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.

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 Motion Control Research of Servo Press

2014 ◽  
Vol 532 ◽  
pp. 212-217
Author(s):  
Juan Zhang ◽  
Xing Song Wang
Keyword(s):  
Adaptive Control ◽  
Real Time ◽  
Control Strategy ◽  
Pid Control ◽  
Position Control ◽  
Model Reference Adaptive Control ◽  
Model Reference ◽  
Servo Press ◽  
Adaptive Control Strategy ◽  
Model Reference Adaptive

Servo press is a new developing trend in forming equipment. In order to improve the position control accuracy, the movement rule of crank press slide block is analyzed. In view of the servo press poison PID control algorithm parameters cant realize real-time adjustment, model reference adaptive control strategy is presented, and a real-time control experiment platform is established in xPC target environment based on MATLAB RTW. The experiment results shows that the model reference adaptive control strategy is of higher accuracy and better robustness than the PID control strategy.

scholarly journals Design of L1 Adaptive Controller for Position Control of Permanent Magnet Linear Synchronous Motor (PMLSM)

2018 ◽  
pp. 39-52
Keyword(s):  
Adaptive Control ◽  
Permanent Magnet ◽  
Position Control ◽  
Adaptive Controller ◽  
Servo Motor ◽  
Model Reference ◽  
L1 Adaptive Control ◽  
Asymptotic Tracking ◽  
L1 Adaptive Controller ◽  
Model Reference Adaptive

In the present work, the design of an L1 adaptive controller for position control of a linear servo motor for X-Y table application has been developed. The AC Permanent Magnet Linear Synchronous Servo Motor (PMLSM) is considered. A comparative study between L1 adaptive control and Model Reference Adaptive Control (MRAC) has been made. The effectiveness of the L1 adaptive controller against uncertain parameters is analyzed based on simulated results. Robustness characteristics of both L1 adaptive controller and model reference adaptive controller to different input reference signals and different structures of uncertainty have been evaluated. The L1-adaptive controller could ensure uniformly bounded transient and asymptotic tracking for input and output signals. Simulations based on MATLAB of an x-y table based on PMLSM with time-varying friction and disturbance are presented to verify the theoretical findings. The simulation results within the environment of MATLAB/SIMULINK showed that L1-adaptive controller could give better tracking performance, dynamic and steady-state characteristics, than that obtained from MRAC for considered types of input and for various structures of uncertainties.

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.

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