In this contribution several approaches to nonlinear adaptive control of mechanical drives are proposed taking into account determined nonlinear actuator characteristics such as friction, hysteresis and other static n...
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In this contribution several approaches to nonlinear adaptive control of mechanical drives are proposed taking into account determined nonlinear actuator characteristics such as friction, hysteresis and other static nonlinearities. Improved control performance in terms of dynamics, high positioning accuracy and robustness is achieved by combining linear position control with nonlinear feed-forward and adaptive friction compensation methods. Based on a precise model of the drive dynamics recursive identification techniques are applied to obtain reliable estimates of the time varying and position dependent process parameters, the nonlinear friction characteristic in particular. As examples the nonlinear friction characteristics of robotic joint drives and pneumatic drives are determined and it is shown how the control can be improved considerably by model based nonlinear control in the presence of time varying nonlinear characteristics and hysteresis effects. Experimental results illustrate the efficiency of the developed nonlinear control schemes.
Many systems with nonlinear behaviour are difficult to model. Consequently model based approaches to fault detection can not be applied easily. Additional problems are introduced in closed loops by the controversity o...
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Many systems with nonlinear behaviour are difficult to model. Consequently model based approaches to fault detection can not be applied easily. Additional problems are introduced in closed loops by the controversity of controller design and fault detection. For a certain class of processes with mainly constant input signals a combination of parameter estimation techniques and the parity space approach allows on-line fault detection with small signal models. Only few knowledge of the process is required: An idea about the dynamic model order and the static nonlinear characteristic curve. Using the proposed method fault detection is performed for temperature control loops in a thermic plant.
A methodology for designing an automated vehicle longitudinal velocity and distance controller is presented and applied to an automobile. Typically a vehicle is described by velocity depended dynamics and specific non...
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A methodology for designing an automated vehicle longitudinal velocity and distance controller is presented and applied to an automobile. Typically a vehicle is described by velocity depended dynamics and specific nonlinearities. Therefore the controller consists of a three layer structure. In the first layer a linearization of the nonlinearities is done in order to achieve a simplified structure for controller design purposes. With respect to changes in the typical vehicle parameters-mass and aerodynamical drag - an adaptive controller structure is used and these parameters are estimated by a recursive least squares algorithm. Based on classical controlling techniques a linear acceleration controller is developed in the middle layer. Up to now no mathematical equations are available describing the subjective demands of the driver and passengers during longitudinal motion. Therefore a fuzzy controller is applied in the upper layer. This controller based on the linguistic description of comfort demands. The complete structure is used in two different series fabricated vehicles and experimental results are shown for highway traffic as well as for stop-go traffic on highway congestions.
Predictive control algorithms determine a series of the control signal minimizing the deviation between the reference and the output signal in a given future horizon. The output of the plant to be controlled is predic...
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Predictive control algorithms determine a series of the control signal minimizing the deviation between the reference and the output signal in a given future horizon. The output of the plant to be controlled is predicted on the basis of a model, which can be linear or nonlinear, parametric or nonparametric. In adaptive control these process parameters are identified and the control signal is calculated taking the identified values into consideration. In the paper simulation results present some properties of adaptive predictive control. Robustness of the control algorithm is illustrated through control of a simple Wiener model . The relationship between the plant order and the prediction horizon is also mentioned. The promising results indicate that further systematic analysis is worthwhile.
An approach for supervision of vehicle dynamics is presented which may be used for intelligent vehicle control and state monitoring. In particular, the task is to determine automatically an altered road surface for ve...
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An approach for supervision of vehicle dynamics is presented which may be used for intelligent vehicle control and state monitoring. In particular, the task is to determine automatically an altered road surface for vehicles (may be due to weather conditions) with possibly lower adhesion between tires and road. Fault detection methods like the parity space approach can be used to detect changes in technical processes. These changes indicate different process behaviour in a new operation range. This may be of special importance when the new behaviour is more dangerous for the driver and the passengers of a car. In a further step, suitable classification algorithms were used to detect the actual driving state by processing the residual output.
A methodology for designing an automated vehicle longitudinal velocity and distance controller is presented and applied to an automobile. nonlinearities. The controller consists of a three layer structure. In the firs...
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A methodology for designing an automated vehicle longitudinal velocity and distance controller is presented and applied to an automobile. nonlinearities. The controller consists of a three layer structure. In the first layer a linearization of the nonlinearities is done in order to achieve a simplified structure for controller design purposes. With respect to changes in the typical vehicle parameters-mass and aerodynamical drag-an adaptive controller structure is used and these parameters are estimated by a recursive least squares algorithm. Based on classical controlling techniques a linear acceleration controller is developed in the middle layer. A fuzzy controller is applied in the upper layer. This controller based on the linguistic description of comfort demands. Additionally a neural network is used in this layer instead of the fuzzy system. The complete structure is used in two different series fabricated vehicles and experimental results are shown for highway traffic and also for stop-go traffic on highway congestions. Additionally a neural network is trained by measurement data as a second approach to describe the comfort demands.
This paper describes a new method of fault detection in milling. The presented detection algorithm is based on the estimation of particular model parameters using measured cutting force signals. By classifying the res...
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This paper describes a new method of fault detection in milling. The presented detection algorithm is based on the estimation of particular model parameters using measured cutting force signals. By classifying the resulting patterns of the estimated parameters, the conditions of the cutting teeth can be determined.
作者:
ISERMANN, RInstitute of Automatic Control
Laboratory for Control Engineering and Process Automation Technical University of Darmstadt Landgraf-Georg-Straβe 4 D-64283 Darmstadt Germany
Beginning with classical supervision a summary is given of the main principles of model-based fault detection methods. Their basic assumptions, results from simulation and experiments allow a discussion of the feature...
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Beginning with classical supervision a summary is given of the main principles of model-based fault detection methods. Their basic assumptions, results from simulation and experiments allow a discussion of the features of the methods with regard to their applicability. The importance of realistic fault modeling is outlined. Finally the role of model-based fault detection as one information source for fault diagnosis is discussed. Several methods of fault detection have to be integrated to support a powerful diagnosis.
Selftuning of classical PI-controllers for processes without known mathematical models is achieved by applying heuristic fuzzy rules. Step changes of the reference input are used to assess control performance in terms...
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Selftuning of classical PI-controllers for processes without known mathematical models is achieved by applying heuristic fuzzy rules. Step changes of the reference input are used to assess control performance in terms of overshoot, rise time and settling time of the step response. Similar to the strategy of an experienced plant operator, a fuzzy logic ''supervision and tuning module'' can conclude from these features, which changes of the controller parameters will improve control behaviour. The concept was tested in simulation series with Matlab/Simulink. Results show the benefits of the proposed fuzzy tuning module applied to 8 different simulated test plants. Experiments with speed-regulation of a DC-servomotor indicate that the concept is suitable for processes with noisy signals as well.
Analytical formulas for the transfer characteristic of several SISO fuzzy controllers are developed using a computer algebra system. Different formulations of the linguistic output variables and different inference me...
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Analytical formulas for the transfer characteristic of several SISO fuzzy controllers are developed using a computer algebra system. Different formulations of the linguistic output variables and different inference methods can be compared with respect to their impact on control characteristics. The offline-defuzzyfication strategy resulting in singletons as output attributes already frequently used in practice is theoretically supported. The example of a fuzzy PI controller with two input variables show the application of the proposed method to the systematic design of fuzzy controllers.< >
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