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Projeto de sistemas de controle multivariáveis robustos com especificações no domínio do tempo.; Robust multivariable control systems design with time domain specifications.

Leonardi, Fabrizio
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 29/11/2002 PT
Relevância na Pesquisa
45.83%
Este trabalho discute o projeto de compensadores multivariáveis robustos com especificações no domínio do tempo. Primeiramente faz-se a análise dos compensadores por observadores de estados como forma de atingir tais objetivos. Mostra-se que, em certas condições, essa estrutura equivale à dos observadores proporcionais-integrais e apresentam-se as condições de estabilidade nominal. Evidencia-se também que é possível tratar esse problema de controle como um problema de "model matching" ou como um problema de controle com dois graus de liberdade. Mostra-se também que o projeto do compensador é equivalente ao projeto de sistemas de controle por realimentação estática da saída. Essa equivalência implica que, embora os compensadores por observadores sejam cômodos à incorporação de especificações temporais, sua estrutura é limitada para garantir que especificações gerais sejam satisfeitas. Contorna-se então essa limitação estendendo-se o estudo ao caso dos compensadores sem essa restrição estrutural. O problema de "model matching" e o problema de controle 2﷓D são considerados como forma indireta de incorporar-se as especificações temporais e condições de projeto são obtidas reduzindo-se os possíveis conservadorismos dos projetos usuais. Ainda neste sentido...

Adaptive and non-adaptive model predictive control of an irrigation channel

Lemos, João Miranda; Machado, Fernando; Nogueira, Nuno; Rato, Luís; Rijo, Manuel
Fonte: American Institute of Mathematical Sciences Publicador: American Institute of Mathematical Sciences
Tipo: Artigo de Revista Científica Formato: 715235 bytes; application/pdf
ENG
Relevância na Pesquisa
55.68%
The performance achieved with both adaptive and non-adaptive Model Predictive Control (MPC) when applied to a pilot irrigation channel is evaluated. Several control structures are considered, corresponding to various degrees of centralization of sensor information, ranging from local upstream control of the di®erent channel pools to multivariable control using only prox- imal pools, and centralized multivariable control relying on a global channel model. In addition to the non-adaptive version, an adaptive MPC algorithm based on redundantly estimated multiple models is considered and tested with and without feedforward of adjacent pool levels, both for upstream and down- stream control. In order to establish a baseline, the results of upstream and local PID controllers are included for comparison. A systematic simulation study of the performances of these controllers, both for disturbance rejection and reference tracking is shown.

Adaptative and Non-adaptative Model Predictive Control of an Irrigation Channel

Lemos, João; Rato, Luís; Rijo, Manuel
Fonte: Universidade de Évora Publicador: Universidade de Évora
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
55.67%
The performance achieved with both adaptive and non-adaptative Model Predictive Control (MPC) when applied to a pilot irrigation channel is evaluated. Several control structures are considered, corresponding to various degrees of centralization of sensor information, ranging from local upstream control of the different channel pools to multivariable control using only proximal pools, and centralized multivariable control relying on a global channel model. In addition to the non-adaptative version, an adaptive MPC algorithm based on redundantly estimated multiple models is considered and tested with and without feedforward of adjacent pool levels, both for upstream and downstream control. In order to establish a baseline, the results of upstream and local PID controllers are included for comparison. A systematic simulation study of the performances of these controllers, both for disturbance rejection and reference tracking is shown.

Estabilidade e robustez de um controlador adaptativo indireto por um modelo de referência e estrutura variável; Robustness and stability analysis of an indirect variable structure model reference adaptive controller

Oliveira, Josenalde Barbosa de
Fonte: Universidade Federal do Rio Grande do Norte; BR; UFRN; Programa de Pós-Graduação em Engenharia Elétrica; Automação e Sistemas; Engenharia de Computação; Telecomunicações Publicador: Universidade Federal do Rio Grande do Norte; BR; UFRN; Programa de Pós-Graduação em Engenharia Elétrica; Automação e Sistemas; Engenharia de Computação; Telecomunicações
Tipo: Tese de Doutorado Formato: application/pdf
POR
Relevância na Pesquisa
55.67%
In this thesis, it is developed the robustness and stability analysis of a variable structure model reference adaptive controller considering the presence of disturbances and unmodeled dynamics. The controller is applied to uncertain, monovariable, linear time-invariant plants with relative degree one, and its development is based on the indirect adaptive control. In the direct approach, well known in the literature, the switching laws are designed for the controller parameters. In the indirect one, they are designed for the plant parameters and, thus, the selection of the relays upper bounds becomes more intuitive, whereas they are related to physical parameters, which present uncertainties that can be known easier, such as resistances, capacitances, inertia moments and friction coefficients. Two versions for the controller algorithm with the stability analysis are presented. The global asymptotic stability with respect to a compact set is guaranteed for both cases. Simulation results under adverse operation conditions in order to verify the theoretical results and to show the performance and robustness of the proposed controller are showed. Moreover, for practical purposes, some simplifications on the original algorithm are developed; Nesta tese é desenvolvida a análise de estabilidade e robustez à dinâmica não modelada e às perturbações externas de um controlador adaptativo por modelo de referência e estrutura variável aplicado a plantas incertas...

