainty and Bounded Disturbance. For this topic, the tec...
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Two-step MPC for systems with input non-linearity and norm-Bounded Disturbance
IET Control Theory & Applications, 2019Co-Authors: Jun Wang, Baocang Ding, Yong WangAbstract:In this study, a novel two-step model predictive control (MPC) for Hammerstein systems subject to norm-Bounded Disturbance is addressed. In the first step, the intermediate control law for the linear part of the system is posed as the solution to the unconstrained MPC problem that minimises a quadratic cost function over a given finite time, for which the solution is determined by a novel Riccati iterative equation. In the second step, the actual control move is obtained by solving non-linear algebraic equation group and desaturation. The quadratic Boundedness technique is used to specify the stability for closed-loop system with norm-Bounded Disturbance, and the sufficient conditions for quadratic convergent of the system state are presented. Simulation results demonstrate the effectiveness of the proposed approach to this class of systems.
Jianchen Hu - One of the best experts on this subject based on the ideXlab platform.
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one step ahead robust mpc for lpv model with Bounded Disturbance
European Journal of Control, 2020Co-Authors: Jianchen Hu, Baocang DingAbstract:Abstract The on-line model predictive control (MPC) approach usually assumes that with the system information been collected at each sampling instant, the control action can be calculated instantaneously. However, the practicality of this approach is limited by its ability to solve the optimization problem in real-time. In this paper, an improved on-line approach is proposed, where the controller parameters optimized from the previous sampling interval is utilized to calculate the current control action, i.e., the control action is implemented in a one-step ahead fashion. This one-step ahead approach solves the optimization problem during the sampling interval, which means that the controller and the real system are running in a concurrent manner. We first introduce the one-step ahead approach to state measurable case. Then, we extend this approach to state unmeasurable case, where the system output is utilized to estimate the system state. Furthermore, the recursive feasibility and stability are guaranteed for both cases. A numerical example is given to show the effectiveness of the proposed approach.
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a summary of dynamic output feedback robust mpc for linear polytopic uncertainty model with Bounded Disturbance
Mathematical Problems in Engineering, 2020Co-Authors: Baocang Ding, Xiaoming Tang, Jianchen HuAbstract:This paper is the summary and enhancement of the previous studies on dynamic output feedback robust model predictive control (MPC) for the linear parameter varying model (described in a polytope) with additive Bounded Disturbance. When the state is measurable and there is no Bounded Disturbance, the robust MPC has been developed with several paradigms and seems becoming mature. For the output feedback case for the LPV model with Bounded Disturbance, we have published a series of works. Anyway, it lacks a unification of these publications. This paper summarizes the existing results and exposes the ideas in a unified framework. Indeed there is a long way to go for the output feedback case for the LPV model with Bounded Disturbance. This paper can pave the way for further research on output feedback MPC.
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dynamic output feedback predictive control with one free control move for the takagi sugeno model with Bounded Disturbance
IEEE Transactions on Fuzzy Systems, 2019Co-Authors: Jianchen Hu, Baocang DingAbstract:In this paper, the fuzzy model-predictive control for a nonlinear system represented by a discrete-time Takagi–Sugeno model with norm-Bounded Disturbance is studied. An output feedback algorithm is proposed by parameterizing the infinite horizon control moves and estimated states into one free control move and one free estimated state followed by a dynamic output feedback law. Since the introduced free control move and free estimated state are decision variables that bring more degrees of freedom for the optimization, a larger feasibility region and better control performance can be achieved. By properly designing the constraints in the optimization problem, the recursive feasibility and the convergence of the closed-loop system to the neighborhood of the equilibrium are guaranteed. A numerical example is given to illustrate the effectiveness of the proposed algorithm.
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output feedback robust mpc using general polyhedral and ellipsoidal true state bounds for lpv model with Bounded Disturbance
International Journal of Systems Science, 2019Co-Authors: Baocang Ding, Jie Dong, Jianchen HuAbstract:This paper considers the dynamic output feedback robust model predictive control (MPC) for a system with both polytopic model parametric uncertainty and Bounded Disturbance. For this topic, the tec...
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Dynamic Output Feedback Predictive Control With One Free Control Move for the Takagi–Sugeno Model With Bounded Disturbance
IEEE Transactions on Fuzzy Systems, 2019Co-Authors: Jianchen Hu, Baocang DingAbstract:In this paper, the fuzzy model-predictive control for a nonlinear system represented by a discrete-time Takagi-Sugeno model with norm-Bounded Disturbance is studied. An output feedback algorithm is proposed by parameterizing the infinite horizon control moves and estimated states into one free control move and one free estimated state followed by a dynamic output feedback law. Since the introduced free control move and free estimated state are decision variables that bring more degrees of freedom for the optimization, a larger feasibility region and better control performance can be achieved. By properly designing the constraints in the optimization problem, the recursive feasibility and the convergence of the closed-loop system to the neighborhood of the equilibrium are guaranteed. A numerical example is given to illustrate the effectiveness of the proposed algorithm.
