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>

scholarly journals Finite-Horizon Kinetic Energy Optimization of a Redundant Space Manipulator

2021 ◽  
Vol 11 (5) ◽  
pp. 2346
Author(s):  
Alessandro Tringali ◽  
Silvio Cocuzza
Keyword(s):  
Kinetic Energy ◽  
Real Time ◽  
Inverse Kinematics ◽  
Degrees Of Freedom ◽  
Computational Time ◽  
Redundant Manipulators ◽  
Space Robotics ◽  
Optimal Method ◽  
End Effector ◽  
Global Optimal

The minimization of energy consumption is of the utmost importance in space robotics. For redundant manipulators tracking a desired end-effector trajectory, most of the proposed solutions are based on locally optimal inverse kinematics methods. On the one hand, these methods are suitable for real-time implementation; nevertheless, on the other hand, they often provide solutions quite far from the globally optimal one and, moreover, are prone to singularities. In this paper, a novel inverse kinematics method for redundant manipulators is presented, which overcomes the above mentioned issues and is suitable for real-time implementation. The proposed method is based on the optimization of the kinetic energy integral on a limited subset of future end-effector path points, making the manipulator joints to move in the direction of minimum kinetic energy. The proposed method is tested by simulation of a three degrees of freedom (DOF) planar manipulator in a number of test cases, and its performance is compared to the classical pseudoinverse solution and to a global optimal method. The proposed method outperforms the pseudoinverse-based one and proves to be able to avoid singularities. Furthermore, it provides a solution very close to the global optimal one with a much lower computational time, which is compatible for real-time implementation.

Simulation on Trajectory Planning of 6R Manipulator Based on the Shortest Distance Criterion

2014 ◽  
Vol 602-605 ◽  
pp. 942-945
Author(s):  
Qing Qing Huang ◽  
Guang Feng Chen ◽  
Jiang Hua Li ◽  
Xin Wei
Keyword(s):  
Trajectory Planning ◽  
Inverse Kinematics ◽  
Spline Interpolation ◽  
Algebraic Method ◽  
End Effector ◽  
Cartesian Space ◽  
Shortest Distance ◽  
Inverse Solutions ◽  
Group Inverses ◽  
Forward And Inverse Kinematics

This paper concerns the trajectory planning and simulation for 6R Manipulator. First, algebraic method was used to deduce the forward and inverse kinematics of 6R manipulator. All inverse solutions were expressed in atan2 to eliminate redundant roots to get the corresponding inverse formula. For the trajectory planning of manipulator in Cartesian space, using the cubic spline interpolation to get the drive function of joint, getting a unique solution from eight group inverses by the shortest distance criterion, and then obtained the actual end-effector trajectory. Using Matlab to verify the proposed trajectory planning method, validated results show that the proposed algorithm is feasible and effective.

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.

REAL-TIME BLIND SOURCE SEPARATION OF ACOUSTIC SIGNALS WITH A RECURSIVE APPROACH

2004 ◽  
Vol 04 (02) ◽  
pp. 193-206 ◽  
Author(s):  
SHUXUE DING ◽  
JIE HUANG ◽  
DAMING WEI
Keyword(s):  
Real Time ◽  
Blind Source Separation ◽  
Correlation Matrix ◽  
Lower Cost ◽  
Source Separation ◽  
Least Square ◽  
Gradient Algorithm ◽  
Normal Equation ◽  
Recursive Least Square ◽  
Second Order Statistics

We propose an approach for real-time blind source separation (BSS), in which the observations are linear convolutive mixtures of statistically independent acoustic sources. A recursive least square (RLS)-like strategy is devised for real-time BSS processing. A normal equation is further introduced as an expression between the separation matrix and the correlation matrix of observations. We recursively estimate the correlation matrix and explicitly, rather than stochastically, solve the normal equation to obtain the separation matrix. As an example of application, the approach has been applied to a BSS problem where the separation criterion is based on the second-order statistics and the non-stationarity of signals in the frequency domain. In this way, we realise a novel BSS algorithm, called exponentially weighted recursive BSS algorithm. The simulation and experimental results showed an improved separation and a superior convergence rate of the proposed algorithm over that of the gradient algorithm. Moreover, this algorithm can converge to a much lower cost value than that of the gradient algorithm.

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 Position and Singularity Analysis of a Class of Planar Parallel Manipulators with a Reconfigurable End-Effector

Machines ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 7
Author(s):  
Tommaso Marchi ◽  
Giovanni Mottola ◽  
Josep M. Porta ◽  
Federico Thomas ◽  
Marco Carricato
Keyword(s):  
Inverse Kinematics ◽  
Kinematic Chain ◽  
Experimental Tests ◽  
Parallel Manipulators ◽  
Singularity Analysis ◽  
Parallel Robots ◽  
End Effector ◽  
Revolute Joints ◽  
A Chain ◽  
And Control

Parallel robots with configurable platforms are a class of robots in which the end-effector has an inner mobility, so that its overall shape can be reconfigured: in most cases, the end-effector is thus a closed-loop kinematic chain composed of rigid links. These robots have a greater flexibility in their motion and control with respect to rigid-platform parallel architectures, but their kinematics is more challenging to analyze. In our work, we consider n-RRR planar configurable robots, in which the end-effector is a chain composed of n links and revolute joints, and is controlled by n rotary actuators located on the base of the mechanism. In particular, we study the geometrical design of such robots and their direct and inverse kinematics for n=4, n=5 and n=6; we employ the bilateration method, which can simplify the kinematic analysis and allows us to generalize the approach and the results obtained for the 3-RRR mechanism to n-RRR robots (with n>3). Then, we study the singularity configurations of these robot architectures. Finally, we present the results from experimental tests that have been performed on a 5–RRR robot prototype.

Real-Time Solution of Inverse Kinematics for 6R Robot Manipulators Using Digital Signal Processor

2013 ◽  
Vol 423-426 ◽  
pp. 2788-2791
Author(s):  
Lin Chen ◽  
Hai Hong Pan ◽  
Han Ling Mao
Keyword(s):  
Real Time ◽  
Digital Signal Processor ◽  
Inverse Kinematics ◽  
Robot Manipulators ◽  
Digital Signal ◽  
Central Processor ◽  
Real Time Control ◽  
End Effector ◽  
The Real ◽  
Signal Processor

It is a challenge to get real-time solutions for the inverse kinematics problem of 6R robot. In this study, a digital signal processor (DSP) was adopted as the central processor for the algorithm inverse kinematics. Based on it, the robot end-effector carried out the interpolation of point-to-point spatial straight line, and the inverse kinematics solving of 6R robot manipulators end-effector was achieved. The deflection variations of the 6 joints were acquired during the interpolation in a Cartesian coordinate. The results show that inverse Kinematics solution for each interpolation point only cost 0.06588 millisecond using DSP 6711. This hard structure can ensure the real-time performance of control for robot and content the real-time control performance expected of industrial manipulators.

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