The Experts below are selected from a list of 16596 Experts worldwide ranked by ideXlab platform

Ralph Kennel - One of the best experts on this subject based on the ideXlab platform.

  • direct model predictive control with an extended Prediction Horizon for quasi z source inverters
    Conference of the Industrial Electronics Society, 2016
    Co-Authors: Ayman Ayad, Petros Karamanakos, Ralph Kennel
    Abstract:

    This paper presents a direct model predictive control (MPC) with an extended Prediction Horizon for the quasi-Z-source inverter (qZSI). The proposed MPC controls both sides of the qZSI based on the inductor current of the qZS network and the output current of the ac side. In order to improve the system performance, the MPC with extended Prediction Horizon is used. However, increasing the Prediction Horizon results in a huge increase in the computational burden which prevents the implementation of the MPC in real time. To solve this problem, two techniques are utilized, namely a branch-and-bound scheme and move blocking strategy. In this work, the discrete-time model of the qZSI is derived that accurately captures all operating modes and states. Then, the steady-state and transient operations of the qZSI with the proposed MPC are experimentally examined. The results confirm that by extending the Prediction Horizon, the qZSI behavior is significantly improved.

  • IECON - Direct model predictive control with an extended Prediction Horizon for quasi-Z-source inverters
    IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, 2016
    Co-Authors: Ayman Ayad, Petros Karamanakos, Ralph Kennel
    Abstract:

    This paper presents a direct model predictive control (MPC) with an extended Prediction Horizon for the quasi-Z-source inverter (qZSI). The proposed MPC controls both sides of the qZSI based on the inductor current of the qZS network and the output current of the ac side. In order to improve the system performance, the MPC with extended Prediction Horizon is used. However, increasing the Prediction Horizon results in a huge increase in the computational burden which prevents the implementation of the MPC in real time. To solve this problem, two techniques are utilized, namely a branch-and-bound scheme and move blocking strategy. In this work, the discrete-time model of the qZSI is derived that accurately captures all operating modes and states. Then, the steady-state and transient operations of the qZSI with the proposed MPC are experimentally examined. The results confirm that by extending the Prediction Horizon, the qZSI behavior is significantly improved.

  • model predictive torque control with an extended Prediction Horizon for electrical drive systems
    International Journal of Control, 2015
    Co-Authors: Fengxiang Wang, Ralph Kennel, Zhenbin Zhang, Jose Rodriguez
    Abstract:

    This paper presents a model predictive torque control method for electrical drive systems. A two-step Prediction Horizon is achieved by considering the reduction of the torque ripples. The electromagnetic torque and the stator flux error between predicted values and the references, and an over-current protection are considered in the cost function design. The best voltage vector is selected by minimising the value of the cost function, which aims to achieve a low torque ripple in two intervals. The study is carried out experimentally. The results show that the proposed method achieves good performance in both steady and transient states.

  • variable switching point predictive torque control with extended Prediction Horizon
    International Conference on Industrial Technology, 2015
    Co-Authors: Ilias Alevras, Petros Karamanakos, S N Manias, Ralph Kennel
    Abstract:

    This paper introduces the extension of the Prediction Horizon in the one-step variable switching point predictive torque control (VSP2TC). Even though in the majority of power electronics applications using model predictive control (MPC) based schemes, a Prediction Horizon of one suffices, the use of longer Prediction Horizons offers substantial performance benefits. To highlight this, the proposed algorithm is applied to a low voltage (LV) drive system, which comprises a two-level inverter and an induction machine (IM). As it is shown, by extending the Prediction Horizon, important drive quality indices, such as the torque ripple, and the total harmonic distortion (THD) of the stator currents are reduced. However, the computational effort required for solving the formulated optimization problem in real time can be overwhelming. The implementation of a branch-and-bound technique is introduced to front this tricky matter. Simulation results verify the performance of the presented control strategy.

  • ICIT - Variable switching point predictive torque control with extended Prediction Horizon
    2015 IEEE International Conference on Industrial Technology (ICIT), 2015
    Co-Authors: Ilias Alevras, Petros Karamanakos, S N Manias, Ralph Kennel
    Abstract:

    This paper introduces the extension of the Prediction Horizon in the one-step variable switching point predictive torque control (VSP2TC). Even though in the majority of power electronics applications using model predictive control (MPC) based schemes, a Prediction Horizon of one suffices, the use of longer Prediction Horizons offers substantial performance benefits. To highlight this, the proposed algorithm is applied to a low voltage (LV) drive system, which comprises a two-level inverter and an induction machine (IM). As it is shown, by extending the Prediction Horizon, important drive quality indices, such as the torque ripple, and the total harmonic distortion (THD) of the stator currents are reduced. However, the computational effort required for solving the formulated optimization problem in real time can be overwhelming. The implementation of a branch-and-bound technique is introduced to front this tricky matter. Simulation results verify the performance of the presented control strategy.

