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.

Identification of continuous-time hybrid “Box–Jenkins” systems with multiple unknown time delays using two-stage parameter estimation algorithm

2019 ◽  
Vol 36 (6) ◽  
pp. 2111-2130
Author(s):  
Yamna Ghoul
Keyword(s):  
Continuous Time ◽  
Time Delays ◽  
Least Square ◽  
Identification Algorithm ◽  
Recursive Least Square ◽  
Two Stage ◽  
Content Type ◽  
Single Output ◽  
Multiple Input ◽  
Unknown Time Delays

Purpose This study/paper aims to present a separable identification algorithm for a multiple input single output (MISO) continuous time (CT) hybrid “Box–Jenkins”. Design/methodology/approach This paper proposes an optimal method for the identification of MISO CT hybrid “Box–Jenkins” systems with unknown time delays by using the two-stage recursive least-square (TS-RLS) identification algorithm. Findings The effectiveness of the proposed scheme is shown with application to a simulation example. Originality/value A two-stage recursive least-square identification method is developed for multiple input single output continuous time hybrid “Box–Jenkins” system with multiple unknown time delays from sampled data. The proposed technique allows the division of the global CT hybrid “Box–Jenkins” system into two fictitious subsystems: the first one contains the parameters of the system model, including the multiple unknown time delays, and the second contains the parameters of the noise model. Then the TS-RLS identification algorithm can be applied easily to estimate all the parameters of the studied system.

Model Learning in a Multistage Machining Process: Online Identification of Force Coefficients and Model Use in the Manufacturing Enterprise

2013 ◽  
Author(s):  
Parikshit Mehta ◽  
Laine Mears
Keyword(s):  
Process Control ◽  
Least Square ◽  
Machining Process ◽  
Force Model ◽  
Recursive Least Square ◽  
Force Coefficient ◽  
Model Learning ◽  
Force Coefficients ◽  
Control Objective ◽  
And Control

This work presents a systems approach in machining process control. Traditional force-based machining process control has been focused on single machine-single operation. The force or power sensor is used to measure the instantaneous force/power, and control action is taken by changing the feedrate in real time to follow a given force setpoint. The application of such control has successfully been implemented to prevent chatter and to elongate tool life by minimizing tool wear. This research seeks to extend the application of control algorithms to learn about the machining system (comprised in this context of a workpiece being operated on in progressive machining), and how knowledge generated by the process can be passed on to the next process for optimization. To demonstrate this, turning of a partially hardened bar is explored. A nonlinear mechanistic force model-based control framework attempts to control the cutting force at a designated setpoint, with material properties changing over the cut. The force coefficients for the material are calculated offline using experimental data and Bayesian inference methods. Since the hardened part of the bar will shift the force coefficient values, an online estimation strategy (Bayesian Recursive Least Square estimator) is used to learn the new coefficients as well as satisfying the control objective. With the newly learned coefficients passed downstream, the subsequent operation experiences no compromise of control objective as well reduces the maximum values of force encountered. Numerical analyses presented show the adaptation and control scheme performance.

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.

scholarly journals Dynamic Model and Inverse Kinematic Identification of a 3-DOF Manipulator Using RLSPSO

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 416 ◽  
Author(s):  
Josias Batista ◽  
Darielson Souza ◽  
Laurinda dos Reis ◽  
Antônio Barbosa ◽  
Rui Araújo
Keyword(s):  
Dynamic Model ◽  
Inverse Kinematics ◽  
Lagrange Equation ◽  
Computational Cost ◽  
Dynamic Parameter ◽  
Search Space ◽  
Least Square ◽  
Identification Accuracy ◽  
Identification Algorithm ◽  
Recursive Least Square

This paper presents the identification of the inverse kinematics of a cylindrical manipulator using identification techniques of Least Squares (LS), Recursive Least Square (RLS), and a dynamic parameter identification algorithm based on Particle Swarm Optimization (PSO) with search space defined by RLS (RLSPSO). A helical trajectory in the cartesian space is used as input. The dynamic model is found through the Lagrange equation and the motion equations, which are used to calculate the torque values of each joint. The torques are calculated from the values of the inverse kinematics, identified by each algorithm and from the manipulator joint speeds and accelerations. The results obtained for the trajectories, speeds, accelerations, and torques of each joint are compared for each algorithm. The computational costs as well as the Multi-Correlation Coefficient ( R 2 ) are computed. The results demonstrated that the identification accuracy of RLSPSO is better than that of LS and PSO. This paper brings an improvement in RLS because it is a method with high complexity, so the proposed method (hybrid) aims to improve the computational cost and the results of the classic RLS.

