Adaptive Control of Pressure Tracking for Polishing Process

2010 ◽  
Vol 132 (1) ◽  
Author(s):  
Liang Liao ◽  
Fengfeng Jeff Xi ◽  
Kefu Liu
Keyword(s):  
Adaptive Control ◽  
Contact Stress ◽  
Control Method ◽  
Minimum Degree ◽  
Adaptive Controller ◽  
Recursive Least Squares ◽  
Pole Placement ◽  
Polishing Process ◽  
Stress Theory ◽  
Self Tuning

In this paper, an adaptive controller is developed for the pressure tracking of the pressurized toolhead in order to maintain the constant contact stress for the polishing process. This is a new polishing control method, which combines the adaptive control theory and the constant stress theory of the contact model. By using an active pneumatic compliant toolhead, a recursive least-squares estimator is developed to estimate the pneumatic model, and then a minimum-degree pole-placement method is applied to design a self-tuning controller. The simulation and experiment results of the proposed controller are presented and discussed. The main advantage of the constant contact stress control is high figuring accuracy.

Robust Adaptive Control for Hydraulic Servosystems

1996 ◽  
Vol 118 (2) ◽  
pp. 237-244 ◽  
Author(s):  
A. R. Plummer ◽  
N. D. Vaughan
Keyword(s):  
Closed Loop ◽  
Control Method ◽  
Controller Design ◽  
Adaptive Controller ◽  
Robust Adaptive Control ◽  
Recursive Least Squares ◽  
Pole Placement ◽  
Line System ◽  
Self Tuning ◽  
Robust Adaptive

The application of an indirect (self-tuning) adaptive controller to an electro-hydraulic positioning system is described. The underlying control method is pole placement, with the addition of a demand filter to allow noise effects to be reduced without degrading closed-loop performance. Recursive least squares is used to estimate the plant parameters, but the data is pre-filtered to reduce bias. A novel covariance trace limiting algorithm provides estimator reliability despite periods of insufficient excitation. Off-line system identification is employed to help controller design for the electro-hydraulic servosystem. The resulting controller performs well, and adapts rapidly to changes in load stiffness and supply pressure.

Comparison of Adaptive Control Techniques Applied to Diesel Engine Idle Speed Regulation

2002 ◽  
Vol 124 (4) ◽  
pp. 682-688 ◽  
Author(s):  
Douglas W. Memering ◽  
Peter H. Meckl
Keyword(s):  
Diesel Engine ◽  
Adaptive Controller ◽  
Minimum Variance ◽  
Recursive Least Squares ◽  
Pole Placement ◽  
Good Convergence ◽  
Idle Speed ◽  
Engine Model ◽  
Speed Regulation ◽  
Self Tuning

Two self-tuning adaptive algorithms are developed for a heavy-duty diesel engine in order to tune the idle governor to the specific parameters of a given engine. Engine parameters typically vary across engines and over time, thus causing potentially detrimental effects on engine idle speed performance. Self-tuning controllers determine the specific parameters of a given engine, and then adjust the controller algorithm accordingly. Recursive least squares is used to do the parameter identification, whose samples are synchronized with the discrete injection events of the diesel engine for good convergence. Both Minimum Variance and Pole Placement Self-Tuning Regulators are developed and simulated on the nonlinear diesel engine model. The results show successful tuning of each adaptive controller to the specific parameters of a given engine model, with parameter convergence occurring within 30 seconds.

Joint Self-Tuning With Cartesian Setpoints

1986 ◽  
Vol 108 (2) ◽  
pp. 146-150 ◽  
Author(s):  
P. G. Backes ◽  
G. G. Leininger ◽  
Chun-Hsien Chung
Keyword(s):  
Real Time ◽  
Control Method ◽  
Hybrid Control ◽  
Pole Placement ◽  
Real Time Control ◽  
Time Control ◽  
Self Tuning ◽  
Cause Of Error ◽  
Time Position ◽  
Manipulator Control

A joint coordinate self-tuning manipulator control method is presented which uses Cartesian setpoints. The method is capable of both position and hybrid control. Position and force errors are transformed from Cartesian coordinates to position and force errors at the joints. The position and force errors at each joint are combined into one hybrid error that is eliminated using pole-placement self-tuning. Real time position and hybrid control results are given. No prior knowledge of manipulator or load dynamics is required and real time control results show that the goal of consistent control with changing load dynamics is achieved. The major cause of error in position and hybrid control is the large friction effects in the joints.

scholarly journals Deep submergence rescue vehicle docking based on parameter adaptive control with acoustic and visual guidance

