Adaptive Neural Network– Based Autopilot Design

2012 ◽  
Author(s):  
Karthikeyan Rajagopal ◽  
S Balakrishnan
Keyword(s):  
Neural Network ◽  
Adaptive Neural Network

scholarly journals Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

2021 ◽  
pp. 002029402110211
Author(s):  
Tao Chen ◽  
Damin Cao ◽  
Jiaxin Yuan ◽  
Hui Yang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Sliding Mode Control ◽  
Fractional Order ◽  
Sliding Mode ◽  
Tracking Error ◽  
Adaptive Neural Network ◽  
Dynamic Surface ◽  
Mode Control ◽  
The Stability

This paper proposes an observer-based adaptive neural network backstepping sliding mode controller to ensure the stability of switched fractional order strict-feedback nonlinear systems in the presence of arbitrary switchings and unmeasured states. To avoid “explosion of complexity” and obtain fractional derivatives for virtual control functions continuously, the fractional order dynamic surface control (DSC) technology is introduced into the controller. An observer is used for states estimation of the fractional order systems. The sliding mode control technology is introduced to enhance robustness. The unknown nonlinear functions and uncertain disturbances are approximated by the radial basis function neural networks (RBFNNs). The stability of system is ensured by the constructed Lyapunov functions. The fractional adaptive laws are proposed to update uncertain parameters. The proposed controller can ensure convergence of the tracking error and all the states remain bounded in the closed-loop systems. Lastly, the feasibility of the proposed control method is proved by giving two examples.

On using an adaptive neural network to predict lung tumor motion during respiration for radiotherapy applications

2005 ◽  
Vol 32 (12) ◽  
pp. 3801-3809 ◽  
Author(s):  
Marcus Isaksson ◽  
Joakim Jalden ◽  
Martin J. Murphy
Keyword(s):  
Neural Network ◽  
Lung Tumor ◽  
Tumor Motion ◽  
Adaptive Neural Network

scholarly journals An Intelligent Adaptive Neural Network Controller for a Direct Torque Controlled eCAR Propulsion System

2021 ◽  
Vol 12 (1) ◽  
pp. 44
Author(s):  
Gururaj Banda ◽  
Sri Gowri Kolli
Keyword(s):  
Neural Network ◽  
Pulse Width Modulation ◽  
Simulation Software ◽  
Propulsion System ◽  
Torque Control ◽  
Matlab Simulation ◽  
Time Data ◽  
Adaptive Neural Network ◽  
Adaptive Controllers ◽  
Neural Network Controller

This article deals with an intelligent adaptive neural network (ANN) controller for a direct torque controlled (DTC) electric vehicle (EV) propulsion system. With the realization of artificial intelligence (AI) conferred adaptive controllers, the torque control of an electric car (eCAR) propulsion motor can be achieved by estimating the stator reference flux voltage used to synthesize the space vector pulse width modulation (SVPWM) for a DTC scheme. The proposed ANN tool optimizes the parameters of a proportional integral (PI) controller with real-time data and offers splendid dynamic stability. The response of an ANN controller is examined over standard drive cycles to validate the performance of an eCAR in terms of drive range and energy efficiency using MATLAB simulation software.

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