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.

scholarly journals Online Parameter Identification of Lithium-Ion Batteries Using a Novel Multiple Forgetting Factor Recursive Least Square Algorithm

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3180 ◽  
Author(s):  
Bizhong Xia ◽  
Rui Huang ◽  
Zizhou Lao ◽  
Ruifeng Zhang ◽  
Yongzhi Lai ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Lithium Ion ◽  
Least Square ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Recursive Least Square ◽  
Forgetting Factor ◽  
Temperature State

The model parameters of the lithium-ion battery are of great importance to model-based battery state estimation methods. The fact that parameters change in different rates with operation temperature, state of charge (SOC), state of health (SOH) and other factors calls for an online parameter identification algorithm that can track different dynamic characters of the parameters. In this paper, a novel multiple forgetting factor recursive least square (MFFRLS) algorithm was proposed. Forgetting factors were assigned to each parameter, allowing the algorithm to capture the different dynamics of the parameters. Particle swarm optimization (PSO) was utilized to determine the optimal forgetting factors. A state of the art SOC estimator, known as the unscented Kalman filter (UKF), was combined with the online parameter identification to create an accurate estimation of SOC. The effectiveness of the proposed method was verified through a driving cycle under constant temperature and three different driving cycles under varied temperature. The single forgetting factor recursive least square (SFFRLS)-UKF and UKF with fixed parameter were also tested for comparison. The proposed MFFRLS-UKF method obtained an accurate estimation of SOC especially when the battery was running in an environment of changing temperature.

scholarly journals JOINT DESIGN FOR A HYPER-REDUNDANT SERIAL MANIPULATOR

2011 ◽  
Author(s):  
Mark Alonso ◽  
Mark Stephenson ◽  
Afaan Naqvi
Keyword(s):  
Low Cost ◽  
Open Loop ◽  
Relay System ◽  
Joint Design ◽  
Control Signals ◽  
Power And Control ◽  
Duty Cycles ◽  
Control Scheme ◽  
Helical Coils ◽  
And Control

We propose a joint design for a low-cost, light-weight, hyper-redundant serial manipulator. The proposed design consists of two rigid end plates connected by a universal joint; two sets of agonist/antagonist linear actuator pairs allow independent 2-DOF control. In tethered configuration, power and control signals are delivered to the robot by wire; the proposed control scheme is an open-loop relay system. The actuators consist of approximately helical coils of shape memory alloy wire. Current is alternately directed (at a frequency of 10Hz) through the members of each actuator pair; joint deflection is achieved by varying the respective duty cycles.

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.

Accurate estimation of modern power system harmonics using a novel LSA hybridized recursive least square technique

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ashok Bhoi ◽  
Ranjan Kumar Mallick ◽  
Gayadhar Panda ◽  
Pravati Nayak
Keyword(s):  
Power System ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Least Square ◽  
Computational Time ◽  
Natural Phenomenon ◽  
Accurate Estimation ◽  
Hybrid Technique ◽  
Recursive Least Square ◽  
Time Data

Abstract This paper purposes a new type of hybrid technique depends on lightning search algorithm (LSA) and recursive least square (RLS) named as LSA-RLS to overcome the harmonic estimation issues in time varying modern power system signals buried with noises. LSA is based on a natural phenomenon of lightning. It consists of three types of projectiles: transition, space and lead projectiles. Transition projectiles create population, space projectiles do the exploration and the lead projectiles do the work of exploitation and find the optimal solution. The basic LSA algorithm is mixed with RLS algorithm in an adaptive way to estimate the states of the harmonic signals. Simulation and validation are made with real time data obtained from a converter fed D.C motor drive. The efficacy of the proposed algorithm is verified by comparing the simulation results of recently reported algorithms such as particle swarm optimization (PSO), differential evolution (DE), bacteria foraging optimization (BFO), gravity search algorithm hybridized recursive least square method (GSA-RLS). It is verified that proposed (LSA-RLS) technique is the best in terms of computational time, convergence, accuracy.

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.

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