control design for underactuated mechanical systems is an active area of research. In this paper we focus on mechanical controlsystems defined on Lie groups with the Lagrangian equal to kinetic energy. Examples inclu...
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ISBN:
(纸本)9783952426906
control design for underactuated mechanical systems is an active area of research. In this paper we focus on mechanical controlsystems defined on Lie groups with the Lagrangian equal to kinetic energy. Examples include satellites and underwater vehicles. Under a controllability assumption, we propose two algorithms to compute small amplitude, periodic inputs that achieve arbitrary reconfigurations.
A grey box model-based method for fault diagnosis is proposed in this paper which is based on a first principle model of the process unit: a heat exchanger, and on a grey box model of the fault: the deterioration of t...
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A grey box model-based method for fault diagnosis is proposed in this paper which is based on a first principle model of the process unit: a heat exchanger, and on a grey box model of the fault: the deterioration of the heat transfer surface by aging. Under normal operating conditions the heat transfer coefficient is constant or slowly decreasing due to a layer of settled material building up on the heat transfer surface, and we propose to use a recursive least squares estimator with forgetting factor to track the heat transfer coefficients during these stages. In old heat exchangers, big chunks of the settled material can break away from the surface causing the heat transfer coefficients to rise sharply. This fault is detected via detection of abrupt positive jump in the estimated heat transfer coefficients using a Hinkley detector
The paper presents an application-oriented robust decentralized control design technique based upon the sufficient condition for robust stability (SCRS) of systems under decentralized control (DC) which is fulfilled u...
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The paper presents an application-oriented robust decentralized control design technique based upon the sufficient condition for robust stability (SCRS) of systems under decentralized control (DC) which is fulfilled using parameter tuning of fixed-structure local controllers. Graphical interpretation of the SCRS provides two useful stability criteria used to test stability of the overall system as well as stability of subsystems. Theoretical results are illustrated in a case study.
This paper presents a genetic-based approach to multi-criteria position and configuration optimisation of mobile manipulator systems. Optimisation criteria include obstacle avoidance, least joint torque norm, manipula...
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This paper presents a genetic-based approach to multi-criteria position and configuration optimisation of mobile manipulator systems. Optimisation criteria include obstacle avoidance, least joint torque norm, manipulability and joint torque distribution. Due to the competition among various criteria, the multi-criteria optimisation problem typically exhibits many local minima. The emphasis of the paper is put on using genetic algorithms to search for global optimum and solve the minilnax problem for torque distribution. Various simulations for a system including a three-link lnanipulator mounted on a mobile platform show that the proposed genetic algorithm approach performs better than conventional methods.
The paper analyses characteristics of the closed loop system with the P-, PI-, and PID-controllers and different models approximating the plant time lags. It extends some early analytical results derived by Oldenbourg...
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The paper analyses characteristics of the closed loop system with the P-, PI-, and PID-controllers and different models approximating the plant time lags. It extends some early analytical results derived by Oldenbourg and Sartorius (1951) in such a manner that they can be considered as a generalisation of the other well known classical method experimentally derived by Ziegler and Nichols (1942) and modified for discrete time systems by Takahashi at al. (1971).
作者：
Ole SørensenAalborg University
Inst. of Electronic Systems Dept. of Control Engineering Fredrik Bajersvej 7C DK-9220 Aalborg Ø Denmark
A new neural network application for non-linear state control is described. One neural network is modelled to form a Kalmann predictor and trained to act as an optimal state observer for a non-linear process. Another ...
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A new neural network application for non-linear state control is described. One neural network is modelled to form a Kalmann predictor and trained to act as an optimal state observer for a non-linear process. Another neural network is modelled to form a state controller and trained to produce a minimum variance controller for the non-linear process. After training, tuning possibilities for the observer as well as for the controller are introduced to improve the closed loop robustness and noise suppression. The advantage of this method is that tuning takes place after the time consuming training session. The method is illustrated by a simple, multi variable example. Une nouvelle application de réseaux neuraux pour un contrôle non linéaire d'état est décrite. Un predicteur de Kalmann formé à l'aide d'un réseau neural est entraîné pour agir comme un observateur d'état optimal pour un processus non linéaire. Un autre réseau neural modelisant un contrôleur d'état est développé et entraîné comme un contrôleur à variance minimale pour le processus non linéaire. Après l'entraînement, les possibilités d'ajustement et pour l'observateur et pour le contrôleur sont introduites pour ameliorer la robustesse et la suppression de bruit du système en boucle fermé. L avantage de cette méthode est que la procédure d'ajustement n'intervient qu'aprés que le long processus d'entraînement soit expiré. Un exemple à multi-variables est utilisé pour l'illustration de la méthode.
The exponential stability of large-scale nonlinear parabolic systems with time-varying delays is studied in this paper using a new generalization of Gronwalrs inequality and Lyapunov theory. The systems are interconne...
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The exponential stability of large-scale nonlinear parabolic systems with time-varying delays is studied in this paper using a new generalization of Gronwalrs inequality and Lyapunov theory. The systems are interconnected by bounded operators, although this can be generalized. All the time-delays will be assumed to have bounded time derivatives for simplicity here.
An integrated control system of active rear wheel steering (4WS) and direct yaw moment control (DYC) is presented in this paper. Because of the tire nonlinearity that is mainly due to the saturation of cornering force...
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An integrated control system of active rear wheel steering (4WS) and direct yaw moment control (DYC) is presented in this paper. Because of the tire nonlinearity that is mainly due to the saturation of cornering forces, vehicle handling performance is improved but limited to a certain extent only by steering control. Direct yaw moment control using braking and/or driving forces is effective not only in linear but also nonlinear ranges of tire friction circle. The proposed control system is a model matching controller which makes the vehicle follow the desired dynamic model by the state feedback of both yaw rate and side slip angle. Various computer simulations are carried out and show that vehicle handling performance is much improved by the integrated control system.
Model reduction has significant importance in Large-Scale systems. In this paper one of the main classes of model reduction-aggregation is used and the results of the two different methods are discused and compared fo...
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Model reduction has significant importance in Large-Scale systems. In this paper one of the main classes of model reduction-aggregation is used and the results of the two different methods are discused and compared for application in Power systems.
A practical scheme for on-line automatic tuning the existing industrial PID controllers with the assistance of a neural network is developed. A radial basis function (RBF) network have been used to implement the neura...
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A practical scheme for on-line automatic tuning the existing industrial PID controllers with the assistance of a neural network is developed. A radial basis function (RBF) network have been used to implement the neural network PID auto-tuner. A real-time version of the neural network assisted PID auto-tuner has been implemented on a personal computer (PC). Experimental testings on some generic process models, and comparison with some commercial PID auto-tuners shows favourable result on the performance of the developed scheme.
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