Resolved Motion Adaptive Control for Mechanical Manipulators

1984 ◽  
Vol 106 (2) ◽  
pp. 134-142 ◽  
Author(s):  
C. S. G. Lee ◽  
B. H. Lee
Keyword(s):  
Adaptive Control ◽  
Low Cost ◽  
Equations Of Motion ◽  
Least Square ◽  
Hand Position ◽  
Recursive Least Square ◽  
Joint Torques ◽  
Hand Trajectory ◽  
Self Tuning ◽  
Mechanical Manipulators

This paper presents the development of a resolved motion adaptive control which adopts the ideas of “resolved motion rate control” [8] and “resolved motion acceleration control” [10] to control a manipulator in Cartesian coordinates for various loading conditions. The proposed adaptive control is performed at the hand level and is based on the linearized perturbation system along a desired hand trajectory. The controlled system is characterized by feedforward and feedback components which can be computed separately and simultaneously. The feedforward component resolves the specified positions, velocities, and accelerations of the hand into a set of values of joint positions, velocities, and accelerations from which the nominal joint torques are computed using the Newton-Euler equations of motion to compensate all the interaction forces among the various joints. The feedback component consisting of recursive least square identification scheme and an optimal adaptive self-tuning controller for the linearized system computes the perturbation torques which reduce the manipulator hand position and velocity errors along the nominal hand trajectory. The feasibility of implementing the proposed adaptive control using present day low-cost microprocessors is explored.

EVALUATION OF DISCRETE ADAPTIVE FAIL-SAFE ACTIVE SUSPENSION

1997 ◽  
Vol 21 (3) ◽  
pp. 205-220 ◽  
Author(s):  
R.V. Dukkipati ◽  
S.S. Vallurupalli ◽  
M.O.M. Osman
Keyword(s):  
Adaptive Control ◽  
Active Suspension ◽  
Full Scale ◽  
Least Square ◽  
Recursive Least Square ◽  
Single Degree Of Freedom ◽  
Computer Controlled ◽  
Fail Safe ◽  
Model Reference Adaptive ◽  
First Time

Hardware implementation of discrete adaptive control for a full scale vehicular single degree of freedom (SDOF) active suspension has been discussed in this paper. This paper describes an experimental evaluation of full scale fail-safe adaptive active (SDOF) suspension system that has been performed for the first time. A servo hydraulic force actuator is installed along with passive suspension components to form a fail-safe active suspension. A discrete model reference adaptive control (DMRAC) approach with recursive least square (RLS) estimation and covariance modification has been used for the software/hardware based digital control. A real time computer controlled adaptive active suspension software which shows the experimental response and animation of the results has been developed.

Non-Linear Complementary SO(3) Filters for Attitude Estimation and Navigation of Ground Vehicles

2014 ◽  
Vol 663 ◽  
pp. 254-258
Author(s):  
Fargham Sandhu ◽  
Hazlina Selamat ◽  
Yahaya Md Sam
Keyword(s):  
Low Cost ◽  
Attitude Estimation ◽  
Least Square ◽  
Low Grade ◽  
Ground Vehicles ◽  
Recursive Least Square ◽  
Stability And Control ◽  
Filter Approach ◽  
Navigational System ◽  
And Control

The use of Inertial Navigational System (INS) has been proven to be suitable for vehicular stability and control. The same system can be used for inertial based navigation in the absence of GPS. In this paper, the problem of obtaining good attitude estimates from low cost sensors used for car navigation in the absence of GPS data is discussed. The states to be estimated are using angular velocity and linear accleration signals obtained from the sets of gyros and accelerometers of the INS. The special orthogonal group, the SO(3)-based complementary filters, have been used as the estimators as they are most suited for embedded systems to generate highly efficient algorithms for navigation. The INS has also been integrated with a set of magnetometers to assist in achieving global navigation. This integration requires kinematics equations as well as the inclusion of the gyro and accelerometer calibration and filtering. By using the quatronion representation, not only highly compact algorithms for integration can be generated, but it can also estimate and remove the effects of other biases and misalignments caused by, for instance, inaccurate installations and inherent sensors problems. The results obtained through simulation indicate better performance then Kalman filter approach as well as iterative recursive least square approach even with low grade sensors. The results are comparable with attitude estimation using roll index but with much less computations and better performance.

Simulation and Research for Generalized Predictive Control

2013 ◽  
Vol 694-697 ◽  
pp. 2205-2210
Author(s):  
Xiao Li Yu
Keyword(s):  
Predictive Control ◽  
Least Square ◽  
The Self ◽  
Dynamic Responses ◽  
Generalized Predictive Control ◽  
Model Parameters ◽  
Recursive Least Square ◽  
Input Signals ◽  
Self Tuning ◽  
Time Invariant

This paper presents analysis and experiments for Generalized Predictive Control (GPC) algorithm based on software simulation. First, we illustrate the time invariant GPC algorithm in detail. Then, we describe the principle for the control parameter selection of GPC based on empirical results. The Recursive Least Square (RLS) algorithm will be used to identify model parameters in the self-tuning GPC. The performance of GPC algorithm is validated by simulation results, which show that the algorithm has rapid and accurate dynamic responses for input signals, such as step signal and square wave. When the model parameters are unknown, with the assistance of RLS, the self-tuning GPC algorithm also presents good performance and robustness capability, even when white Gaussian noise exists.