Xubin Ping - One of the best experts on this subject based on the ideXlab platform.
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Output Feedback Model Predictive Control of Interval Type-2 T–S Fuzzy System With Bounded Disturbance
IEEE Transactions on Fuzzy Systems, 2020Co-Authors: Xubin Ping, Witold PedryczAbstract:In this paper, the problem of output feedback model predictive control (MPC) for interval Type-2 Takagi-Sugeno fuzzy systems with Bounded Disturbance is investigated. The output feedback MPC approach includes an offline design of the state observer to estimate true states and predict bounds of future estimation error sets, and an online problem that optimizes the controller gains to stabilize the closed-loop observer system. The dynamics of the estimation error system is determined by the offline designed observer gain, and bounds of which are online refreshed by scaling a minimal robust positively invariant (RPI) set via a scalar. The optimized controller gains steer the current estimated state from an RPI set into another one such that future estimated states are invariant in the subsequent RPI set. Convergence of the estimation error system and stability condition on the closed-loop observer system in terms of linear matrix inequalities are derived using the technique of S-procedure. The estimation error and estimated state converge within the corresponding time-varying RPI sets, and therefore, recursive feasibility of the optimization problem and input-to-state stability of the closed-loop observer system with respect to the estimation error and Bounded Disturbance are ensured. For reducing the online computational burden, a lookup table that stores the offline calculated controller gains with corresponding regions of attraction is offline constructed for online searching real-time controller gains. A simulation example is given to show the effectiveness of the approach.
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output feedback model predictive control of interval type 2 t s fuzzy system with Bounded Disturbance
IEEE Transactions on Fuzzy Systems, 2020Co-Authors: Xubin Ping, Witold PedryczAbstract:In this paper, the problem of output feedback model predictive control (MPC) for interval Type-2 Takagi–Sugeno fuzzy systems with Bounded Disturbance is investigated. The output feedback MPC approach includes an offline design of the state observer to estimate true states and predict bounds of future estimation error sets, and an online problem that optimizes the controller gains to stabilize the closed-loop observer system. The dynamics of the estimation error system is determined by the offline designed observer gain, and bounds of which are online refreshed by scaling a minimal robust positively invariant (RPI) set via a scalar. The optimized controller gains steer the current estimated state from an RPI set into another one such that future estimated states are invariant in the subsequent RPI set. Convergence of the estimation error system and stability condition on the closed-loop observer system in terms of linear matrix inequalities are derived using the technique of S-procedure. The estimation error and estimated state converge within the corresponding time-varying RPI sets, and therefore, recursive feasibility of the optimization problem and input-to-state stability of the closed-loop observer system with respect to the estimation error and Bounded Disturbance are ensured. For reducing the online computational burden, a lookup table that stores the offline calculated controller gains with corresponding regions of attraction is offline constructed for online searching real-time controller gains. A simulation example is given to show the effectiveness of the approach.
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dynamic output feedback robust mpc for lpv systems subject to input saturation and Bounded Disturbance
International Journal of Control Automation and Systems, 2017Co-Authors: Xubin Ping, Zhiwu Li, Abdulrahman AlahmariAbstract:For linear parameter varying (LPV) systems with unknown scheduling parameters and Bounded Disturbance, a synthesis approach of dynamic output feedback robust model predictive control (OFRMPC) with input saturation is investigated. By pre-specifying partial controller parameters, a main optimization problem is solved by convex optimization to reduce the on-line computational burden. The main optimization problem guarantees that the estimated state and estimation error converge within the corresponding invariant sets such that recursive feasibility and robust stability are guaranteed. The consideration of input saturation in the main optimization problem improves the control performance. Two numerical examples are given to illustrate the effectiveness of the approach.
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Robust MPC for perturbed nonholonomic vehicle
2016 35th Chinese Control Conference (CCC), 2016Co-Authors: Peng Wang, Xubin Ping, Xinxi Feng, Weihua Li, Wangsheng YuAbstract:This paper considers the robust model predictive control (RMPC) synthesis for the simultaneous tracking and regulation problem (STRP) of nonholonomic vehicle, which is perturbed by Bounded Disturbance. Considering the Bounded Disturbance, a robust positively invariant (RPI) set with a designed admissible controller is first given for the perturbed nonholonomic vehicle, which contributes to the foundation of the RMPC synthesis. Then by extending the “tube-based” approach to the nonholonomic, nonlinear and continuous-time case, we employ the developed RPI set for determining the robust tubes and terminal state region, and construct the optimal control problem. Following the contributed properties of the developed RPI set and extended “tube-based” approach, a robust MPC algorithm is finally proposed with the guarantees of recursive feasibility and robustly exponential convergence, i.e., robustly exponential stability of tracking and regulation errors.
