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A fighter aircraft pitch-rate command-tracking controller based on a neural network parallel controller is proposed. The scheme consists of an online radial basis function neural network (RBFNN) in parallel with a model reference adaptive controller (MRAC) and uses a growing dynamic RBFNN to augment MRAC. Updating the RBFNN width, the center and weight characteristics are performed such that the error...
It is difficult to structure a precise math mode in the control of room's temperature for air-conditioning, because the controlled system has nonlinearity,change parameters, large delay and more disturbing. A new controller based on fuzzy control theory is designed to control room's temperature for air-conditioning in this paper.In the control,neural network is used to train fuzzy reason parameters...
Rule number explosion and adaptive weights tuning are two main issues in the design of fuzzy control systems. To overcome the problems, we propose the method of state variable composition, and combine it with adaptive control scheme to tune controller parameters. Furthermore, LQR feedback controller coefficients of double inverted pendulum are used as factors of state variable composition to design...
It is difficult to establish accurate models for complex flight control systems, but neural network has arbitrary nonlinear approximation ability. In order to overcome modeling errors and disturbances, a method of hybrid flight control is proposed. Firstly, inverse model of the object is identified online through neural networks and the feedback linearization control system is reached. And then circle...
In this paper dynamic surface control (DSC) technique is combined with neural network based adaptive control design framework to design the longitudinal dynamics controller for a nonlinear generic hypersonic air vehicle (HSAV). Detailed stability analysis is carried out to prove the uniform ultimate boundedness of all the signals in the closed loop system. The effectiveness of the proposed strategy...
A dynamic control system design has been a great demand in the control engineering community, with many applications particularly in the field of flight control. This paper presents investigations into the development of a dynamic nonlinear inverse-model based control of a twin rotor multi-input multi-output system (TRMS). The TRMS is an aerodynamic test rig representing the control challenges of...
The focus of this paper is on nonlinear adaptive inversion control system of a longitudinal hypersonic vehicle model with neural network compensation for uncertain effects. Considering the partially unknown model dynamic for hypersonic vehicle, and the adaptive dynamic inversion control method with neural network compensation is presented, which can compensate the hypersonic vehicle model errors by...
Influenced by the results obtained in neuroscience and biology, we have introduced a model (AIRM) that, inspired by biological rhythms, adaptively controls a behavior based robotic system (BBRS). The proposed model is implemented by means of an NSP (neuro symbolic processor). Since the NSP can be implemented on FPGA, we can take advantage of a parallel execution of the AIRM model and then an improvement...
Because the flight simulator works in different environment for simulating various flight attitudes, the different control algorithm is used for improving the dynamic and static performance. The neural network has good self-learning and self-adaptive, and PID has good robustness, so the adaptive controller based on neural network and PID is used to overcome the coupling inertia and imbalance torque...
A method of multi-variable neural network adaptive control based on dynamical recurrent neural network was put forward to control aeroengine with strong nonlinearity and time-varying uncertainty. In the method, nonlinear model of engine was real-time identified by dynamical recurrent neural network, and system sensitivity information was real-time feed back to neural network controller so that controller...
The paper considers the problem of wing load control problems. To stabilize and control the wing load of the aircraft, an active wing load control system is designed. The dynamic model inversion is used as the feedback linearization method of choice. To dispose the model drift and instability of the aerodynamic model, an on line adaptive neural networks is introduced to reconstruct the inversion error...
Considered influence of nonlinear system and disturbance, a new adaptive fault-tolerant control method based on neural network model-following adaptive inversion control is introduced for flight control system in the presence of control surface damage. To restrain modeling uncertainties caused by fault system, neural network PID and inversion controllers are design for fault-tolerant flight control...
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