Model Reference Adaptive Control of Train Dynamic Braking

2012 ◽  
Author(s):  
Husain Ahmad ◽  
Mehdi Ahmadian
Keyword(s):  
Adaptive Control ◽  
Aerodynamic Drag ◽  
Adaptive Controller ◽  
Model Reference Adaptive Control ◽  
Accurate Estimation ◽  
Model Reference ◽  
Traction Motors ◽  
Braking Distance ◽  
Dynamic Braking ◽  
Model Reference Adaptive

Model Reference Adaptive Control (MRAC) is developed to control the amount of current through the traction motors under various wheel/rail adhesion conditions while braking. More accurate estimation and control of train braking distance will allow the trains to be run with closer spacing. In order to minimize the braking distance of a train, dynamic braking forces need to be maximized while maintaining good wheel/rail adhesion. Wheel/rail adhesion coefficient plays an important role in safe train braking. Excessively large dynamic braking can cause wheel lockup that can damage the wheels and the rail. In addition, it can cause large buff loads that cause derailment or coupler damage. Dynamic braking force is directly proportional to the current supplied to the traction motors. In this study, a multibody formulation of a locomotive and three railcars is used to develop a model reference adaptive controller for adjusting the current provided to the traction motors such that the maximum dynamic braking is achieved, without wheel lockup. Aerodynamic drag and air brake forces are included in the model. The coupler forces are also considered in the control model to ensure that they remain within acceptable levels. The results indicate that the MRAC system significantly improves braking distance while maintaining better wheel/rail adhesion and coupler dynamics during braking.

Adapting Dynamic Braking of AC Motors to Varying Wheel/Rail Adhesion Condition

2013 ◽  
Author(s):  
Husain Ahmad ◽  
Mehdi Ahmadian
Keyword(s):  
Reference Model ◽  
Excitation Frequency ◽  
Adaptive Controller ◽  
Rolling Stock ◽  
Accurate Estimation ◽  
Model Reference ◽  
Braking Distance ◽  
Estimation And Control ◽  
Dynamic Braking ◽  
Model Reference Adaptive

Model reference adaptive control (MRAC) is developed to control the electrical excitation frequency of AC traction motors under various wheel/rail adhesion conditions during dynamic braking. More accurate estimation and control of train braking distance can allow more efficient braking of rolling stock, as well as spacing trains closer together for Positive Train Control (PTC). In order to minimize the braking distance of a train, dynamic braking forces need to be maximized for varying wheel/rail adhesion. The wheel/rail adhesion coefficient plays an important role in safe train braking. Excessively large dynamic braking can cause wheel lockup that can damage the wheels and rail, or may lead to large coupler forces, possibly causing derailment or broken components. In this study, a multibody formulation of a locomotive and three railcars is used to develop a model reference adaptive controller for adjusting the voltage excitation frequency of an AC motor such that the maximum dynamic braking is achieved, without locking up the wheels. A relationship between creep forces, creepages, and motor braking torque is established. This relationship is used to control the motor excitation frequency in order to closely follow the reference model that aims at achieving maximum allowable adhesion during dynamic braking. The results indicate that MRAC significantly improves braking distance while maintaining better wheel/rail adhesion and coupler dynamics during dynamic braking.

Nonlinear L1 adaptive control of an airplane with structural damage

2017 ◽  
Vol 233 (1) ◽  
pp. 341-353 ◽  
Author(s):  
Karim Ahmadi ◽  
Davood Asadi ◽  
Farshad Pazooki
Keyword(s):  
Adaptive Control ◽  
Control Strategy ◽  
Nonlinear Dynamic ◽  
Structural Damage ◽  
Adaptive Controller ◽  
Model Reference Adaptive Control ◽  
Dynamic Inversion ◽  
Model Reference ◽  
Model Reference Adaptive ◽  
Nonlinear Dynamic Inversion

This paper investigates the design of a novel nonlinear L1 adaptive control architecture to stabilize and control an aircraft with structural damage. The airplane nonlinear model is developed considering center of gravity variation and aerodynamic changes due to damage. The new control strategy is applied by using nonlinear dynamic inversion as a baseline augmented with an L1 adaptive control strategy on NASA generic transport model in presence of un-modeled actuator dynamics, wing and vertical tail damage. The L1 adaptive controller with appropriate design of filter and gains is applied to accommodate uncertainty due to structural damage and un-modeled dynamics in the nonlinear dynamic inversion loop, and to meet desired performance requirements. The properties of the proposed nonlinear adaptive controller are investigated against a model reference adaptive control, a robust model reference adaptive control, and an adaptive sliding mode control strategy. The results clearly represent the excellent overall performance of the designed controller.