Chris Manzie - One of the best experts on this subject based on the ideXlab platform.

  • Continuity and monotonicity of the MPC value function with respect to sampling time and Prediction Horizon
    Automatica, 2016
    Co-Authors: Vincent Bachtiar, William H. Moase, Eric C Kerrigan, Chris Manzie
    Abstract:

    The digital implementation of model predictive control (MPC) is fundamentally governed by two design parameters; sampling time and Prediction Horizon. Knowledge of the properties of the value function with respect to the parameters can be used for developing optimization tools to find optimal system designs. In particular, these properties are continuity and monotonicity. This paper presents analytical results to reveal the smoothness properties of the MPC value function in open- and closed-loop for constrained linear systems. Continuity of the value function and its differentiability for a given number of Prediction steps are proven mathematically and confirmed with numerical results. Non-monotonicity is shown from the ensuing numerical investigation. It is shown that increasing sampling rate and/or Prediction Horizon does not always lead to an improved closed-loop performance, particularly at faster sampling rates.

  • Smoothness properties of the MPC value function with respect to sampling time and Prediction Horizon
    2015 10th Asian Control Conference (ASCC), 2015
    Co-Authors: Vincent Bachtiar, William H. Moase, Eric C Kerrigan, Chris Manzie
    Abstract:

    Sampling time and the Prediction Horizon length are two underlying design choices for the implementation of model predictive control (MPC). Smoothness properties of the open- and closed-loop value function with respect to the two parameters are essential to characterise for the purpose of providing knowledge that is useful in the context of MPC design optimisation to maximise system performance. Specifically, these properties are continuity, differentiability and monotonicity. This paper presents both numerical and analytical results to reveal the smoothness properties of the value function. Increasing sampling rate and/or Prediction Horizon does not necessarily improve closed-loop performance. Furthermore, the value function in open-loop under input constraints is differentiable, which may contradict the traditional thinking and expectations.

  • ASCC - Smoothness properties of the MPC value function with respect to sampling time and Prediction Horizon
    2015 10th Asian Control Conference (ASCC), 2015
    Co-Authors: Vincent Bachtiar, William H. Moase, Eric C Kerrigan, Chris Manzie
    Abstract:

    Sampling time and the Prediction Horizon length are two underlying design choices for the implementation of model predictive control (MPC). Smoothness properties of the open- and closed-loop value function with respect to the two parameters are essential to characterise for the purpose of providing knowledge that is useful in the context of MPC design optimisation to maximise system performance. Specifically, these properties are continuity, differentiability and monotonicity. This paper presents both numerical and analytical results to reveal the smoothness properties of the value function. Increasing sampling rate and/or Prediction Horizon does not necessarily improve closed-loop performance. Furthermore, the value function in open-loop under input constraints is differentiable, which may contradict the traditional thinking and expectations.

Ammar Hasan - One of the best experts on this subject based on the ideXlab platform.

  • Machine Learning Based Adaptive Prediction Horizon in Finite Control Set Model Predictive Control
    IEEE Access, 2018
    Co-Authors: Muhammad Saleh Murtaza Gardezi, Ammar Hasan
    Abstract:

    In this paper, an adaptive Prediction Horizon approach based on machine learning is presented for the finite control set model predictive control (FCS-MPC) of power converters. Usually, in FCS-MPC, the Prediction Horizon is kept constant. A large Prediction Horizon improves performance, however, it significantly increases the computational cost. The Prediction Horizon is typically chosen to be just large enough to give the required performance. We present a novel technique, where the Prediction Horizon adapts to the states of the converter. We define a cyber-physical objective function that penalizes both the error in converter performance and computational complexity. We perform several offline simulations to find the optimal Prediction Horizon based on the instantaneous state of the converter, based on a cyber-physical objective function. An artificial neural network is trained to calculate the optimal Prediction Horizon in run time. The proposed scheme allows a varying Prediction Horizon that reduces the overall computational complexity, while guaranteeing the required physical performance. The simulations and experimental results of the proposed technique justify the usefulness of our approach.

