Showing papers by "Rolf Isermann published in 1996"

Basis function networks for interpolation of local linear models

Abstract: In this paper, a new algorithm (LOLIMOT) for nonlinear dynamic system identification with local linear models is proposed. The input space is partitioned by a tree-construction algorithm. The local models are interpolated by overlapping local basis functions. The resulting structure is equivalent to a Sugeno-Takagi fuzzy system and a local model network and can therefore be interpreted correspondingly. The LOLIMOT algorithm is very simple, easy to implement, and fast. Moreover, this approach has the following appealing properties: it does not underlie the "curse of dimensionality", it reveals irrelevant inputs, it detects inputs that influence the output mainly in a linear way, and it applies robust local linear estimation schemes. The drawbacks are that only orthogonal cuts are performed and that the local estimation approach may lead to interpolation errors.

Modeling and design methodology for mechatronic systems

Abstract: The integration of mechanical systems and microelectronics opens many new possibilities for process design and automatic functions. After discussing the mutual interrelations between the design of the mechanical system and digital electronic system the different ways of integration within mechatronic systems and the resulting properties are described. The information processing can be organized in multi levels, ranging from low level control through supervision to general process management. In connection with knowledge bases and inference mechanisms, intelligent control systems result. The design of control systems for mechanical systems is described, from modeling, identification to adaptive control for nonlinear systems. This is followed by solving supervision tasks with fault diagnosis. Then design tools for mechatronic systems are considered and examples of applications are given, like adaptive control of electromagnetic and pneumatic actuators, adaptive semiactive shock absorbers for vehicle suspension, and electronic drive-chain damping.

Local basis function networks for identification of a turbocharger

Abstract: This paper deals with nonlinear dynamic system identification by local basis function networks A special kind of local basis function network generated by a tree construction algorithm is proposed This local linear model tree (LOLIMOT) is applied for identification of a truck diesel engine exhaust turbocharger The charging pressure is modelled as the output of a nonlinear second order multiple input system with engine speed and injection rate as inputs The LOLIMOT approach was capable to identify the turbocharger with measured signals during road driving and with ten local linear models in less than one minute on a Pentium PC

On the design and control of mechatronic systems-a survey

Abstract: The integration of mechanical systems and microelectronics opens new possibilities for mechanical design and automatic functions. After a discussion of the mechanical and electronic design the organization of information processing in different levels is described. Within this frame "low-degree-intelligent" mechatronic systems can be developed which comprise adaptive control, supervision with fault diagnosis, and decisions with regard to further actions. This requires the realization of knowledge-based systems with learning abilities. Some aspects of the design of information processing including modeling and estimation, control, and supervision methods are considered. Finally as an example an adaptive semiactive shock absorber or vehicle suspension systems is shown.

Robust hybrid control based on inverse fuzzy process models

Abstract: Takagi-Sugeno type fuzzy models are universal approximators for nonlinear dynamic processes. If they are trained to represent the inverse plant characteristics they can be used as feedforward controllers. The achievable control performance strongly depends on the model quality, and the simple inverse model controller does not cope with disturbances and process uncertainty. As a consequence, a hybrid control scheme is proposed which considerably improves the robustness properties. This paper reviews the identification of both forward and inverse fuzzy models. The difficulties accompanying the latter task are discussed. The control scheme based on the idea of disturbance observation is introduced and thoroughly analyzed. Finally, the controller is applied to a cooling blast with nonlinear behavior and variant dynamics.

Implant for controlled draining of cerebrospinal fluid esp. for therapy of hydrocephalus

Abstract: The implant works with a catheter, with a first free end led to a point in the brain, where excessive fluid can be drained off. A second free end is led in to the body, where the excessive fluid can be received. A valve is fitted in the course of the catheter. A measuring unit (20) is connected to the sensor (9,10,11), which measures the amount of fluid per time unit, flowing through the catheter. The measuring unit for the adjustment and/or the monitoring of the valve, works in conjunction with an adjusting unit (23) and/or with a control unit located outside the body. The through flow sensor consists of a selected flow resistance and a pressure sensor. The pressure sensor measures the pressure drop at the resistance

Towards intelligent mechatronic systems

Abstract: The integration of mechanical systems and microelectronics opens new possibilities for mechanical design and automatic functions After a discussion of the mechanical and electronic design the organization of information processing in different levels is described Within this frame ‘low-degree intelligent’ mechatronic systems can be developed which comprise adaptive control, supervision with fault diagnosis and decisions with regard to the further actions as, for example, fail safe or reconfiguration This requires the realization of knowledge based systems in the form of on-line expert systems with learning abilities Some aspects of the design including modeling and estimation, control and supervision methods are considered Finally as an example an intelligent magnetic actuator is shown which uses parameter estimation for adaptive control and fault detection

Implant for controlled drainage of brain fluid esp for treating hydrocephalus condition

Abstract: The implant works with a catheter, with a first free end led to a point in the brain, where excessive fluid can be drained off. A second free end is led in to the body, where the excessive fluid can be received. A valve is fitted in the course of the catheter. A measuring unit is connected to the sensor, which measures the amount of fluid per time unit, flowing through the catheter. The measuring unit for the adjustment and/or the monitoring of the valve, works in conjunction with an adjusting unit and/or with a control unit located outside the body. The through flow sensor consists of a selected flow resistance and a pressure sensor. The pressure sensor measures the pressure drop at the resistance

Identification of nonlinear dynamic processes based on dynamic radial basis function networks

Abstract: An attempts has been made to establish a discrete-time neuron model with a radial basis function. The neuron is utilized to build RBF-networks with locally distributed dynamics to identify input/output models of dynamic nonlinear processes. The adaptation algorithm which ascertains the optimal network parameters is provided. Further, an enhanced parameter estimation algorithm is derived, the so-called compound estimation procedure, which combines elaborated least squares techniques to highly decrease the training times. The proposed neural model is applied to identify black-box models of a turbocharging process within a Diesel engine. Benefits and drawbacks of the proposed neural structure are worked out.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Intelligent electromechanical actuators

Abstract: Servo systems play an important role in many automated processes. In order to fulfill the hard demands on reliability and fast and precise operation, intelligent concepts for the control, supervision and (re)configuration are necessary. In this paper, an approach is presented which integrates different levels of signal processing in an electromechanical servo system. The digital controller and the model-based fault detection scheme are designed taking into account model-uncertainty and the time variant process behavior, which is caused by temperature influences, wear, aging, etc. After a brief description of the theoretical basis an experimental application shows results for an automobile servo system which is driven by a d.c. motor.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.