Providing a simple and effective way to describe the nonlinear input-output behaviour of a system, three-dimensional mappings (3-D maps) have gained a lot of importance in modern automotive technology. Applications co...
详细信息
Providing a simple and effective way to describe the nonlinear input-output behaviour of a system, three-dimensional mappings (3-D maps) have gained a lot of importance in modern automotive technology. Applications cover a wide range from real-time controlsystems up to the area of vehicle simulation. Replacing the conventional look-up-tables by neural network or fuzzy logic representations offers an easy possibility to generate 3-D maps by measured data and to adapt them online using measured signals. This paper describes the modelling of engine characteristics for vehicle control and simulation purposes by multilayer perceptron and radial-basis function networks. In addition to that, a neuro-fuzzy approach is discussed as well.
This paper deals with identification and control of a highly nonlinear real world application, The performance and applicability of the proposed methods are demonstrated for an industrial heat exchanger. The main diff...
详细信息
This paper deals with identification and control of a highly nonlinear real world application, The performance and applicability of the proposed methods are demonstrated for an industrial heat exchanger. The main difficulties for identification and control of this plant arise from the strongly nonlinear center and the widely varying dead times introduced by different water flows. The identification of this three input one output process is based on the local linear model trees (LOLIMOT) algorithm. It combines efficient local linear least-squares techniques for parameter estimation of the local linear models with a tree construction algorithm that determines the structure of their validity functions. Furthermore, a subset selection technique based on the orthogonal least-squares (OLS) algorithm is applied for an automatic determination of the model orders and dead times. This strategy allows to design a wide range high accuracy nonlinear dynamic model of the heat exchanger on which the predictive control approach is based on. The nonlinear predictive control takes the speed and limit constraints of the actuator into account and leads to a high performance control over all ranges of operation.
The synergetic integration of mechanical processes, micro-electronics and information processing opens new possibilities as well to the design of processes as for its automaticcontrol. The solution of tasks within me...
详细信息
The synergetic integration of mechanical processes, micro-electronics and information processing opens new possibilities as well to the design of processes as for its automaticcontrol. The solution of tasks within mechatronic systems is performed on the process side and the digital-electronic side. As the interrelations during the design play an import role simultaneous engineering from the very beginning has to take place. Mechatronic systems are developed for mechanical elements, machines, vehicles and precision mechanic devices. The integration of mechatronic systems can be performed by the components (hardware-integration) and by information processing (software-integration). The information processing consists of low-level and high-level feedback control, supervision and diagnosis and general process management. Special signal processing, model based and adaptive methods are applied. With the aid of a knowledge base and inference mechanisms, mechatronic systems with increasing intelligence will be developed. The main goals are to increase systems performance, reliability and economy, and production costs. Some examples for the development of mechatronic systems are given, such as smart actuators, adaptive suspensions, electrical brakes, adaptive cruising control of cars, and hardware-in-the-loop simulation of combustion engines. Experimental results are shown, and the improvements by the mechatronic approaches are pointed out.
Heat exchangers play an important role in chemical and process industries. In order to improve reliability and control performance, intelligent concepts for control, supervision and reconfiguration are necessary. In t...
详细信息
Heat exchangers play an important role in chemical and process industries. In order to improve reliability and control performance, intelligent concepts for control, supervision and reconfiguration are necessary. In this paper, an approach is presented which integrates model-based adaptive control and reconfiguration based on fault detection/diagnosis applied to a heat exchanger plant. The adaptive controller and the fault detection scheme are based on a fuzzy model of the process (Takagi-Sugeno type) and the fault diagnosis is performed using a self-organizing fuzzy structure.
Deals with identification of nonlinear processes and model-based fault detection/isolation (FDI). The applicability of the proposed methods is illustrated on a three-tank laboratory setup. The process identification i...
详细信息
Deals with identification of nonlinear processes and model-based fault detection/isolation (FDI). The applicability of the proposed methods is illustrated on a three-tank laboratory setup. The process identification is based on the local linear model tree (LOLIMOT) algorithm and leads to local linear models. The parameters of the local models are used for generation of structured residual equations, similar to the well-known parity space approach. This enables detection and isolation of five different sensor faults of the three-tank process, continously over all ranges of operation.