Uma contribuição ao estudo de controladores robustos; A contribution for studing of robust control

Silva, Cláudio Homero Ferreira da
Fonte: Universidade Federal de Uberlândia Publicador: Universidade Federal de Uberlândia
Tipo: Tese de Doutorado
POR
Relevância na Pesquisa
45.79%
O controlador preditivo baseado em modelo (MPC) tem sido aplicado com sucesso na indústria, com especial destaque na indústria petroquímica. A característica básica dos algoritmos preditivos baseados em modelos é a formulação de um problema de otimização, para o cálculo da seqüência de ações de controle que minimizem uma função de desempenho num horizonte temporal com a melhor informação disponível a cada instante, e submetida a restrições de operação e ao modelo da planta. A presença de incertezas no modelo pode resultar em perda de desempenho da planta controlada baseada em controladores com modelo nominal ou até mesmo na instabilidade da malha fechada. A classe de controladores preditivos robustos (RMPC) baseia-se na consideração explícita das incertezas. Este trabalho apresenta dois algoritmos de RMPC. Um algoritmo que adiciona ao problema de controle robusto, com incerteza invariante no tempo na forma de otimização mín-máx, um conjunto de restrições com garantia de estabilidade robusta (RMPC-MMR). E outro algoritmo que desenvolve o RMPC usando desigualdades de matrizes lineares (LMI) e incerteza na forma de politopo (LMI-RMPCR). Os algoritmos são avaliados, através de simulações computacionais...

Delay-dependent adaptive reconfiguration control in the presence of input saturation and actuator faults

Guo, Y.; Jiang, B.; Shi, P.
Fonte: ICIC International Publicador: ICIC International
Tipo: Artigo de Revista Científica
Publicado em //2010 EN
Relevância na Pesquisa
55.66%
In this paper, an active fault tolerant control strategy is developed for a class of linear state-delayed systems with unknown actuator faults and input constraints. The design is a combination between a direct adaptive control algorithm and multiple model switching, and the μ-modification is introduced in the model reference control architecture. The main features of the proposed control strategy are the reliability and simplicity in tracking against actuator faults. By Lyapunov-Krasovskii theory, the stability of overall system is guaranteed and the boundness of all signals is ensured. Numerical simulation results demonstrate the effectiveness of the proposed fault-tolerant control scheme.; Yuying Guo, Bin Jiang, and Peng Shi

Back-propagation neural networks in adaptive control of unknown nonlinear systems

Cakarcan, Alpay
Fonte: Monterey, California. Naval Postgraduate School Publicador: Monterey, California. Naval Postgraduate School
Tipo: Tese de Doutorado
EN_US
Relevância na Pesquisa
55.61%
The objective of this thesis research is to develop a Back-Propagation Neural Network (BNN) to control certain classes of unknown nonlinear systems and explore the network's capabilities. The structure of the Direct Model Reference Adaptive Controller (DMRAC) for Linear Time Invariant (LTI) systems with unknown parameters is first analyzed and then is extended to nonlinear systems by using BNN, Nonminimum phase systems, both linear and nonlinear, have also be considered. The analysis of the experiments shows that the BNN DMRAC gives satisfactory results for the representative nonlinear systems considered, while the conventional least-squares estimator DMRAC fails. Based on the analysis and experimental findings, some general conditions are shown to be required to ensure that this technique is satisfactory. These conditions are presented and discussed. It has been found that further research needs to be done for the nonminimum phase case in order to guarantee stability and tracking. Also, to establish this as a more general and significant control technique, further research is required to develop more specific rules and guidelines for the BNN design and training.; Lieutenant Junior Grade, Turkish Navy

Dribbling Control of an Omnidirectional Soccer Robot; Dribbling Regelung eines Omnidirektionalen Roboters

