Control of Self-Organizing Nonlinear Systems. Eckehard Scholl

Control of Self-Organizing Nonlinear Systems


Control.of.Self.Organizing.Nonlinear.Systems.pdf
ISBN: 9783319280271 | 475 pages | 12 Mb


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Control of Self-Organizing Nonlinear Systems Eckehard Scholl
Publisher: Springer International Publishing



This paper presents a self-organizing control system based on a class of multiple-input-multiple-output (MIMO) uncertain nonlinear systems. Specially, we considered self-organizing based tracking control of uncertain nonaffine systems and optimal control of uncertain nonlinear systems. Self-organizing quasi-linear ARX RBFN modeling for identification and control ofnonlinear systems. Directly from the weights of a self organizing map (SOM), which functions as a. And Applications to Nonlinear System Identification and. Thus, the proposed new method of the synthesis of self-organizing nonlinear system; nonlinear oscillations; energy invariants; energy-efficient control laws. Computation controller including a self-organizing fuzzy neural SOFNN identifier is used to online estimate the controlled system. The book summarizes the state-of-the-art of research on control of self-organizingnonlinear systems with contributions from leading international. Keywords: Multiple models; Sliding mode control; Self-organizing map; Unknownnonlinear system. We first develop a self-organizing neural fuzzy network (SONFN) with concurrent tracking control problems of unknown nonlinear MIMO systems. Network (SORBFNN) is proposed for nonlinear systems. Control of Self-Organizing NonlinearSystems Adaptively Controlled Synchronization of Delay-Coupled Networks. 4 Self-organizing dynamics in human sensorimotor coordination and perception Part IV Nonlinear control of biological and other excitable systems. PhD: Lateral self-organization in nonlinear transport systems described by S. Complex systems are usually difficult to design and control. Nowadays, a good control system must meet some complex requirements. Herrmann lab at the Max-Planck-Institute for Dynamics and Self-Organization, Germany. This paper proposes a self-organizing adaptive fuzzy neural control (SAFNC) via sliding-mode approach for a class of nonlinear systems.





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