The Design of a Signal Synthesis Model Reference Adaptive Control System for Linear Unknown Plants

1977 ◽  
Vol 99 (2) ◽  
pp. 123-129 ◽  
Author(s):  
P. N. Nikiforuk ◽  
M. M. Gupta ◽  
K. Tamura
Keyword(s):  
Adaptive Control ◽  
Linear Time ◽  
Adaptive Controller ◽  
General Concept ◽  
Model Reference Adaptive Control ◽  
The State ◽  
Model Reference ◽  
Control Techniques ◽  
Signal Synthesis ◽  
Model Reference Adaptive

In this paper, a design of a signal synthesis adaptive controller for a class of linear time-varying uncertain plants using model reference adaptive control techniques is presented. Following a discussion of the general concept, the design of an adaptive controller for unconstrained and constrained control conditions is given. The small ultimate boundness of the state error is considered as the adaptation criterion, which is shown to be satisfied by a Liapunov type stability theorem. To handle the uncertainties that are associated with the plant dynamics and its environment, a min-max concept is employed in the design of the controller. The signal synthesis adaptive approach presented in this paper does not require as many structural assumptions as do most other adaptive approachers, and this is its principal advantage. The state vectors of the plant and model are assumed to be both accessible. Some simulation results are presented which illustrate the effectiveness of the design given here.

Model Reference Adaptive Control of Dual-Mode Micro/Macro Dynamics of Ball Screws for Nanometer Motion

1993 ◽  
Vol 115 (1) ◽  
pp. 103-108 ◽  
Author(s):  
P. I. Ro ◽  
P. I. Hubbel
Keyword(s):  
Adaptive Control ◽  
Sliding Friction ◽  
Position Control ◽  
Adaptive Controller ◽  
Model Reference Adaptive Control ◽  
Ball Screw ◽  
Model Reference ◽  
Frictional Contacts ◽  
Motion Characterization ◽  
Model Reference Adaptive

A desire to improve the positioning accuracy of ball screws prompted an investigation into the dynamics of nanometer motion. Characterization of the ball screw indicated that nanometer motion is possible prior to friction breakaway via elastic deformation of the frictional contacts while macroscopic motion involves slipping across the friction interfaces. The observed dynamics are nonlinear, and consequently result in inconsistent and unpredictable closed-loop response while under PI position control. The ball-screw can be modeled in two stages: The microdynamic stage includes “elastic” friction while the macrodynamic stage incorporates kinetic (sliding) friction. A two-stage model reference adaptive control (MRAC) strategy is adopted and a Lyapunov design technique is applied to derive the adaptive laws. Experimental results obtained via a DSP implementation of the adaptive controller indicate that the each stage of the adaptive control performs well within the respective dynamic regions, but performance deteriorates as either controller is operated near the boundary of the regions.

Evolution of Adaptive Control

1993 ◽  
Vol 115 (2B) ◽  
pp. 381-391 ◽  
Author(s):  
Ioan D. Landau
Keyword(s):  
Adaptive Control ◽  
Adaptive Controller ◽  
Minimum Variance ◽  
Model Reference Adaptive Control ◽  
General Purpose ◽  
Control Algorithms ◽  
Model Reference ◽  
Control Approach ◽  
Systems Research ◽  
Model Reference Adaptive

The evolution of the adaptive control algorithms driven by the results obtained in the application of the 1st generation of adaptive controllers (model reference adaptive control, self-tuning minimum variance) is examined. Research in the field of adaptive control has been directed, on the one hand, toward the development of a robust general purpose adaptive controller and, on the other, towards the extension of the model reference adaptive control approach to nonlinear systems. Research has also investigated the stability/passivity approach for developing dedicated adaptive control algorithms for particular classes of nonlinear plants (e.g., rigid robots). The paper will review the results obtained in these directions both from the theoretical and the practical points of view. In the final part, current research directions will be included.

scholarly journals Fractional-Order Closed-Loop Model Reference Adaptive Control for Anesthesia

Algorithms ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 106 ◽  
Author(s):  
Gerardo Navarro-Guerrero ◽  
Yu Tang
Keyword(s):  
Adaptive Control ◽  
Fractional Order ◽  
Closed Loop ◽  
Adaptive Controller ◽  
Model Reference Adaptive Control ◽  
Loop Model ◽  
Unknown Parameters ◽  
Model Reference ◽  
State Measurement ◽  
Model Reference Adaptive

The design of a fractional-order closed-loop model reference adaptive control (FOCMRAC) for anesthesia based on a fractional-order model (FOM) is proposed in the paper. This proposed model gets around many difficulties, namely, unknown parameters, lack of state measurement, inter and intra-patient variability, and variable time-delay, encountered in controller designs based on the PK/PD model commonly used for control of anesthesia, and allows to design a simple adaptive controller based on the Lyapunov analysis. Simulations illustrate the effectiveness and robustness of the proposed control.

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