Robust nonlinear generalized predictive control of a permanent magnet synchronous motor with an anti-windup compensator

This paper presents a robust nonlinear generalized predictive control (RNGPC) strategy applied to a permanent magnet synchronous motor (PMSM) for speed trajectory tracking and disturbance rejection. The nonlinear predictive control law is derived by using a newly defined design cost function. The Taylor series expansion is used to carry out the prediction in a finite horizon. No information about the external perturbation and parameters uncertainties are needed to ensure the robustness of the proposed RNGPC. Moreover, to maintain the phase current within the limits using saturation blocks, a cascaded structure is adopted and an anti-windup compensator is proposed. The validity of the proposed control strategy is implemented on a dSPACE DS1104 board driving in real-time a 0.25 kW PMSM. Experimental results have demonstrated the stability, robustness and the effectiveness of the proposed control strategy regarding trajectory tracking and disturbance rejection

Real-time predictive control for SI engines using linear parameter-varying models

As a response to the ever more stringent emission standards, automotive engines have become more complex with more actuators. The traditional approach of using many single-input single output controllers has become more difficult to design, due to complex system interactions and constraints. Model predictive control offers an attractive solution to this problem because of its ability to handle multi-input multi-output systems with constraints on inputs and outputs. The application of model based predictive control to automotive engines is explored below and a multivariable engine torque and air-fuel ratio controller is described using a quasi-LPV model predictive control methodology. Compared with the traditional approach of using SISO controllers to control air fuel ratio and torque separately, an advantage is that the interactions between the air and fuel paths are handled explicitly. Furthermore, the quasi-LPV model-based approach is capable of capturing the model nonlinearities within a tractable linear structure, and it has the potential of handling hard actuator constraints. The control design approach was applied to a 2010 Chevy Equinox with a 2.4L gasoline engine and simulation results are presented. Since computational complexity has been the main limiting factor for fast real time applications of MPC, we present various simplifications to reduce computational requirements. A benchmark comparison of estimated computational speed is included

Robust nonlinear predictive controller for multivariable nonlinear systems with different relative degree

This paper presents a robust nonlinear generalized predictive control (RNGPC) strategy applied to a permanent magnet synchronous motor (PMSM) for speed trajectory tracking and disturbance rejection. The nonlinear predictive control law is derived by using a newly defined design cost function. The Taylor series expansion is used to carry out the prediction in a finite horizon. No information about the external perturbation and parameters uncertainties are needed to ensure the robustness of the proposed RNGPC. Moreover, to maintain the phase current within the limits using saturation blocks, a cascaded structure is adopted and an anti-windup compensator is proposed. The validity of the proposed control strategy is implemented on a dSPACE DS1104 board driving in real-time a 0.25 kW PMSM. Experimental results have demonstrated the stability, robustness and the effectiveness of the proposed control strategy regarding trajectory tracking and disturbance rejection

Robust nonlinear generalised predictive control for a class of uncertain nonlinear systems via an integral sliding mode approach

In this paper, a robust nonlinear generalised predictive control (GPC) method is proposed by combining an integral sliding mode approach. The composite controller can guarantee zero steady-state error for a class of uncertain nonlinear systems in the presence of both matched and unmatched disturbances. Indeed, it is well known that the traditional GPC based on Taylor series expansion cannot completely reject unknown disturbance and achieve offset-free tracking performance. To deal with this problem, the existing approaches are enhanced by avoiding the use of the disturbance observer and modifying the gain function of the nonlinear integral sliding surface. This modified strategy appears to be more capable of achieving both the disturbance rejection and the nominal prescribed specifications for matched disturbance. Simulation results demonstrate the effectiveness of the proposed approach

Robust nonlinear generalized predictive control of a permanent magnet synchronous motor with an anti-windup compensator

This paper presents a robust nonlinear generalized predictive control (RNGPC) strategy applied to a permanent magnet synchronous motor (PMSM) for speed trajectory tracking and disturbance rejection. The nonlinear predictive control law is derived by using a newly defined design cost function. The Taylor series expansion is used to carry out the prediction in a finite horizon. No information about the external perturbation and parameters uncertainties are needed to ensure the robustness of the proposed RNGPC. Moreover, to maintain the phase current within the limits using saturation blocks, a cascaded structure is adopted and an anti-windup compensator is proposed. The validity of the proposed control strategy is implemented on a dSPACE DS1104 board driving in real-time a 0.25 kW PMSM. Experimental results have demonstrated the stability, robustness and the effectiveness of the proposed control strategy regarding trajectory tracking and disturbance rejection

