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.

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.

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.

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.

Least Squares Adaptive Control for Trajectory Following Robots

1987 ◽  
Vol 109 (2) ◽  
pp. 104-110 ◽  
Author(s):  
C. W. deSilva ◽  
J. Van Winssen
Keyword(s):  
Least Squares ◽  
Reference Model ◽  
Recursive Algorithm ◽  
Adaptive Controller ◽  
Nonlinear Damping ◽  
Feedback Gain ◽  
Online Identification ◽  
Control Scheme ◽  
Trajectory Following ◽  
Time Changes

A control scheme is presented for trajectory following with robotic manipulators. The method employs a feedforward torque for gross compensation and adaptive feedback gain scheduling for correcting deviations from the desired trajectory. The adaptive controller eliminates trajectory errors in the least squares sense without using online identification or a reference model. The control scheme takes into account dynamic nonlinearities (e.g., coriolis and centrifugal accelerations and payload changes), geometric nonlinearities (e.g., nonlinear coordinate expressions for large excursions) and physical nonlinearities (e.g., nonlinear damping) as well as dynamic coupling present in a manipulator. The method can accommodate real-time changes in the desired trajectory. In practice, a recursive algorithm would be needed to accomplish this. Computer simulations are given to demonstrate the feasibility of the control scheme.

A neural network enhanced generalized minimum variance self-tuning proportional, integral and derivative control algorithm for complex dynamic systems

2002 ◽  
Vol 216 (3) ◽  
pp. 265-273
Author(s):  
Q M Zhu ◽  
K Warwick
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Controller Design ◽  
Design Procedure ◽  
Minimum Variance ◽  
Recursive Least Squares ◽  
Equivalent Model ◽  
Proportional Integral ◽  
Wide Range ◽  
Self Tuning

A neural network enhanced proportional, integral and derivative (PID) controller is presented that combines the attributes of neural network learning with a generalized minimum-variance self-tuning control (STC) strategy. The neuro PID controller is structured with plant model identification and PID parameter tuning. The plants to be controlled are approximated by an equivalent model composed of a simple linear submodel to approximate plant dynamics around operating points, plus an error agent to accommodate the errors induced by linear submodel inaccuracy due to non-linearities and other complexities. A generalized recursive least-squares algorithm is used to identify the linear submodel, and a layered neural network is used to detect the error agent in which the weights are updated on the basis of the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model, and therefore the error agent is naturally functioned within the control law. In this way the controller can deal not only with a wide range of linear dynamic plants but also with those complex plants characterized by severe non-linearity, uncertainties and non-minimum phase behaviours. Two simulation studies are provided to demonstrate the effectiveness of the controller design procedure.

scholarly journals Self-Tuning Control Scheme Based on the Robustnessσ-Modification Approach

2017 ◽  
Vol 2017 ◽  
pp. 1-13
Author(s):  
Nabiha Touijer ◽  
Samira Kamoun ◽  
Najib Essounbouli ◽  
Abdelaziz Hamzaoui
Keyword(s):  
Recursive Algorithm ◽  
Recursive Least Squares ◽  
Cruise Control ◽  
Rls Algorithm ◽  
Cruise Control System ◽  
Control Scheme ◽  
Estimation Scheme ◽  
Self Tuning ◽  
Small Gain ◽  
The Stability

This paper deals with the self-tuning control problem of linear systems described by autoregressive exogenous (ARX) mathematical models in the presence of unmodelled dynamics. An explicit scheme of control is described, which we use a recursive algorithm on the basis of the robustnessσ-modification approach to estimate the parameters of the system, to solve the problem of regulation tracking of the system. This approach was designed with the assumptions that the norm of the vector of the parameters is well-known. A new quadratic criterion is proposed to develop a modified recursive least squares (M-RLS) algorithm withσ-modification. The stability condition of the proposed estimation scheme is proved using the concepts of the small gain theorem. The effectiveness and reliability of the proposed M-RLS algorithm are shown by an illustrative simulation example. The effectiveness of the described explicit self-tuning control scheme is demonstrated by simulation results of the cruise control system for a vehicle.

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