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Bounded Disturbance

The Experts below are selected from a list of 8556 Experts worldwide ranked by ideXlab platform

Baocang Ding – 1st expert on this subject based on the ideXlab platform

  • one step ahead robust mpc for lpv model with Bounded Disturbance
    European Journal of Control, 2020
    Co-Authors: Jianchen Hu, Baocang Ding

    Abstract:

    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.

  • a summary of dynamic output feedback robust mpc for linear polytopic uncertainty model with Bounded Disturbance
    Mathematical Problems in Engineering, 2020
    Co-Authors: Baocang Ding, Xiaoming Tang, Jianchen Hu

    Abstract:

    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.

  • dynamic output feedback predictive control with one free control move for the takagi sugeno model with Bounded Disturbance
    IEEE Transactions on Fuzzy Systems, 2019
    Co-Authors: Jianchen Hu, Baocang Ding

    Abstract:

    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.

Jianchen Hu – 2nd expert on this subject based on the ideXlab platform

  • one step ahead robust mpc for lpv model with Bounded Disturbance
    European Journal of Control, 2020
    Co-Authors: Jianchen Hu, Baocang Ding

    Abstract:

    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.

  • a summary of dynamic output feedback robust mpc for linear polytopic uncertainty model with Bounded Disturbance
    Mathematical Problems in Engineering, 2020
    Co-Authors: Baocang Ding, Xiaoming Tang, Jianchen Hu

    Abstract:

    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.

  • dynamic output feedback predictive control with one free control move for the takagi sugeno model with Bounded Disturbance
    IEEE Transactions on Fuzzy Systems, 2019
    Co-Authors: Jianchen Hu, Baocang Ding

    Abstract:

    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 – 3rd expert on this subject based on the ideXlab platform

  • Output Feedback Model Predictive Control of Interval Type-2 T–S Fuzzy System With Bounded Disturbance
    IEEE Transactions on Fuzzy Systems, 2020
    Co-Authors: Xubin Ping, Witold Pedrycz

    Abstract:

    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.

  • output feedback model predictive control of interval type 2 t s fuzzy system with Bounded Disturbance
    IEEE Transactions on Fuzzy Systems, 2020
    Co-Authors: Xubin Ping, Witold Pedrycz

    Abstract:

    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.

  • dynamic output feedback robust mpc for lpv systems subject to input saturation and Bounded Disturbance
    International Journal of Control Automation and Systems, 2017
    Co-Authors: Xubin Ping, Zhiwu Li, Abdulrahman Alahmari

    Abstract:

    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.