Nonlinear order-reduced adaptive controller for a DC motor driven electric cart

2014 ◽  
Author(s):  
Jozsef K. Tar ◽  
Tamas Haidegger ◽  
Levente Kovacs ◽  
Krisztian Kosi ◽  
Balazs Botka ◽  
...  
Keyword(s):  
Adaptive Controller ◽  
Dc Motor

scholarly journals Nonlinear Controllers for a Light-Weighted All-Electric Vehicle Using Chebyshev Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Vikas Sharma ◽  
Shubhi Purwar
Keyword(s):  
Neural Network ◽  
Electric Vehicle ◽  
Aerodynamic Drag ◽  
Rolling Resistance ◽  
Adaptive Controller ◽  
Dc Motor ◽  
Resistance Coefficient ◽  
Chebyshev Neural Network ◽  
Nonlinear Controllers ◽  
Aerodynamic Drag Coefficient

Two nonlinear controllers are proposed for a light-weighted all-electric vehicle: Chebyshev neural network based backstepping controller and Chebyshev neural network based optimal adaptive controller. The electric vehicle (EV) is driven by DC motor. Both the controllers use Chebyshev neural network (CNN) to estimate the unknown nonlinearities. The unknown nonlinearities arise as it is not possible to precisely model the dynamics of an EV. Mass of passengers, resistance in the armature winding of the DC motor, aerodynamic drag coefficient and rolling resistance coefficient are assumed to be varying with time. The learning algorithms are derived from Lyapunov stability analysis, so that system-tracking stability and error convergence can be assured in the closed-loop system. The control algorithms for the EV system are developed and a driving cycle test is performed to test the control performance. The effectiveness of the proposed controllers is shown through simulation results.

Design of BLDCM Control System with Fuzzy Adaptive Controller

2013 ◽  
Vol 765-767 ◽  
pp. 1791-1795 ◽  
Author(s):  
Zheng Zhong Li ◽  
Li Xia Guo ◽  
Guo Fang Gao
Keyword(s):  
Control System ◽  
Pid Control ◽  
Digital Signal Processor ◽  
High Performance ◽  
Digital Signal ◽  
Adaptive Controller ◽  
Dc Motor ◽  
System Structure ◽  
Brushless Dc Motor ◽  
Brushless Dc

To handle the shortages of conventional PID control, recur to the high-performance digital signal processor, combine the fuzzy self-adaption controller with brushless dc motor control system. The results show the control system structure is simplified and the performances of control system are improved comparing to the conventional PID control, the performance index is better than that in conventional PID control system, so the stability of brushless dc motor operation is strengthened.

scholarly journals Controlling a DC Motor through Lypaunov-like Functions and SAB Technique

2018 ◽  
Vol 8 (4) ◽  
pp. 2180
Author(s):  
Alejandro Rincón ◽  
Fabiola Angulo ◽  
Fredy Hoyos
Keyword(s):  
Digital Signal ◽  
Adaptive Controller ◽  
Dc Motor ◽  
Motor Speed ◽  
Adaptive Backstepping ◽  
Inertia Moment ◽  
Rapid Control ◽  
Rapid Control Prototyping ◽  
Robust Adaptive ◽  
Robust Adaptive Controller

<p>In this paper, state adaptive backstepping and Lyapunov-like function methods are used to design a robust adaptive controller for a DC motor. The output to be controlled is the motor speed. It is assumed that the load torque and inertia moment exhibit unknown but bounded time-varying behavior, and that the measurement of the motor speed and motor current are corrupted by noise. The controller is implemented in a Rapid Control Prototyping system based on Digital Signal Processing for dSPACE platform and experimental results agree with theory.</p>

Sign in / Sign up

Export Citation Format

Share Document