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dynamic output feedback robust mpc using general polyhedral state bounds for the polytopic uncertain system with Bounded Disturbance
Asian Journal of Control, 2016Co-Authors: Baocang Ding, Xubin PingAbstract:This paper considers the dynamic output feedback robust model predictive control MPC for a system with both polytopic model parametric uncertainty and Bounded Disturbance. For this topic, the techniques for handling the unknown true state are crucial, and the strict guarantee of the input/output/state constraints favors replacing the true state by its bound in the optimization problems. The previous utilized polyhedral bounds, constructed by virtue of the error signals which are some linear combinations of the true state, the estimated state and the output, are generalized, where a bias item is utilized. Based on this unified bounding approach, new techniques for handling the unknown true state are given for both the main and the auxiliary optimization problems. As before, the main optimization problem calculates the control law parameters conditionally, and the auxiliary optimization problem determines the time to refresh these parameters. By applying the proposed method, the augmented state of the closed-loop system is guaranteed to converge to the neighborhood of the equilibrium point. A numerical example is given to illustrate the effectiveness of the new method.
Witold Pedrycz - One of the best experts on this subject based on the ideXlab platform.
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Output Feedback Model Predictive Control of Interval Type-2 T–S Fuzzy System With Bounded Disturbance
IEEE Transactions on Fuzzy Systems, 2020Co-Authors: Xubin Ping, Witold PedryczAbstract:In this paper, the problem of output feedback model predictive control (MPC) for interval Type-2 Takagi-Sugeno fuzzy systems with Bounded Disturbance is investigated. The output feedback MPC approach includes an offline design of the state observer to estimate true states and predict bounds of future estimation error sets, and an online problem that optimizes the controller gains to stabilize the closed-loop observer system. The dynamics of the estimation error system is determined by the offline designed observer gain, and bounds of which are online refreshed by scaling a minimal robust positively invariant (RPI) set via a scalar. The optimized controller gains steer the current estimated state from an RPI set into another one such that future estimated states are invariant in the subsequent RPI set. Convergence of the estimation error system and stability condition on the closed-loop observer system in terms of linear matrix inequalities are derived using the technique of S-procedure. The estimation error and estimated state converge within the corresponding time-varying RPI sets, and therefore, recursive feasibility of the optimization problem and input-to-state stability of the closed-loop observer system with respect to the estimation error and Bounded Disturbance are ensured. For reducing the online computational burden, a lookup table that stores the offline calculated controller gains with corresponding regions of attraction is offline constructed for online searching real-time controller gains. A simulation example is given to show the effectiveness of the approach.
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output feedback model predictive control of interval type 2 t s fuzzy system with Bounded Disturbance
IEEE Transactions on Fuzzy Systems, 2020Co-Authors: Xubin Ping, Witold PedryczAbstract:In this paper, the problem of output feedback model predictive control (MPC) for interval Type-2 Takagi–Sugeno fuzzy systems with Bounded Disturbance is investigated. The output feedback MPC approach includes an offline design of the state observer to estimate true states and predict bounds of future estimation error sets, and an online problem that optimizes the controller gains to stabilize the closed-loop observer system. The dynamics of the estimation error system is determined by the offline designed observer gain, and bounds of which are online refreshed by scaling a minimal robust positively invariant (RPI) set via a scalar. The optimized controller gains steer the current estimated state from an RPI set into another one such that future estimated states are invariant in the subsequent RPI set. Convergence of the estimation error system and stability condition on the closed-loop observer system in terms of linear matrix inequalities are derived using the technique of S-procedure. The estimation error and estimated state converge within the corresponding time-varying RPI sets, and therefore, recursive feasibility of the optimization problem and input-to-state stability of the closed-loop observer system with respect to the estimation error and Bounded Disturbance are ensured. For reducing the online computational burden, a lookup table that stores the offline calculated controller gains with corresponding regions of attraction is offline constructed for online searching real-time controller gains. A simulation example is given to show the effectiveness of the approach.
Hieu Trinh - One of the best experts on this subject based on the ideXlab platform.
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New results on state bounding for discrete-time systems with interval time-varying delay and Bounded Disturbance inputs
IET Control Theory & Applications, 2014Co-Authors: Le Van Hien, Nguyen Thanh An, Hieu TrinhAbstract:This study considers the problem of state bounding for a class of discrete-time systems with interval time-varying delay and Bounded Disturbance inputs. By using an improved Lyapunov-Krasovskii functional combining with the delay-decomposition technique and the reciprocally convex approach, the authors first derive new delay-dependent conditions in terms of matrix inequalities to guarantee the existence of a ball such that, for any initial condition, the state trajectory of the system is either Bounded within that ball or converges exponentially within it. On the basis of these new conditions, the authors then derive an improved ellipsoid reachable set bounding and a new result on exponential stability of discrete-time systems with interval time-varying delay. Numerical examples are presented to show the effectiveness of the obtained results and improvement over existing results.
