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PRODUCT DESIGN: The six phases of the generic development process
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PRODUCT DESIGN About this project Ready to get started?Explore our interior design service or shop our products Neural-network-based model reference adaptive systems for high-performance motor drives and motion controls
This paper focuses on designing an adaptive radial basis function neural network (RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method. The novel adaptive control method is designed to reduce the amount of computations effectively. The uniform ultimate boundedness of the closed-loop system is guaranteed by the proposed controller. A coupled motor drives (CMD) system, which satisfies the structure of nonlinear system, is taken for simulation to confirm the effectiveness of the method. Simulations show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system.

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PRODUCT DESIGN
By incorporating sliding mode control dynamics into this mapping, the output of this neural network is used as a direct tracking command. A compensator is added to prevent system dynamics from staying outside the desired range. A stable weight adjustment mechanism is determined in terms of Lyapunov theory. A simulation is performed to validate the proposed adaptive control scheme.


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This scheme can be used in the control of nonlinear systems and/or as an adaptive controller if desired. A direct adaptive neural network controller is proposed for a class of nonlinear systems. The only restriction for the plant is that the input-output gain has to be strictly monotonic. An artificial neural network is adapted to identify the inverse mapping between the input and the desired state differential.

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A compensator is added to prevent system dynamics from staying outside the desired range. Children are individuals who are developing into adulthood. This scheme can be used in the control of nonlinear systems and/or as an adaptive controller if desired. Host your publication on your website or blog with just a few clicks. A model reference structure is developed and a neural network is used as a compensator in the closed loop system.

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