Li, Xiang
Fonte: Universität Tübingen Publicador: Universität Tübingen
Tipo: Dissertation; info:eu-repo/semantics/doctoralThesis
EN
Relevância na Pesquisa
55.71%
This thesis is concerned with motion control of omnidirectional robots. From developing a robot control system to designing different controllers, this thesis focuses on achieving high control performance with consideration of important issues, such as actuator dynamics, actuator saturation and constraints of robot systems. As a testbed, the motion control of an omnidirectional robot of the Tübingen Attempto robot soccer team, especially the ball dribbling control of the soccer robot, has been considered in this thesis. Before designing motion control methods, a control system combining dynamics and kinematics is adopted for the Attempto soccer robot. This architecture allows to design and test low-level controllers according to the dynamic model and high-level controllers based on the kinematic model separately. Taking actuator saturation and actuator dynamics into account, the proposed control system builds a foundation to design high-level controllers with consideration of the low-level system's performance. Based on the robot control system, path following and orientation tracking problems of omnidirectional robots are addressed in this thesis. Since these two problems are all formulated in the form of error kinematics...

Integrated real-time optimization and model predictive control under parametric uncertainties

Adetola, Veronica A.
Fonte: Quens University Publicador: Quens University
Tipo: Tese de Doutorado Formato: 1881071 bytes; application/pdf
EN; EN
Relevância na Pesquisa
65.68%
The actualization of real-time economically optimal process operation requires proper integration of real-time optimization (RTO) and dynamic control. This dissertation addresses the integration problem and provides a formal design technique that properly integrates RTO and model predictive control (MPC) under parametric uncertainties. The task is posed as an adaptive extremum-seeking control (ESC) problem in which the controller is required to steer the system to an unknown setpoint that optimizes a user-specified objective function. The integration task is first solved for linear uncertain systems. Then a method of determining appropriate excitation conditions for nonlinear systems with uncertain reference setpoint is provided. Since the identification of the true cost surface is paramount to the success of the integration scheme, novel parameter estimation techniques with better convergence properties are developed. The estimation routine allows exact reconstruction of the system's unknown parameters in finite-time. The applicability of the identifier to improve upon the performance of existing adaptive controllers is demonstrated. Adaptive nonlinear model predictive controllers are developed for a class of constrained uncertain nonlinear systems. Rather than relying on the inherent robustness of nominal MPC...

Multiple model adaptive control with safe switching

Anderson, Brian; Brinsmead, Thomas; Liberzon, Daniel; Morse, A
Fonte: John Wiley & Sons Inc Publicador: John Wiley & Sons Inc
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
75.68%
The purpose of this paper is to marry the two concepts of multiple model adaptive control and safe adaptive control. In its simplest form, multiple model adaptive control involves a supervisor switching among one of a finite number of controllers as more

High-Speed Finite Control Set Model Predictive Control for Power Electronics

Stellato, Bartolomeo; Goulart, Paul J.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 19/10/2015
Relevância na Pesquisa
55.62%
Common approaches for direct model predictive control (MPC) for current reference tracking in power electronics suffer from the high computational complexity encountered when solving integer optimal control problems over long prediction horizons. We propose an efficient alternative method based on approximate dynamic programming, greatly reducing the computational burden and enabling sampling times under 25 $\mu$s. Our approach is based on the offline minimization of an infinite horizon cost function estimate which is then applied to the tail cost of the MPC problem. This allows us to reduce the controller horizon to a very small number of stages improving overall controller performance. Our proposed algorithm is validated on a variable speed drive system with a three-level voltage source converter.

A model-free control strategy for an experimental greenhouse with an application to fault accommodation

Lafont, Frédéric; Balmat, Jean-François; Pessel, Nathalie; Fliess, Michel
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 26/11/2014
Relevância na Pesquisa
55.63%
Writing down mathematical models of agricultural greenhouses and regulating them via advanced controllers are challenging tasks since strong perturbations, like meteorological variations, have to be taken into account. This is why we are developing here a new model-free control approach and the corresponding intelligent controllers, where the need of a good model disappears. This setting, which has been introduced quite recently and is easy to implement, is already successful in many engineering domains. Tests on a concrete greenhouse and comparisons with Boolean controllers are reported. They not only demonstrate an excellent climate control, where the reference may be modified in a straightforward way, but also an efficient fault accommodation with respect to the actuators.

