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In this paper, a trajectory tracking control for a nonholonomic mobile robot by the integration of a kinematic neural controller (KNC) and a torque neural controller (TNC) is proposed, where both the kinematic and dynamic models contain parametric and nonparametric uncertainties. The proposed neural controller (PNC) is constituted of the KNC and the TNC, and designed by use of a modeling technique of Gaussian radial basis function neural networks (RBFNNs). The KNC is applied to compensate the parametric uncertainties of the mobile robot kinematics. The TNC, based on the sliding mode theory, is constituted of a dynamic neural controller (DNC) and a robust neural compensator (RNC), and applied to compensate the mobile robot dynamics, significant uncertainties, bounded unknown disturbances, neural network modeling errors, influence of payload, and unknown kinematic parameters. To alleviate the problems met in practical implementation using classical sliding mode controllers and to eliminate the chattering phenomenon is used the RNC of the TNC, which is nonlinear and continuous, in lieu of the discontinuous part of the control signals present in classical forms. Also, the PNC neither requires the knowledge of the mobile robot kinematics and dynamics nor the time-consuming training process. Stability analysis and convergence of tracking errors to zero as well as the learning algorithms for weights are guaranteed with basis on Lyapunov method. Simulations results are provided to show the effectiveness of the proposed approach.

International audience; In the context of energy consumption reduction, this paper focuses on the application of Model Predictive Control to occupants' thermal comfort together with indoor air quality control while improving the whole building energy efficiency. First, an open-space office split in three zones, located in Cork Institute of Technology, is modeled. A centralized MPC is designed to control the temperature and $CO_2$ concentration in the three zones. Then, a distributed version of the MPC, with three separate local controllers, is considered. Finally, simulation results show that the distributed MPC solution achieves control performance quite close to the centralized version with less computing effort.

Made available in DSpace on 2022-04-29T08:03:54Z (GMT). No. of bitstreams: 0 Previous issue date: 2015-01-01 This paper analyses a trajectory tracking control problem for a wheeled mobile robot, using integration of a kinematic neural controller (KNC) and a torque neural controller (TNC), in which both the kinematic and dynamic models contain uncertainties and disturbances. The proposed adaptive neural controller (PANC) is composed of the KNC and the TNC and is designed with use of a modeling technique of Gaussian radial basis function neural networks (RBFNNs). The KNC is a variable structure controller, based on the sliding mode theory and is applied to compensate for the disturbances of the wheeled mobile robot kinematics. The TNC is an inertia-based controller composed of a dynamic neural controller (DNC) and a robust neural compensator (RNC) applied to compensate for the wheeled mobile robot dynamics, bounded unknown disturbances, and neural network modeling errors. To minimize the problems found in practical implementations of the classical variable structure controllers (VSC) and sliding mode controllers (SMC), and to eliminate the chattering phenomenon, the nonlinear and continuous KNC and RNC of the TNC are applied in lieu of the discontinuous components of the control signals that are present in classical forms. Additionally, the PANC neither requires the knowledge of the wheeled mobile robot kinematics and dynamics nor the timeconsuming training process. Stability analysis, convergence of the tracking errors to zero, and the learning algorithms for the weights are guaranteed based on the Lyapunov method. Simulation results are provided to demonstrate the effectiveness of the proposed approach. Universidade Estadual de Maringá Departamento de Informática, Avenida Colombo, 5790 Lyon Université INSA - Institut National des Sciences Appliquées, 20, avenue Albert Einstein Universidade Federal de Santa Catarina Departamento de Automãçao e Sistemas Programa de Pós-Graduãçao em Engenharia de Automãçao e Sistemas, Caixa Postal 476 Universidade Estadual Paulista Júlio de Mesquita Filho Faculdade de Ciências Departamento de Computãçao, Avenida Luiz Edmundo Carrijo Coube, Caixa Postal 473 Universidade Estadual Paulista Júlio de Mesquita Filho Faculdade de Ciências Departamento de Computãçao, Avenida Luiz Edmundo Carrijo Coube, Caixa Postal 473

International audience; Linear systems governed by continuous-time difference equations cover a wide class of linear systems. From the Lyapunov–Krasovskii approach, we investigate L2-stability for such a class of systems. Sufficient conditions, and in some particular cases, necessary and sufficient conditions for exponential L2-stability are established, for multivariable systems with commensurate or rationally independent delays. An analysis of discontinuities evolution appearing in the system response is proposed. Finally, a robustness issue is discussed for time-varying delays.