Robust nonlinear generalised predictive control for a class of uncertain nonlinear systems via an integral sliding mode approach

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Control on 15/02/2016, available online: http://dx.doi.org/10.1080/00207179.2016.1145356.In this paper, a robust nonlinear generalised predictive control (GPC) method is proposed by combining an integral sliding mode approach. The composite controller can guarantee zero steady-state error for a class of uncertain nonlinear systems in the presence of both matched and unmatched disturbances. Indeed, it is well known that the traditional GPC based on Taylor series expansion cannot completely reject unknown disturbance and achieve offset-free tracking performance. To deal with this problem, the existing approaches are enhanced by avoiding the use of the disturbance observer and modifying the gain function of the nonlinear integral sliding surface. This modified strategy appears to be more capable of achieving both the disturbance rejection and the nominal prescribed specifications for matched disturbance. Simulation results demonstrate the effectiveness of the proposed approach

Contribuições ao controle preditivo robusto de sistemas com atraso

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Elétrica.Este trabalho apresenta um estudo direcionado à análise e projeto de controladores preditivos baseados em modelo para sistemas lineares e não lineares com atraso, visando a melhoria da robustez e levando em conta aspectos práticos de aplicação. O estudo considera sistemas lineares estáveis, integradores, instáveis, não lineares e um estudo de caso na área da medicina. No primeiro caso, estudam-se as condições que deve satisfazer um sistema de controle preditivo linear para garantir a estabilidade robusta de malha fechada quando se controlam processos estáveis com incertezas no atraso. No segundo e terceiro apresenta-se o estudo do controle preditivo de sistemas lineares com dinâmica integradora ou instável e atraso e propõe-se um novo algoritmo que utiliza idéias de controladores de compensação de tempo morto robustos. No caso de sistemas não lineares propõe-se a extensão das propriedades de robustez de um algoritmo utilizado em sistemas de controle lineares com atraso para processos não lineares com atraso. Finalmente apresenta-se um estudo de caso aplicado à dosagem de anestesia a pacientes durante cirurgia, com garantia de estabilidade sob condições de operação. Resultados de simulação ou ensaios numa planta piloto são apresentados para cada tipo de controlador proposto, mostrando as vantagens dos métodos de ajuste, que principalmente estão orientados a melhorar a robustez e permitir a sua aplicação simples em processos industriais. This work presents the analysis and design of model-based predictive controllers MPC for linear and nonlinear dead-time systems. The proposed control strategies take into account both practical aspects and robustness specifications. The study considers: (i) stable, integrative and unstable linear systems, (ii) nonlinear systems and (iii) a medicine area case study. In the first case, the necessary conditions to guarantee the robust stability when MPC are applied to stable processes with dead time are studied. In addition, it is proposed a robust predictive controller for integrative or unstable linear processes with dead time. This controller uses some of the robustness ideas of dead-time compensators. In the second case, the robustness properties of linear predictive controllers are extended for stable nonlinear processes with dead time. Finally, it is presented a case study where the robust MPC is applied to drug dosing during anesthesia in patients undergoing surgery. Also, a tuning rule is obtained to guarantee the stability of the system under operation conditions. Experimental or simulation results are presented for each proposed controller, to illustrate the advantages of the tuning methods

Real time simulation of nonlinear generalized predictive control for wind energy conversion system with nonlinear observer

In order to make a wind power generation truly cost-effective and reliable, an advanced control techniques must be used. In this paper, we develop a new control strategy, using nonlinear generalized predictive control (NGPC) approach, for DFIG-based wind turbine. The proposed control law is based on two points: NGPC-based torque-current control loop generating the rotor reference voltage and NGPC-based speed control loop that provides the torque reference. In order to enhance the robustness of the controller, a disturbance observer is designed to estimate the aerodynamic torque which is considered as an unknown perturbation. Finally, a real-time simulation is carried out to illustrate the performance of the proposed controller