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142091995 ◽  
Author(s):  
Yushan Sun ◽  
Xiangrui Ran ◽  
Jian Cao ◽  
Yueming Li
Keyword(s):  
Adaptive Control ◽  
Reinforcement Learning ◽  
Attitude Determination ◽  
Control Method ◽  
Adaptive Controller ◽  
Control Effect ◽  
Visual Guidance ◽  
Docking Method ◽  
Parameter Adaptive

In view of the difficulties in the attitude determination of wrecked submarine and the automatic attitude matching of deep submergence rescue vehicles during the docking and guidance of a submarine rescue vehicle, this study proposes a docking method based on parameter adaptive control with acoustic and visual guidance. This study omits the process of obtaining the information of the wrecked submarine in advance, thus saving considerable detection time and improving rescue efficiency. A parameter adaptive controller based on reinforcement learning is designed. The S-plane and proportional integral derivative controllers are trained through reinforcement learning to obtain the control parameters in the improvement of the environmental adaptability and anti-current ability of deep submarine rescue vehicles. The effectiveness of the proposed method is proved by simulation and pool tests. The comparison experiment shows that the parameter adaptive controller based on reinforcement learning has better control effect, accuracy, and stability than the untrained control method.

Managing driving disturbances in lateral vehicle dynamics via adaptive integrated chassis control

2020 ◽  
pp. 146441932097737
Author(s):  
Narjes Ahmadian ◽  
Alireza Khosravi ◽  
Pouria Sarhadi
Keyword(s):  
Adaptive Control ◽  
Control Method ◽  
Linear Time ◽  
Hierarchical Control ◽  
Control Level ◽  
Adaptive Controller ◽  
Stability Control ◽  
Control Laws ◽  
Mimo Model ◽  
Yaw Moment

This paper presents a vehicle stability control method based on a multi-input multi-output (MIMO) model reference adaptive control (MRAC) strategy as an advanced driver assistance system (ADAS) to enhance the handling and yaw stability of the vehicle lateral dynamics. The corrective yaw moment and additive steering angle are generated using direct yaw moment control (DYC) and active front steering (AFS) at the upper control level in the hierarchical control algorithm. A nonlinear term is added to the conventional adaptive control laws to handle parametric uncertainties and disturbances. The desired yaw moment generated by the upper-level controller is converted to the brake forces and is distributed to the rear wheels by an optimal procedure at the lower-level. The major contribution of this study is the introduction of a nonlinear integrated adaptive control method based on a constraint optimization algorithm. To verify the effectiveness of the proposed control strategy, the nonlinear integrated adaptive controller, and linear time-varying MRAC are designed and used for comparison. Simulation results are performed for the J-turn and double lane change (DLC) manoeuvres at high speeds and low tyre-road friction coefficients. The desired performance of the proposed controller exhibited significant improvement compared to the conventional MRAC in terms of yaw rate tracking and handling of sideslip limitation.

Self-tuning proportional, integral and derivative controller based on genetic algorithm least squares

1998 ◽  
Vol 212 (6) ◽  
pp. 437-448 ◽  
Author(s):  
K Warwick ◽  
Y-H Kang
Keyword(s):  
Least Squares ◽  
Adaptive Controller ◽  
Recursive Least Squares ◽  
Industrial Processes ◽  
Industrial Plant ◽  
Parameter Estimator ◽  
Proportional Integral ◽  
Control Scheme ◽  
Self Tuning ◽  
Real Plant

A self-tuning proportional, integral and derivative control scheme based on genetic algorithms (GAs) is proposed and applied to the control of a real industrial plant. This paper explores the improvement in the parameter estimator, which is an essential part of an adaptive controller, through the hybridization of recursive least-squares algorithms by making use of GAs and the possibility of the application of GAs to the control of industrial processes. Both the simulation results and the experiments on a real plant show that the proposed scheme can be applied effectively.

Lyapunov Based Adaptive Control Method for Hydraulic Servo Drive

2016 ◽  
Author(s):  
Hamid Roozbahani ◽  
Konstantin Frumkin ◽  
Heikki Handroos
Keyword(s):  
Adaptive Control ◽  
Control Algorithm ◽  
Environmental Changes ◽  
Control Method ◽  
Servo System ◽  
Adaptive Controller ◽  
Servo Drive ◽  
Hydraulic Servo ◽  
Hydraulic Servo System ◽  
Adaptive Control Algorithm