This paper deals with nonlinear predictive control based on higher order Takagi-Sugeno fuzzy systems which can also be interpreted as generalized radial basis function networks. We investigate how the fuzzy models can...
详细信息
This paper deals with nonlinear predictive control based on higher order Takagi-Sugeno fuzzy systems which can also be interpreted as generalized radial basis function networks. We investigate how the fuzzy models can be linked to a special type of model based predictive control algorithm, namely the dynamic matrix control (DMC). Previously, purely linear step response models were used for long-range prediction. Here, the method is extended to nonlinear processes. Therefore, various step responses for different operating points are extracted from the fuzzy model. For performance evaluation, a heat exchanger is identified by means of the local linear model tree algorithm and controlled by the modified DMC.
In this contribution, fuzzy model-based predictive control is applied to temperature control of a cross-flow water/air heat exchanger. After a review of Takagi-Sugeno type fuzzy systems and their identification from m...
In this contribution, fuzzy model-based predictive control is applied to temperature control of a cross-flow water/air heat exchanger. After a review of Takagi-Sugeno type fuzzy systems and their identification from measurement data the on-line adaptation of these models is discussed. If the nonlinearity of the process is assumed to keep its structure, the linear parameters in the rule consequents can be locally updated by a recursive least-squares algorithm. Since this algorithm is computationally inexpensive it can be utilized in nonlinear model predictive control (NMPC). If the process is influenced by unmeasurable or unmodeled disturbances, the fuzzy model is adapted to these quantities. It can be distinguished between disturbances and model deficiencies which require the adaptation of all linear parameters in the rule consequents and model offsets in the static mapping which can be canceled by local adaptation of one offset parameter per rule. The second case is particularly simple to implement because there is no need for persistent excitation. Moreover, this mode of operation copes with the challenges in control which are specific to the heat exchanger. The effectiveness of the adaptive nonlinear model predictive controller is proven by application to an industrial-scale pilot plant.
Intelligent automationsystems integrate a great variety of methods based on models of the controlled processes, e. g. model-based control or model-based supervision and fault diagnosis. Hence, there emerges a great d...
Intelligent automationsystems integrate a great variety of methods based on models of the controlled processes, e. g. model-based control or model-based supervision and fault diagnosis. Hence, there emerges a great demand for modeling techniques suitable for the identification of nonlinear dynamic systems. Besides classical approaches, neuro/fuzzy systems appear promising due to their approximation qualities. In this contribution, it is investigated how Takagi-Sugeno type fuzzy systems can be utilized for identification of a cross-flow water/air heat exchanger. On the one hand, these models provide transparency and interpretability which allows to incorporate qualitative and quantitative prior knowledge about the plant. On the other hand, Takagi-Sugeno models can also be identified from measurement data in order to compensate for incomplete system knowledge. Data gathering requires appropriate excitation of the system. It will be shown how elementary prior knowledge can be exploited for the design of identification signals. The concept of error bars is reviewed, and it is used to roughly evaluate the model quality depending on the properties of the excitation signal. For identification from training data the local linear model tree algorithm (LOLIMOT) is applied.
The development of a reliable fault detection and isolation (FDI) scheme for nonlinear processes is often laborious and difficult to achieve due to the complexity of the system. Neural networks and fuzzy models, able ...
详细信息
The development of a reliable fault detection and isolation (FDI) scheme for nonlinear processes is often laborious and difficult to achieve due to the complexity of the system. Neural networks and fuzzy models, able to approximate nonlinear dynamic functions offer a powerful tool to cope with this problem. In this paper, a multi-model approach for FDI of sensor faults on a highly nonlinear real world processes is introduced. The approach is based local linear fuzzy models and provides residuals which are structured in a way that detection of six and isolation of five different sensor faults over all ranges of operation becomes possible. The approach enables on-line supervision of the process. Furthermore, a sensitivity analysis of the residuals to different faults can be made, based on the parameters of the fuzzy models.
暂无评论