The main interest of the present thesis is the constrained control of time-delay system, more specifically taking into consideration the discretization problem (due to, for example, a communication network) and the presence of constraints in the system’s trajectories and control inputs. The effects of data-sampling and modeling problem are studied in detail, where an uncertainty is added into the system due to additional effect of the discretization and delay. The delay variation with respect to the sampling instants is characterized by a polytopic supra-approximation of the discretization/delay induced uncertainty. Some stabilizing techniques, based on Lyapunov’s theory, are then derived for the unconstrained case. Lyapunov-Krasovskii candidates were also used to obtain LMI conditions for a state feedback, in the ``original” state-space of the system. For the constrained control purposes, the set invariance theory is used intensively, in order to obtain a region where the system is ``well-behaviored”, despite the presence of constraints and (time-varying) delay. Due to the high complexity of the maximal delayed state admissible set obtained in the augmented state-space approach, in the present manuscript we proposed the concept of set invariance in the ``original” state-space of the system, called D-invariance. Finally, in the las part of the thesis, the MPC scheme is presented, in order to take into account the constraints and the optimality of the control solution.; Le thème principal de ce mémoire est la commande sous contraintes pour des systèmes à retard, en tenant compte de la problématique d’échantillonnage (où les informations concernant le système en temps continu sont, par exemple, envoyées par un réseau de communication) et de la présence de contraintes sur les trajectoires du système et sur l’entrée de commande. Pendant le processus d’échantillonnage, le retard variable dans le temps peut être traité comme une incertitude, le but étant de confiner cette variation dans un polytope, de façon à couvrir toutes les variations possibles du retard. Pour stabiliser des systèmes à retard, nous nous sommes basés sur la théorie de Lyapunov. En utilisant cette méthode, nous pouvons trouver un retour d’état qui stabilise le système malgré la présence du retard variable dans la boucle. Une autre possibilité est l’utilisation des candidates de Lyapunov-Krasovskii. La théorie des ensembles invariants est largement utilisée dans ce manuscrit, car il est souhaitable d’obtenir une région de ≪ sûreté ≫, ou le comportement du système est connu, en dépit de la présence du retard (variable) et des contraintes sur les trajectoires du système. Lorsqu’ils sont obtenus dans l’espace d’état augmenté, les ensembles invariants sont très complexes, car la dimension de l’espace Euclidien sera proportionnelle à la taille du système mais aussi à la taille du retard. Le concept de D-invariance est ainsi proposé. La commande prédictive (en anglais MPC) est présentée, pour tenir compte des contraintes sur les trajectoires et appliquer une commande optimale à l’entrée du système.

