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.

scholarly journals Dynamic Parameter Identification of a Lower Extremity Exoskeleton Using RLS-PSO

2019 ◽  
Vol 9 (2) ◽  
pp. 324 ◽  
Author(s):  
Fusheng Zha ◽  
Wentao Sheng ◽  
Wei Guo ◽  
Shiyin Qiu ◽  
Jing Deng ◽  
...  
Keyword(s):  
Lower Extremity ◽  
Parameter Identification ◽  
Dynamic Parameter ◽  
Search Space ◽  
Least Square ◽  
Identification Accuracy ◽  
Identification Algorithm ◽  
Recursive Least Square ◽  
Experimental Conditions ◽  
Dynamic Parameter Identification

The lower extremity exoskeleton is a device for auxiliary assistance of human movement. The interaction performance between the exoskeleton and the human is determined by the lower extremity exoskeleton’s controller. The performance of the controller is affected by the accuracy of the dynamic equation. Therefore, it is necessary to study the dynamic parameter identification of lower extremity exoskeleton. The existing dynamic parameter identification algorithms for lower extremity exoskeletons are generally based on Least Square (LS). There are some internal drawbacks, such as complicated experimental processes and low identification accuracy. A dynamic parameter identification algorithm based on Particle Swarm Optimization (PSO) with search space defined by Recursive Least Square (RLS) is developed in this investigation. The developed algorithm is named RLS-PSO. By defining the search space of PSO, RLS-PSO not only avoids the convergence of identified parameters to the local minima, but also improves the identification accuracy of exoskeleton dynamic parameters. Under the same experimental conditions, the identification accuracy of RLS-PSO, PSO and LS was quantitatively compared and analyzed. The results demonstrated that the identification accuracy of RLS-PSO is higher than that of LS and PSO.

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.

scholarly journals A Steering-Following Dynamic Model with Driver’s NMS Characteristic for Human-Vehicle Shared Control

2020 ◽  
Vol 10 (7) ◽  
pp. 2626 ◽  
Author(s):  
Hanbing Wei ◽  
Yanhong Wu ◽  
Xing Chen ◽  
Jin Xu
Keyword(s):  
Dynamic Model ◽  
Unscented Kalman Filter ◽  
Autonomous Vehicle ◽  
Least Square ◽  
Experimental Results ◽  
Shared Control ◽  
Dynamic State ◽  
Dynamic Mode ◽  
Identification Algorithm ◽  
Control Authority

For investigating driver characteristic as well as control authority allocation during the process of human–vehicle shared control (HVSC) for an autonomous vehicle (AV), a HVSC dynamic mode with a driver’s neuromuscular (NMS) state parameters was proposed in this paper. It takes into account the driver’s NMS characteristics such as stretch reflection and reflex stiffness. By designing a model predictive control (MPC) controller, the vehicle’s state feedback and driver’s state are incorporated to construct the HVSC dynamic model. For the validation of the model, a field experiment was conducted. The vehicle state signals are collected by V-BOX, and the driver’s state signals are obtained with the electromyography instrument. Subsequently, the hierarchical least square (HLS) parameter identification algorithm was implemented to identify the parameters of the model based on the experimental results. Moreover, the Unscented Kalman Filter (UKF) was utilized to estimate the important NMS parameters which cannot be measured directly. The experimental results showed that the model we proposed has excellent accuracy in characterizing the vehicle’s dynamic state and estimating the driver’s NMS parameter. This paper will serve as a theoretical basis for the new control strategy allocation between human and vehicle for L3 class AVs.

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.

IMPROVING THE EFFICIENCY OF A LEAST MEDIAN OF SQUARES SCHEMA FOR THE ESTIMATION OF THE FUNDAMENTAL MATRIX

2006 ◽  
Vol 20 (05) ◽  
pp. 633-648 ◽  
Author(s):  
MARIA TRUJILLO ◽  
EBROUL IZQUIERDO
Keyword(s):  
Fundamental Matrix ◽  
Input Data ◽  
Selection Process ◽  
Computational Cost ◽  
Computer Experiments ◽  
Search Space ◽  
Least Square ◽  
Practical Applications ◽  
Least Median Of Squares ◽  
Low Dimensionality