  • Unit Prediction Horizon Binary Search-Based Model Predictive Control of Full-Bridge DC–DC Converter
    IEEE Transactions on Control Systems Technology, 2018
    Co-Authors: Junaid Saeed, Ammar Hasan
    Abstract:

    In this paper, we present a unit Prediction Horizon binary search-based nonlinear model predictive control (MPC) of phase shift full-bridge dc-dc converter, working in discontinuous conduction mode. The objective of the control algorithm is to regulate the output voltage of the converter to a reference set point while also respecting a nonlinear constraint on the peak inductor current. We utilize a large signal dynamic model of the converter based on the analytical solution of piecewise differential equations. The developed large signal model and the unit Prediction Horizon allow us to use a novel and computationally efficient approach based on the binary search algorithm to solve the optimization problem in the nonlinear MPC. The proposed algorithm offers faster optimization solution for MPC, thereby facilitating high frequency operation of the converter. We compare the computational complexity of the proposed algorithm with the integrated perturbation analysis and sequential quadratic programming algorithm. Furthermore, we address the problem of offset in the output voltage due to unmeasured load disturbances. We include experimental results illustrating the effectiveness of the proposed control scheme on a 500-W converter setup operating on a switching frequency of 20 kHz.

Saeid Nahavandi - One of the best experts on this subject based on the ideXlab platform.

  • mpc based motion cueing algorithm with short Prediction Horizon using exponential weighting
    Systems Man and Cybernetics, 2016
    Co-Authors: Arash Mohammadi, Houshyar Asadi, Shady Mohamed, Kyle Nelson, Saeid Nahavandi
    Abstract:

    A motion simulator is an effective tool for training a driver in a safe environment by mimicking motion similar to the real world. To give a realistic feeling of driving and avoid motion sickness, an accurate motion cueing algorithm is required to restrict the platform within the allowed workspace range while regenerating an appropriate motion feeling for the simulator driver. Recently, employing Model Predictive Control (MPC) in the motion cueing algorithm has become popular. In this control method, by predicting future dynamics, an input is optimized to minimize a cost function over a Prediction Horizon while respecting the constraints. Reducing the Prediction Horizon is desirable to minimize the computational burden; however it draws the system toward instability. In this research, applying a nonuniform weighting method is proposed to stabilize the motion cueing algorithm using MPC with short Prediction Horizon and optimized weighting adjustment. Simulation results show the effectiveness of the proposed method.

  • SMC - MPC-based motion cueing algorithm with short Prediction Horizon using exponential weighting
    2016 IEEE International Conference on Systems Man and Cybernetics (SMC), 2016
    Co-Authors: Arash Mohammadi, Houshyar Asadi, Shady Mohamed, Kyle Nelson, Saeid Nahavandi
    Abstract:

    A motion simulator is an effective tool for training a driver in a safe environment by mimicking motion similar to the real world. To give a realistic feeling of driving and avoid motion sickness, an accurate motion cueing algorithm is required to restrict the platform within the allowed workspace range while regenerating an appropriate motion feeling for the simulator driver. Recently, employing Model Predictive Control (MPC) in the motion cueing algorithm has become popular. In this control method, by predicting future dynamics, an input is optimized to minimize a cost function over a Prediction Horizon while respecting the constraints. Reducing the Prediction Horizon is desirable to minimize the computational burden; however it draws the system toward instability. In this research, applying a nonuniform weighting method is proposed to stabilize the motion cueing algorithm using MPC with short Prediction Horizon and optimized weighting adjustment. Simulation results show the effectiveness of the proposed method.

Masoud Nikravesh - One of the best experts on this subject based on the ideXlab platform.

  • Shortest-Prediction-Horizon non-linear model-predictive control
    Chemical Engineering Science, 1998
    Co-Authors: Sairam Valluri, Masoud Soroush, Masoud Nikravesh
    Abstract:

    Abstract This article concerns non-linear control of single-input-single-output processes with input constraints and deadtimes. The problem of input-output linearization in continuous time is formulated as a model-predictive control problem, for processes with full-state measurements and for processes with incomplete state measurements and deadtimes. This model-predictive control formulation allows one (i) to establish the connections between model-predictive and input-output linearizing control methods; and (ii) to solve directly the problems of constraint handling and windup in input-output linearizing control. The derived model-predictive control laws have the shortest possible Prediction Horizon and explicit analytical form, and thus their implementation does not require on-line optimization. Necessary conditions for stability of the closed-loop system under the constrained dynamic control laws are given. The connections between (a) the developed control laws and (b) the model state feedback control and the modified internal model control are established. The application and performance of the derived controllers are demonstrated by numerical simulations of chemical and biochemical reactor examples.

  • Shortest-Prediction Horizon Nonlinear Model Predictive Control 1
    IFAC Proceedings Volumes, 1996
    Co-Authors: Masoud Soroush, Masoud Nikravesh
    Abstract:

    Abstract This article presents a continuous-time formulation of model predictive control. This formulation allows (i) to establish the connections between model predictive control and input-output linearizing control methods and (ii) to address the problems of constraint handling and windup in input-output linearizing control methods. Model predictive control laws with the shortest possible Prediction Horizon are derived for constrained nonlinear processes with deadtime. They have explicit analytical form, and thus their implementation does not require on-line optimization. Furthermore, in the absence of constraints, they are input-output linearizing.