Feedback linearization using neural networks
Automatica (Journal of IFAC)
A direct adaptive neural command controller design for an unstable helicopter
Engineering Applications of Artificial Intelligence
Neural network model-based automotive engine air/fuel ratio control and robustness evaluation
Engineering Applications of Artificial Intelligence
Brief paper: Design and implementation of an autonomous flight control law for a UAV helicopter
Automatica (Journal of IFAC)
Neural block control for synchronous generators
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Brief paper: Adaptive RTRL based neurocontroller for damping subsynchronous oscillations using TCSC
Engineering Applications of Artificial Intelligence
Synthesis of a helicopter nonlinear flight controller using approximate model inversion
Mathematical and Computer Modelling: An International Journal
On adaptive trajectory tracking of a robot manipulator using inversion of its neural emulator
IEEE Transactions on Neural Networks
An integrated approach to hypersonic entry attitude control
International Journal of Automation and Computing
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This paper investigates the development and experimental implementation of an adaptive dynamic nonlinear model inversion control law for a Twin Rotor MIMO System (TRMS) using artificial neural networks. The TRMS is a highly nonlinear aerodynamic test rig with complex cross-coupled dynamics and therefore represents the control challenges of modern air vehicles. A highly nonlinear 1DOF mathematical model of the TRMS is considered in this study and a nonlinear inverse model is developed for the pitch channel of the system. An adaptive neural network element is integrated thereafter with the feedback control system to compensate for model inversion errors. The proposed on-line learning algorithm updates the weights and biases of the neural network using the error between the set-point and the real output. The real-time response of the method shows a satisfactory tracking performance in the presence of inversion errors caused by model uncertainty. The approach is therefore deemed to be suitable to apply real-time to other nonlinear systems with necessary modifications.