This paper presents a model-based control strategy for usage in multilevel pressure boosting systems with centrifugal pumps operating in parallel. The control is based on a nonlinear system model using exactinput-out...
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ISBN:
(纸本)9798350364309;9798350364293
This paper presents a model-based control strategy for usage in multilevel pressure boosting systems with centrifugal pumps operating in parallel. The control is based on a nonlinear system model using exact input-output linearization. With the help of an optimization algorithm, the total consumption flow rate is distributed optimally in such a way that the total hydraulic efficiency of all running pumps is maximized in steady state while complying with all constraints. Trajectories for every single pump are planned on model-based relations in order to reduce overshoots in the pipeline pressure, in particular during start-up processes. The whole control strategy is validated on a test bench.
Neural networks, especially in nonlinear system identification and control applications, are typically considered to be black-boxes which are difficult to analyze and understand mathematically. Due to this reason, an ...
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ISBN:
(纸本)081944085X
Neural networks, especially in nonlinear system identification and control applications, are typically considered to be black-boxes which are difficult to analyze and understand mathematically. Due to this reason, an indepth mathematical analysis offering insight into the different neural network transformation layers based on a theoretical transformation scheme is desired, but up to now neither available nor known. In previous works it has been shown how proven engineering methods such as dimensional analysis and the Laplace transform may be used to construct a neural network controller topology for time-invariant systems. Using the knowledge of neural correspondencies of these two classical methods, the internal nodes of the network could also be successfully interpreted after training. As a further extension to these works, the paper describes the latest results of a theoretical interpretation framework describing the neural network transformation sequences in nonlinear system identification and control. This can be achieved by incorporation of the method of exact input-output linearization in the above mentioned two transformation sequences of dimensional analysis and the Laplace transformation. Based on these three theoretical considerations neural network topologies may be designed in special situations by a pure translation in the sense of a structural compilation of the known classical solutions into their correspondent neural topology. Based on known exemplary results, the paper synthesizes the proposed approach into the visionary goals of a structural compiler for neural networks. This structural compiler for neural networks is intended to automatically convert classical control formulations into their equivalent neural network structure based on the principles of equivalence between formula and operator, and operator and structure which are discussed in detail in this work.
The rotor position of magnetic bearings or bearingless slice motors is usually subject to several harmonic disturbances with time-varying frequencies. To achieve low power consumption and good vibration suppression, e...
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The rotor position of magnetic bearings or bearingless slice motors is usually subject to several harmonic disturbances with time-varying frequencies. To achieve low power consumption and good vibration suppression, efficient control algorithms have to be invoked. In this contribution, exact input-output linearization is applied to a nonlinear bearing model, followed by output regulation via the internal model principle. The resulting disturbance rejection approach does not need to know the disturbance amplitude and phase, and changes its behavior according to the measured disturbance frequency. The method is tested in simulations related to a prototype magnetic bearing.
A novel approach to the design of path-tracking controllers for a tractortrailer with off-axle hitching is presented. The novelty is in the selection of a vehicle's guide-point dependent on whether path-tracking h...
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A novel approach to the design of path-tracking controllers for a tractortrailer with off-axle hitching is presented. The novelty is in the selection of a vehicle's guide-point dependent on whether path-tracking has to be implemented in the forward direction or in reverse, and in the adoption of input-output rather than input-state linearization. This approach offers the advantage of leading to controllers that are perfectly adequate for a practical implementation and that appear to be better perfonning than otherwise obtainable on the basis of the current state of the art.
A computer assisted guidance system for a tractor trailer is developed. From a practical point of view, the ideais to provide a tractor trailer operator with a mean to simplify execution of parking manoeuvres. From a ...
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A computer assisted guidance system for a tractor trailer is developed. From a practical point of view, the ideais to provide a tractor trailer operator with a mean to simplify execution of parking manoeuvres. From a theoretical point of view, we aim at providing experimental evidence of the validity of recent input-outputexactlinearization approaches to articulated vehicles control. These objectives are pursued by designing the guidance system, by implementing an instrumented test bench to evaluate it, and by carrying out appropriate experimental tests.
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