The main interest of the present thesis is the constrained control of time-delay system, more specifically taking into consideration the discretization problem (due to, for example, a communication network) and the presence of constraints in the system’s trajectories and control inputs. The effects of data-sampling and modeling problem are studied in detail, where an uncertainty is added into the system due to additional effect of the discretization and delay. The delay variation with respect to the sampling instants is characterized by a polytopic supra-approximation of the discretization/delay induced uncertainty. Some stabilizing techniques, based on Lyapunov’s theory, are then derived for the unconstrained case. Lyapunov-Krasovskii candidates were also used to obtain LMI conditions for a state feedback, in the ``original” state-space of the system. For the constrained control purposes, the set invariance theory is used intensively, in order to obtain a region where the system is ``well-behaviored”, despite the presence of constraints and (time-varying) delay. Due to the high complexity of the maximal delayed state admissible set obtained in the augmented state-space approach, in the present manuscript we proposed the concept of set invariance in the ``original” state-space of the system, called D-invariance. Finally, in the las part of the thesis, the MPC scheme is presented, in order to take into account the constraints and the optimality of the control solution.; Le thème principal de ce mémoire est la commande sous contraintes pour des systèmes à retard, en tenant compte de la problématique d’échantillonnage (où les informations concernant le système en temps continu sont, par exemple, envoyées par un réseau de communication) et de la présence de contraintes sur les trajectoires du système et sur l’entrée de commande. Pendant le processus d’échantillonnage, le retard variable dans le temps peut être traité comme une incertitude, le but étant de confiner cette variation dans un polytope, de façon à couvrir toutes les variations possibles du retard. Pour stabiliser des systèmes à retard, nous nous sommes basés sur la théorie de Lyapunov. En utilisant cette méthode, nous pouvons trouver un retour d’état qui stabilise le système malgré la présence du retard variable dans la boucle. Une autre possibilité est l’utilisation des candidates de Lyapunov-Krasovskii. La théorie des ensembles invariants est largement utilisée dans ce manuscrit, car il est souhaitable d’obtenir une région de ≪ sûreté ≫, ou le comportement du système est connu, en dépit de la présence du retard (variable) et des contraintes sur les trajectoires du système. Lorsqu’ils sont obtenus dans l’espace d’état augmenté, les ensembles invariants sont très complexes, car la dimension de l’espace Euclidien sera proportionnelle à la taille du système mais aussi à la taille du retard. Le concept de D-invariance est ainsi proposé. La commande prédictive (en anglais MPC) est présentée, pour tenir compte des contraintes sur les trajectoires et appliquer une commande optimale à l’entrée du système.

In this paper, a trajectory tracking control for a nonholonomic mobile robot subjected to uncertainties and disturbances in the kinematic model is proposed. An adaptive variable structure controller based on the sliding mode theory is used, and applied to compensate these uncertainties and disturbances. To minimize the problems found in practical implementation using classical variable structure controllers, and eliminate the chattering phenomenon as well as compensate disturbances a neural compensator is used, which is nonlinear and continuous, in lieu of the discontinuous portion of the control signals present in classical forms. The proposed neural compensator is designed by a modeling technique of Gaussian radial basis function neural networks and does not require the time-consuming training process. Stability analysis is guaranteed with basis on the Lyapunov method. Simulation results are provided to show the effectiveness of the proposed approach., Facultad de Informática

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Elétrica. Este trabalho trata da síntese de controladores para sistemas de visão 2D, multicritérios, por posicionamento, para um alvo móvel ou fixo, de uma câmera orientada num plano horizontal, montada sobre um robô móvel não-holonômico. A qualidade das informações fornecidas pelas câmera atuais possibilita a alimentação direta das malhas de controle com os dados visuais do ambiente. O modelo utilizado é o mesmo definido pelo formalismo de funções-tarefa, e utilizam a noção de torsor de iteração, permitindo assim ligar o movimento da câmera à variação dos índices visuais. O objetivo é propor leis de controle que permitam estabilizar a câmera através das informações bidimensionais vindas da imagem, levando em consideração as incertezas com relação à profundidade dos pontos do alvo, das restrições de visibilidade, e das limitações em amplitude das velocidades e acelerações da câmera. O método consiste em satisfazer uma condição de setor modificada, de tal forma que seja levada em conta a saturação da aceleração em malha fechada via um modelo politópico de incertezas. Desta forma são geradas condições construtivas sob a forma de Desigualdades Lineares Matriciais (LMIs). Deste modo, a resolução de problemas de otimização convexa permite maximizar a região de estabilidade associada, e determinar o ganho de realimentação de estados estabilizante. A técnica, primeiramente aplicada a um alvo fixo, é em seguida aplicada a um alvo móvel, considerando a velocidade desconhecida do alvo como uma perturbação limitada em energia. A seguir são apresentados vários resultados de simulação.