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A nonlinear multivariable adaptive decoupling PID control strategy based on multiple models and neural network is proposed for a class of uncertain discrete time nonlinear dynamical systems. The adaptive decoupling PID controller is composed of a linear adaptive PID decoupling controller, a neural network nonlinear adaptive PID decoupling controller and a switch mechanism. The PID parameters of such...
This paper presents Model predictive control (MPC) of nonlinear hybrid system based on neural network (NN) optimization. Multiple model method is used to modeling of nonlinear hybrid system and these models are combined using Bayes theorem. NN optimization combined gradient NN with recurrent NN is proposed to solve optimization problem of each sample time in MPC. An example of benchmark three spherical...
This paper presents the design of a neural network-based feedback linearisation (NARMA-L2) slip controller for an anti-lock braking system (ABS). The dynamics of the electro-mechanical based braking system are incorporated in the ABS model and thus a slip controller is developed to minimise the braking distance. The proposed controller is compared with an optimally-tuned PID controller. Simulation...
In order to deal with the control problems of nonlinearity and difficulty of establishing an exact model of a micro turbine engine, a control algorithm based on neural network is proposed. The RBF neural network can identify the output value online, which is used by the single neuron controller to adjust its parameters based on a gradient algorithm. The simulation and rig test experiments show that,...
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