Nonlinear Model Predictive Control of A Gasoline HCCI Engine Using Extreme Learning Machines

Janakiraman, Vijay Manikandan; Nguyen, XuanLong; Assanis, Dennis
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 16/01/2015
Relevância na Pesquisa
55.68%
Homogeneous charge compression ignition (HCCI) is a futuristic combustion technology that operates with a high fuel efficiency and reduced emissions. HCCI combustion is characterized by complex nonlinear dynamics which necessitates a model based control approach for automotive application. HCCI engine control is a nonlinear, multi-input multi-output problem with state and actuator constraints which makes controller design a challenging task. Typical HCCI controllers make use of a first principles based model which involves a long development time and cost associated with expert labor and calibration. In this paper, an alternative approach based on machine learning is presented using extreme learning machines (ELM) and nonlinear model predictive control (MPC). A recurrent ELM is used to learn the nonlinear dynamics of HCCI engine using experimental data and is shown to accurately predict the engine behavior several steps ahead in time, suitable for predictive control. Using the ELM engine models, an MPC based control algorithm with a simplified quadratic program update is derived for real time implementation. The working and effectiveness of the MPC approach has been analyzed on a nonlinear HCCI engine model for tracking multiple reference quantities along with constraints defined by HCCI states...

A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley

Doan, Minh Dang; Giselsson, Pontus; Keviczky, Tamás; De Schutter, Bart; Rantzer, Anders
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 08/02/2013
Relevância na Pesquisa
55.64%
A distributed model predictive control (DMPC) approach based on distributed optimization is applied to the power reference tracking problem of a hydro power valley (HPV) system. The applied optimization algorithm is based on accelerated gradient methods and achieves a convergence rate of O(1/k^2), where k is the iteration number. Major challenges in the control of the HPV include a nonlinear and large-scale model, nonsmoothness in the power-production functions, and a globally coupled cost function that prevents distributed schemes to be applied directly. We propose a linearization and approximation approach that accommodates the proposed the DMPC framework and provides very similar performance compared to a centralized solution in simulations. The provided numerical studies also suggest that for the sparsely interconnected system at hand, the distributed algorithm we propose is faster than a centralized state-of-the-art solver such as CPLEX.

Model predictive control of voltage profiles in MV networks with distributed generation

Farina, Marcello; Guagliardi, Antonio; Mariani, Federico; Sandroni, Carlo; Scattolini, Riccardo
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 14/11/2013
Relevância na Pesquisa
55.67%
The Model Predictive Control (MPC) approach is used in this paper to control the voltage profiles in MV networks with distributed generation. The proposed algorithm lies at the intermediate level of a three-layer hierarchical structure. At the upper level a static Optimal Power Flow (OPF) manager computes the required voltage profiles to be transmitted to the MPC level, while at the lower level local Automatic Voltage Regulators (AVR), one for each Distributed Generator (DG), track the reactive power reference values computed by MPC. The control algorithm is based on an impulse response model of the system, easily obtained by means of a detailed simulator of the network, and allows to cope with constraints on the voltage profiles and/or on the reactive power flows along the network. If these constraints cannot be satisfied by acting on the available DGs, the algorithm acts on the On-Load Tap Changing (OLTC) transformer. A radial rural network with two feeders, eight DGs, and thirty-one loads is used as case study. The model of the network is implemented in DIgSILENT PowerFactory, while the control algorithm runs in Matlab. A number of simulation results is reported to witness the main characteristics and limitations of the proposed approach.

Application of Modified Multi Model Predictive Control Algorithm to Fluid Catalytic Cracking Unit

Rehman, Nafay Hifzur; Verma, Neelam
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 12/05/2014
Relevância na Pesquisa
55.67%
This paper presents a modified multi model predictive control algorithm for the control of riser outlet temperature and regenerator temperature for the fluid catalytic cracking unit (FCCU). The models of the fluid catalytic cracking unit are estimated using subspace identification (N4SID) algorithm. The PRBS signal is applied as an input signal to estimate the FCCU models. Since the estimated model does not give 100% fit; especially for nonlinear systems having more than one operating conditions, multi-model approach is proposed. In multi model, more than one model of FCCU used in MPC design. The main advantages of proposed method are that it can handle hard input and output constraints and it can be used for multi input multi output processes (MIMO) without increasing the complexity in control design. MATLAB/Simulink is used to estimate the models of FCCU and simulate the results for the controller. The simulation results show that the proposed algorithm provides better result for both reference tracking and disturbance rejection.; Comment: 7 pages, 12 figures