One Layer Nonlinear Economic Closed-Loop Generalized Predictive Control for a Wastewater Treatment Plant

The main scope of this paper is the proposal of a new single layer Nonlinear Economic Closed-Loop Generalized Predictive Control (NECLGPC) as an efficient advanced control technique for improving economics in the operation of nonlinear plants. Instead of the classic dual-mode MPC (model predictive controller) schemes, where the terminal control law defined in the terminal region is obtained offline solving a linear quadratic regulator problem, here the terminal control law in the NECLGPC is determined online by an unconstrained Nonlinear Generalized Predictive Control (NGPC). In order to make the optimization problem more tractable two considerations have been made in the present work. Firstly, the prediction model consisting of a nonlinear phenomenological model of the plant is expressed with linear structure and state dependent matrices. Secondly, instead of including the nonlinear economic cost in the objective function, an approximation of the reduced gradient of the economic function is used. These assumptions allow us to design an economic unconstrained nonlinear GPC analytically and to state the NECLGPC allow for the design of an economic problem as a QP (Quadratic Programing) problem each sampling time. Four controllers based on GPC that differ in designs and structures are compared with the proposed control technique in terms of process performance and energy costs. Particularly, the methodology is implemented in the N-Removal process of a Wastewater Treatment Plant (WWTP) and the results prove the efficiency of the method and that it can be used profitably in practical cases

One layer nonlinear economic closed-loop generalized predictive control for a wastewater treatment plant

[EN] The main scope of this paper is the proposal of a new single layer Nonlinear Economic Closed-Loop Generalized Predictive Control (NECLGPC) as an efficient advanced control technique for improving economics in the operation of nonlinear plants. Instead of the classic dual-mode MPC (model predictive controller) schemes, where the terminal control law defined in the terminal region is obtained offline solving a linear quadratic regulator problem, here the terminal control law in the NECLGPC is determined online by an unconstrained Nonlinear Generalized Predictive Control (NGPC). In order to make the optimization problem more tractable two considerations have been made in the present work. Firstly, the prediction model consisting of a nonlinear phenomenological model of the plant is expressed with linear structure and state dependent matrices. Secondly, instead of including the nonlinear economic cost in the objective function, an approximation of the reduced gradient of the economic function is used. These assumptions allow us to design an economic unconstrained nonlinear GPC analytically and to state the NECLGPC allow for the design of an economic problem as a QP (Quadratic Programing) problem each sampling time. Four controllers based on GPC that differ in designs and structures are compared with the proposed control technique in terms of process performance and energy costs. Particularly, the methodology is implemented in the N-Removal process of a Wastewater Treatment Plant (WWTP) and the results prove the efficiency of the method and that it can be used profitably in practical cases.[ES] Este trabajo propone un nuevo controlador predictivo generalizado en lazo cerrado no lineal y económico (NECLGPC), implementado en una única capa, como una técnica de control avanzado para mejorar la economía en la operación de plantas. En vez del clásico controlador predictivo en modo dual, donde la ley de control terminal definida en la región terminal se obtiene fuera de línea mediante la resolución de un problema LQR, en este trabajo la ley de control terminal se determina en línea utilizando un controlador predictivo generalizado no lineal sin restricciones (NGPC). Para resolver el problema de optimización se han realizado dos consideraciones. En primer lugar, el modelo de predicción consistente en un modelo fenomenológico no lineal de la planta se expresa mediante una estructura lineal y matrices dependientes del estado. En segundo lugar, en vez de incluir en la función objetivo un término no lineal referente a los costes económicos, se incluye una aproximación consistente en el gradiente reducido de la función económica. Estos requisitos permiten diseñar un controlador GPC no lineal sin restricciones analíticamente y plantear el problema del NECLGPC como un problema de programación cuadrática (QP) cada periodo de muestreo. Se han comparado cuatro controladores basados en GPC con el propuesto en este trabajo en términos de desempeño y costes energéticos. Como caso de estudio la metodología se ha implementado en el proceso de eliminación de nitrógeno en una planta depuradora de aguas residuales (WWTP), mostrando la eficiencia del método para casos prácticos