A robust and efficient approach to estimate the fundamental matrix is proposed. The main goal is to reduce the computational cost involved in the estimation when robust schemas are applied. The backbone of the proposed technique is the conventional Least Median of Squares (LMedS) technique. It is well known that the LMedS is one of the most robust regressors for highly contaminated data and unstable models. Unfortunately, its computational complexity renders it useless for practical applications. To overcome this problem, a small number of low-dimensionality least-square problems are solved using well-selected subsets from the input data. The results of this initial approach are fed into the LMedS schema, which is applied to recover the final estimation of the Fundamental matrix. The complexity is substantially reduced by applying a selection process based on an effective statistical analysis of the inherent correlation of the input data. This analysis is used to define a suitable clustering of the data and to drive the subset selection aiming at the reduction of the search space in the LMedS schema. It is shown that avoiding redundancies better estimates can be obtained while keeping the computational cost low. Selected results of computer experiments were conducted to assess the performance of the proposed technique.

scholarly journals A dynamic parameter identification method of industrial robots considering joint elasticity

2019 ◽  
Vol 16 (1) ◽  
pp. 172988141882521 ◽  
Author(s):  
Hepeng Ni ◽  
Chengrui Zhang ◽  
Tianliang Hu ◽  
Teng Wang ◽  
Qizhi Chen ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Dynamic Model ◽  
Joint Stiffness ◽  
Dynamic Parameter ◽  
Industrial Robots ◽  
Identification Accuracy ◽  
Processing Algorithm ◽  
Identification Method ◽  
Joint Elasticity ◽  
Dynamic Parameter Identification

Considering the joint elasticity, a novel dynamic parameter identification method is proposed for general industrial robots only with motor encoders. Firstly, the unknown parameters of the elastic joint dynamic model are analyzed and divided into two types. The first type is the motion-independent parameter only including the joint stiffness, which can be identified by the static force/torque-deformation experiments without the dynamic model. The second type is the motion-dependent parameter composed of the rest of the parameters, which needs the dynamic excitation experiments. Therefore, these two types of parameters can be identified separately. Meanwhile, it is found that the rotor inertia parameters can be obtained from the manufacturer, which reduces the identification difficulty of other parameters. After obtaining the rotor inertia and joint stiffness, an approximate processing algorithm is proposed considering the motor friction to establish the linear identification model of other parameters. Hence, the least squares can be employed to identify the parameters, and the independence of the inertia and joint viscous friction parameters are not affected. Meanwhile, the exciting trajectories can be optimized throughout the robot workspace, which reduces the effect of measurement noise on identification accuracy. With the proposed separated identification strategy and approximate processing algorithm, the dynamic parameters can be obtained precisely without double encoders on each joint. Finally, a series of simulations are conducted to evaluate the good performance of the proposed method.

scholarly journals Recursive least square and control for PUMA robotics

2021 ◽  
Vol 21 (2) ◽  
pp. 1238
Author(s):  
Lafta E. Jumaa Alkurawy
Keyword(s):  
Real Time ◽  
Inverse Kinematics ◽  
Algebraic Method ◽  
Pi Controller ◽  
Least Square ◽  
Recursive Least Square ◽  
End Effector ◽  
And Control ◽  
Reference Input

<p>The solution of inverse kinematics system based on recursive least square (RLS) theorem is improved this paper. The task in joints of robotics is inverse kinematics for PUMA robotics. The design the manipulator of robotics is not simple if due to model of algebraic method. I suggested a method of RLS method to get predicts the positions of robot and it is comfortable the applications in real-time.<strong> </strong>The RLS is used to find the solution of the inverse kinematics for the joints 6-dof of the robotics. This technique is important to compute the joints of each arm space with Cartesian axes in the end-effector. The identification will be in each joint for PUMA by RLS and applied PI controller on each joint to get the response follows the reference input by tuning the values of coefficients of PI.</p>

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.

Non-Parametric Identification of Structural Nonlinearity with Limited Input and Output Measurements

2011 ◽  
Vol 243-249 ◽  
pp. 5403-5407 ◽  
Author(s):  
Ying Lei ◽  
Yan Wu
Keyword(s):  
Parametric Identification ◽  
Least Square ◽  
Frame Structure ◽  
Identification Algorithm ◽  
Recursive Least Square ◽  
Restoring Force ◽  
Input And Output ◽  
Structural Nonlinearity ◽  
Shear Frame ◽  
Non Parametric

In this paper, a technique is proposed for non-parametric identification of structural nonlinearity with limited input and output measurements. The identification algorithm is based on the classical Kalman estimator for the displacement and the velocity responses and the recursive least square estimation for the unmeasured excitation and the restoring force. Two different models are used to simulate nonlinear structures: One is a 4-storey shear-frame structure with excitation on the top floor and the nonlinearity occurs at the bottom floor. The other is also a 4-storey shear-frame structure with both excitation and the nonlinearity at the top floor. Two numerical examples are carried out for the two kinds of models. Bouc-Wen hysteretic models are used to simulate the nonlinear impact. The simulation results demonstrate the efficiency of the proposed technique with limited output measurements.

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