Stable Hierarchical Model Predictive Control Using an Inner Loop Reference Model and Lambda-Contractive Terminal Constraint Sets

Vermillion, Chris; Menezes, Amor; Kolmanovsky, Ilya
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
55.8%
This paper proposes a novel hierarchical model predictive control (MPC) strategy that guarantees overall system stability. This method differs significantly from previous approaches to guaranteeing overall stability, which have relied upon a multi-rate framework where the inner loop (low level) is updated at a faster rate than the outer loop (high level), and the inner loop must reach a steady-state within each outer loop time step. In contrast, the method proposed in this paper is aimed at stabilizing the origin of an error system characterized by the difference between the inner loop state and the state specified by a full-order reference model. This makes the method applicable to systems with reduced levels of time scale separation. This paper proposes a framework for guaranteeing stability that leverages the use of the reference model, in conjunction with lambda-contractive constraint sets for both the inner and outer loops. The effectiveness of the proposed reference model-based strategy is shown through simulation on an existing stirred tank reactor problem, where we demonstrate that the MPC optimization problem remains feasible and that the system remains stable and continues to perform well when time scale separation between the inner and outer loops is reduced.; Comment: This is a self-contained technical report that contains both the mathematical formulation and proofs for the MPC strategy disclosed in our Automatica publication

Model Reference Adaptive Control of Systems with Gain Scheduled Reference Models

Pakmehr, Mehrdad; Yucelen, Tansel
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 14/03/2014
Relevância na Pesquisa
55.89%
Firstly, a new state feedback model reference adaptive control approach is developed for uncertain systems with gain scheduled reference models in a multi-input multi-output (MIMO) setting. Specifically, adaptive state feedback for output tracking control problem of MIMO nonlinear systems is studied and gain scheduled reference model system is used for generating desired state trajectories. Using convex optimization tools, a common Lyapunov matrix is computed for multiple linearizations near equilibrium and non-equilibrium points of the nonlinear closed loop gain scheduled reference system. This approach guarantees stability of the closed-loop gain scheduled system. Adaptive state feedback control scheme is then developed, and its stability is proven. The resulting closed-loop system is shown to have bounded solutions with bounded tracking error, with the proposed stable gain scheduled reference model. Secondly, the developed control approach is improved for systems with constraints on the control inputs. The resulting closed-loop system is shown to have bounded solutions with bounded tracking error. Sufficient conditions for ultimate boundedness of the closed-loop system are derived. A semi-global stability result is proved with respect to the level of saturation for open-loop unstable plants while the stability result is shown to be global for open-loop stable plants. Thirdly...

Model reference adaptive control for mobile robots in trajectory tracking using radial basis function neural networks

Rossomando,F. G.; Soria,C.; Patiño,D.; Carelli,R.
Fonte: Latin American applied research Publicador: Latin American applied research
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/04/2011 EN
Relevância na Pesquisa
55.76%
This paper propose an Model Reference Adaptive Control (MRAC) for mobile robots for which stability conditions and performance evaluation are given. The proposed control structure combines a feedback linearization model, based on a kinematics nominal model, and a direct neural network-based adaptive dynamics control. The architecture of the dynamic control is based on radial basis functions neural networks (RBF-NN) to construct the MRAC controller. The parameters of the adaptive dynamic controller are adjusted according to a law derived using Lyapunov stability theory and the centers of the RBF are adapted using the supervised algorithm. The resulting MRAC controller is efficient and robust in the sense that it succeeds to achieve a good tracking performance with a small computational effort. Stability result for the adaptive neuro-control system is given. It is proved that control errors are ultimately bounded as a function of the approximation error of the RBF-NN. Experimental results showing the practical feasibility and performance of the proposed approach to mobile robotics are given.

Experimental application of a neural constrained model predictive controller based on reference system

Montandon,A. G.; Borges,R. M.; Henrique,H. M.
Fonte: Latin American applied research Publicador: Latin American applied research
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/01/2008 EN
Relevância na Pesquisa
55.75%
The proposed constrained model predictive control (MPC) is based on a successive linearization of a neural model at each sampling time and the closed loop response is subject to a first order reference system as set of equality constraints. In addition the system inputs are subject to hard constraints. In order to satisfy both types of constraints simultaneously it was needed to include a slack vector in the equality constraints. This slack vector provides more flexibility in the control moves in order to render the solution of the optimization problem feasible. The proposed MPC was implemented in an experimental pH neutralization plant. Results showed a very satisfactory performance of the proposed strategy.