Nonlinear Internal Model Control with Automotive Applications
By (author) Dieter Schwarzmann
Paperback (Published)
(February 2008)
ISBN: 9783832518233
5.71 x 8.27 inches
Price: $61.00
Out of stock
This work develops an internal model control (IMC) design method for nonlinear plants and employs this method to design pressure controllers for a one-stage and a two-stage turbocharged diesel engine. The main focus lies on developing an applicable controller design method for automotive control problems. Automotive applications are characterised by a combination of the limited computational power of the car’s on-board control unit and the nonlinear character of the systems to be controlled. Moreover, a required step in the development of series production controllers is the manual adaptation (calibration) of the controller parameters after the actual design. For this reason, the controller should provide tunable parameters. The parameters of the internal model of an IMC controller are chosen to serve for this purpose. Thus, IMC is proposed as the control structure. The contribution of this thesis is two-fold. First, this work presents an IMC design procedure for nonlinear single-input, single-output systems. The nonlinear IMC, as proposed here, is based on the IMC structure known from linear systems and is based on a nonlinear feedforward control design. It is inversion-based and uses a low-pass state-variable filter which connects to the right inverse of the plant model to obtain a realisable IMC controller. Basic system properties, such as relative degree and internal dynamics, are exploited to extend the system class to stable and invertible plants. Input constraints and model singularities are taken into account by using a nonlinear low-pass filter that is made aware of the possible input/output behaviour of the model. This awareness is introduced by a model-dependent constraint of the filter’s highest output derivative. The nonlinear IMC provides robust stability and robust tracking of the closed-loop system. Second, the feasibility of this control scheme is presented. A single-input, single-output boost-pressure IMC controller is designed for a one-stage turbocharged diesel engine. The controlled plant was tested at the test bed and showed good results, surpassing the performance of the production PID-type controller. Two-stage turbocharging recently produced interest among car manufacturers and poses a challenging control problem due to the nonlinearity of the MIMO plant and a singularity of its inverse. This thesis presents the first model-based solution to this control problem. A multi-input, multi-output nonlinear IMC controller is designed and tested in simulations, showing good performance and robustness.
- By (author) Dieter Schwarzmann
Similar Books
Care in an Era of New Technologies and Artificial Intelligence
Relationships in a Connected World
Volume 14
Buchblogs zwischen Passion und Profession
Zur Diskursivierung digitaler literaturbezogener Anschlusskommunikation als Arbeit
Volume 5
Proceedings of the 7th Symposium of the Hellenic Society for Archaeometry
Archaeology Archaeometry: 30 Years Later
Die Einfuhrung ins richtige Handeln in der Arithmetik (Madhal ar-rasad ila ‘ilm al-‘adad) von al-Qalasadi (st. 1486)
Bearbeitung und Einordnung in die magribinische Mathematikgeschichte
Volume 19
I disegni e i discorsi di Giovanni Antonio Nigrone vol. I
fontanaro e ingegniero de acqua (1585-1609 ca.)
Volume 481
I disegni e i discorsi di Giovanni Antonio Nigrone vol. II
Fontanaro e ingegniero de acqua (1585-1609 ca.)
Volume 497
Analysing Data from Capacitive Floor Sensors for Human Gait Assessment Using Artificial Neural Networks
Volume 5
Modeling Methods for Process Induced Distortions of CFRP-Parts produced in the Prepreg-Autoclave-Process
Volume 20
Performance Management in Humanitarian Logistics
Development of a Process-driven and IT-supported Performance Measurement System
Volume 66
Supporting Operational and Real-time Planning Tasks of Road Freight Transport with Machine Learning
Guiding the Implementation of Machine Learning Algorithms
Volume 69