Application of an Adaptive Forecast Algorithm to the River Västerdalälven

1988 ◽  
Vol 19 (5) ◽  
pp. 293-302 ◽  
Author(s):  
László Iritz
Keyword(s):  
Parameter Estimation ◽  
Systems Theory ◽  
Least Square ◽  
The Self ◽  
Type Model ◽  
Recursive Least Square ◽  
Electronic Engineering ◽  
Self Tuning ◽  
Recursive Least Square Algorithm ◽  
Hydrological Forecast

During the last two decades, advances in electronic engineering, hydrological modelling and systems theory have given considerable benefits to the hydrological forecast developments. Today several powerful adaptive techniques are available, which can improve the reliability of hydrological forecasting. One of these techniques is the self-tuning predictor based on an ARMA type model using direct parameter estimation by recursive least square algorithm. The selftuning predictor has been tested on the River Västerdalälven in Sweden.

A Simulation Study on a Self-Tuning Portable Controller of Blood Glucose

1993 ◽  
Vol 16 (1) ◽  
pp. 51-57 ◽  
Author(s):  
P. Brunetti ◽  
C. Cobelli ◽  
P. Cruciani ◽  
P.G. Fabietti ◽  
F. Filippucci ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Blood Glucose ◽  
Simulation Study ◽  
Blood Glucose Control ◽  
Minimum Variance ◽  
Least Square ◽  
Recursive Least Square ◽  
Nonlinear Controller ◽  
Self Tuning ◽  
Patient Responses

A self-tuning, nonlinear controller was developed to drive portable or implantable micro-pumps for blood glucose control in diabetic subjects. The parameter estimation is based on a recursive least-square algorithm applied to a discrete time simplified mathematical model of the glucose system; the controller, which is integrated with the estimator, uses an-extended minimum-variance method. The software was designed to drive a micro-controller wearable unit, operating with different kinds of glucose sensors, in order to perform chemical and biological experimentations. Some strategies were introduced to avoid hypoglycaemia, even tolerating a reduction in control speed and accuracy. The whole system was tested in a simulation study, performed on a mathematical model implemented on a personal computer. The tests were performed while simulating different controller structures and settings, and patient responses. They showed a satisfactory control behaviour, mostly as far as stability and robustness are concerned, in all simulated conditions.

Real-time implementation of self-tuning regulator control technique for coupled tank industrial process system

2018 ◽  
Vol 232 (8) ◽  
pp. 1039-1052 ◽  
Author(s):  
Kagan Koray Ayten ◽  
Ahmet Dumlu ◽  
Aliriza Kaleli
Keyword(s):  
Real Time ◽  
Process Model ◽  
Sliding Mode ◽  
Industrial Process ◽  
Least Square ◽  
The Self ◽  
Control Technique ◽  
Recursive Least Square ◽  
Parameter Variations ◽  
Self Tuning

This article presents the self-tuning regulator control technique for a coupled tank liquid level system that often used in industry. An autoregressive with exogenous model has been used as the liquid process model with the self-tuning control implementation in order to track the desired tank level trajectories with disturbances and uncertainties of the system dynamics. The designed self-tuning controller has been sensitive to parameter variations of the nonlinear coupled tank system. The parameters of the proposed controller are periodically updated themselves during the process by means of online recursive least square method with the forgetting factor algorithm. In this way, the parameter variations and unwanted disturbances of the system are eliminated in real-time application. In order to demonstrate the efficiency of the self-tuning regulator control technique, the real-time studies have been executed. The obtained experimental results demonstrated that the proposed controller gives the better trajectory tracking performance and smaller magnitude in overshot and undershot than the designed classical proportional–integral and sliding mode controllers.

An Implicit Quadratic H∞ Self-Tuning Controller

1991 ◽  
Vol 113 (4) ◽  
pp. 729-735 ◽  
Author(s):  
R. A. Hashim ◽  
M. J. Grimble
Keyword(s):  
Diophantine Equations ◽  
Simple Procedure ◽  
Optimal Solution ◽  
Least Square ◽  
Identification Algorithm ◽  
Recursive Least Square ◽  
Control Signals ◽  
Control Scheme ◽  
Self Tuning ◽  
And Control

An implicit H∞ self-tuning control scheme is presented. Costing of the system error and control signals is achieved using a dynamic cost function. The H∞ optimal solution to this problem is obtained using a recursive least square identification algorithm. The simple procedure for calculating the controller, without solving any diophantine equations, make this method particularly suitable for self-tuning control applications.

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