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A control scheme combining novel model reference adaptive control (MRAC) and neural network (NN) is proposed in this paper to achieve high tracking precision for servo systems. This scheme comprises an MRAC controller and an online NN controller in the velocity-loop and a traditional PD controller in the position-loop. For reducing influences which arose from modeling error, unknown model dynamics,...
Focused on robustness, low power consumption and unbalance compensation demands from Magnetic Suspended Flywheel (MSF), a network controller is presented using on adaptive linear neuron, and characteristic equation is derived and discussed form the point of stability, then, a method to ensure close loop stability is established by checking the update of neural network weight. Simulations are performed...
To track the reference attitude angles and angular accelerations in the presence of strong uncertainties and disturbances, a new robust generalized predictive control (GPC) law is presented for a near-space hypersonic vehicle (NHV). The control law consists of the optimal GPC law and radius basis function disturbance observer (RBFDO). The output prediction defined on finite horizon is carried out...
In this paper, we propose a new type of neural adaptive control via dynamic neural networks. For a class of unknown nonlinear systems, a neural identifier-based feedback linearization controller is first used. Dead-zone and projection techniques are applied to assure the stability of neural identification. Then four types of compensator are addressed. The stability of closed-loop system is also proven.
A new scheme of incorporating the fault tolerance and disturbance rejection in an existing flight control system is proposed. Method to design a standalone compensator is suggested which is added to the original system in order to guarantee the system stability and performance in the presence of fault or disturbances. Thus the controller, which is designed separately considering all the design parameters...
A robust adaptive neural network control scheme is proposed for a class of strict-feedback nonlinear systems with unknown control directions and unmodeled dynamics. The proposed design method expands the class of nonlinear systems for which robust adaptive control approaches have been studied. A priori knowledge of the signs of the control directions is not required. It is proved that under the proposed...
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