Adaptive control systems are one of the most significant research directions of modern control theory. It is well known that every mechanical appliance’s behavior noticeably depends on environmental changes, functioning-mode parameter changes and changes in technical characteristics of internal functional devices. An adaptive controller involved in control process allows reducing an influence of such changes. In spite of this such type of control methods is applied seldom due to specifics of a controller designing. The work presented in this paper shows the design process of the adaptive controller built by Lyapunov’s function method for a hydraulic servo system. The modeling of the hydraulic servo system were conducting with MATLAB® software including Simulink® and Symbolic Math Toolbox™. In this study, the Jacobi matrix linearization of the object’s mathematical model and derivation of the suitable reference models based on Newton’s characteristic polynomial were applied. In addition, an intelligent adaptive control algorithm and system model including its nonlinearities was developed to solve Lyapunov’s equation. Developed algorithm works properly and considered plant is met requirement of functioning with. The results shows that the developed adaptive control algorithm increases system performance in use devices significantly and might be used for correction of system’s behavior and dynamics.

CAD Assisted Adaptive Control for Milling

1991 ◽  
Vol 113 (3) ◽  
pp. 444-450 ◽  
Author(s):  
A. Spence ◽  
Y. Altintas
Keyword(s):  
Adaptive Control ◽  
Control Method ◽  
Adaptive Controller ◽  
Milling Process ◽  
Force Model ◽  
Online Information ◽  
Geometric Information ◽  
Milling Force ◽  
Part Geometry ◽  
Milling Force Model

A milling process adaptive control method, which prevents force overshoots during sudden part geometry changes, has been developed by providing online information to the controller from the part’s CAD representation. A first-order discrete model structure to represent the milling process for adaptive control was analytically developed and experimentally identified. Provided with geometric information obtained from the part’s CAD model, and utilizing the milling force model, the adaptive controller predicts the maximum cutting force expected in advance of dangerous immersion changes. The technique permits the controller to anticipate the changing workpiece in time to eliminate force overshoots which would otherwise break the tool, yet adaptive control at all times remains active to respond to other geometrical and material variations. Simulation and experimental results are presented to confirm the viability of the proposed method.

The Effect of Computational Delay on Performance of Adaptive Control Systems

2006 ◽  
Author(s):  
Pawel Konrad Orzechowski ◽  
Tsu-Chin Tsao ◽  
James Steve Gibson
Keyword(s):  
Adaptive Control ◽  
Least Squares ◽  
Adaptive Filters ◽  
Adaptive Controller ◽  
Recursive Least Squares ◽  
Variable Order ◽  
Stochastic Disturbance ◽  
Disturbance Dynamics ◽  
Fractional Delay ◽  
Computationally Intensive

In many adaptive control applications, especially where the recursive-least-squares (RLS) algorithms are used, the real-time implementation of high order adaptive filters for estimating the disturbance dynamics is computationally intensive. The delay associated with the computational burden is usually either underestimated as no delay or overestimated as one sample delay in the control system design and analysis. For a stochastic disturbance dynamics, the H2 optimal control performance for the case of one-step delay is worse than that of no delay due to the nonminimum phase plant zero introduced by the delay. The optimal performance for a fractional delay is bounded between these two extremes. The paper investigates the effect of the fractional computational delay on a variable order adaptive controller based on a recursive least-squares adaptive lattice filter. The trade-off between the adaptive filter order and the computational delay is analyzed and evaluated by an example.

scholarly journals Adaptive Control of a Four-Dimensional Hyperchaotic System

2019 ◽  
pp. 1-17
Author(s):  
Xuxia Li ◽  
Xinghua Fan ◽  
Jiuli Yin ◽  
Ying Zhang ◽  
Xiangxiang Lv
Keyword(s):  
Adaptive Control ◽  
Feedback Control ◽  
Control Method ◽  
Adaptive Controller ◽  
Linear Feedback ◽  
Hyperchaotic System ◽  
Linear Feedback Control ◽  
Adaptive Law ◽  
Unstable Equilibrium Point ◽  
Lyapunov Exponent Spectrum

This paper concerns the application of adaptive control method in a four-dimensional hyperchaoticsystem. Firstly, we carry out a systematic dynamic analysis including the properties of equilibriumpoint, stability, dissipation, Lyapunov exponent spectrum, and bifurcation. Both the existenceof two positive Lyapunov exponents and the Lyapunov dimension value show the hyperchaotic property of the system. Based on Lyapunov stability theorem, we then construct an adaptive controller and the adaptive law to suppress hyperchaos to the origin, which is an unstable equilibrium point under a certain parameter set. The effectiveness of the adaptive control is veried by theoretical analysis and numerical simulation. We nally brie y demonstrate the control eciency of self-linear feedback control and misaligned feedback control. For the fourdimensional hyperchaotic system, the adaptive control outperforms them from the view of control speed.

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