Acceleration decoupling control of 6 degrees of freedom electro-hydraulic shaking table

2019 ◽  
Vol 25 (21-22) ◽  
pp. 2758-2768 ◽  
Author(s):  
Guang-feng Guan ◽  
AR Plummer
Keyword(s):  
Degrees Of Freedom ◽  
Inverse Dynamics ◽  
Joint Space ◽  
Decoupling Control ◽  
Shaking Table ◽  
Tracking Performance ◽  
Actuator Dynamics ◽  
Stability Margins ◽  
Seismic Testing ◽  
Acceleration Tracking

Electro-hydraulic shaking tables are widely used for vibration testing where high force and displacement amplitudes are required. In particular, they are a vital tool in seismic testing, enabling the development of buildings and other structures which are earthquake resistant. Three-variable-control (TVC) is commonly used for the control of multi-degrees of freedom (DOFs) electro-hydraulic shaking tables. However, the coupling between the DOFs is often significant and is not compensated by TVC. In this paper, an acceleration decoupling control (ADC) method is presented for a 6 DOFs electro-hydraulic shaking table system to improve the acceleration tracking performance and decouple the motion in task space. The gravitational, Coriolis, and centripetal forces are compensated for in joint space based on a dynamic model of the shaking table. Modal control is used to transform the coupled dynamics into six independent systems. Inverse dynamics models are used to cancel the differences in actuator dynamics. The proportional gains in modal space are tuned heuristically to give sufficient stability margins to provide robustness in the presence of modeling errors. The input filter and feedforward controller in TVC are added to improve the acceleration tracking performance of each independent system. Experimental acceleration frequency responses are used to demonstrate the effectiveness of ADC, and in particular these show a consistent reduction in cross-axis coupling compared to TVC. Moreover, only four parameters need to be tuned, as opposed to 36 for TVC, and the method provides a viable route to improving the accuracy of seismic testing in the future.

Decentralized LPV Gain-Scheduled PD Control of a Robotic Manipulator

2009 ◽  
Author(s):  
H. Abbas ◽  
S. M. Hashemi ◽  
H. Werner
Keyword(s):  
Degrees Of Freedom ◽  
Inverse Dynamics ◽  
Controller Design ◽  
Robotic Manipulator ◽  
Low Complexity ◽  
Torque Control ◽  
Tracking Performance ◽  
Pd Controller ◽  
Model Based ◽  
Gain Scheduled

In this paper, low-complexity linear parameter-varying (LPV) modeling and control of a two-degrees-of-freedom robotic manipulator is considered. A quasi-LPV model is derived and simplified in order to facilitate LPV controller synthesis. An LPV gain-scheduled, decentralized PD controller in linear fractional transformation form is designed, using mixed sensitivity loop shaping to take — in addition to high tracking performance — noise and disturbance rejection into account, which are not considered in model-based inverse dynamics or computed torque control schemes. The controller design is based on the existence of a parameter-dependent Lyapunov function — employing the concept of quadratic separators — thus reducing the conservatism of design. The resulting bilinear matrix inequality (BMI) problem is solved using a hybrid gradient-LMI technique. Experimental results illustrate that the LPV controller clearly outperforms a decentralized LTI-PD controller and achieves almost the same accuracy as a model-based inverse dynamics and a full-order LPV controllers in terms of tracking performance while being of significantly lower complexity.

Closed loop trajectory control of walking machines

Robotica ◽  
1990 ◽  
Vol 8 (1) ◽  
pp. 13-22 ◽  
Author(s):  
J. F. Gardner ◽  
K. Srinivasan ◽  
K. J. Waldron
Keyword(s):  
Degrees Of Freedom ◽  
Controller Design ◽  
Coupling Effect ◽  
Legged Locomotion ◽  
Joint Space ◽  
Trajectory Control ◽  
Coordinate Frame ◽  
Actuator Dynamics ◽  
Control Loops ◽  
Walking Machines

SUMMARYThe global trajectory control of walking machines is addressed here with particular attention paid to the consequences of actuator redundancy for control and to the inclusion of actuator dynamics in trajectory controller design. Redundancy of actuation, typical of walking machines, results in the trajectory control problem being formulated perforce in a global coordinate frame, instead of the joint space, as in nonredundant manipulators. This lack of one-to-one correspondence between the degrees of freedom of motion in the global coordinate frame and the actuators results in coupling between the different trajectory control loops. A mechanism for reducing this coupling effect is proposed here, along with a procedure to take into account approximately the effect of actuator dynamics in designing the trajectory controllers. The proposed methods are evaluated by simulation for an example problem in legged locomotion and are shown to be effective.

A learning-based control framework for cable-driven parallel robots with unknown Jacobians

2020 ◽  
Vol 234 (9) ◽  
pp. 1024-1036
Author(s):  
Hao Xiong ◽  
Lin Zhang ◽  
Xiumin Diao
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Degrees Of Freedom ◽  
Inverse Dynamics ◽  
Parallel Robot ◽  
Joint Space ◽  
Parallel Robots ◽  
The Neural Network ◽  
Control Framework ◽  
In Series

Cable-driven parallel robots have been studied by many researchers in the past decades. The Jacobian of a cable-driven parallel robot may not be determined in some applications such as rehabilitation. In order to control the pose of a fully constrained cable-driven parallel robot with unknown Jacobian and driven by torque-controlled actuators, a learning-based control framework consisting of a robust controller and a neural network in series is proposed in this article. The neural network takes over the role of the Jacobian by mapping a wrench applied on the end-effector of the cable-driven parallel robot at a pose in the task space to a set of cable tensions in the joint space. In this way, the cable-driven parallel robot can be controlled by cable tensions derived from such a mapping, rather than solving the inverse dynamics problem based on the Jacobian. As an example, a control strategy is developed to demonstrate how the proposed control framework works. The control strategy includes a proportional–integral–derivative controller and a feedforward neural network. Simulation results show that the control strategy can successfully control a cable-driven parallel robot with four cables, three degrees of freedom, and unknown Jacobian.

scholarly journals Task-space regulation of rigid-link electrically-driven robots with uncertain kinematics using neural networks

2021 ◽  
Vol 54 (1-2) ◽  
pp. 102-115
Author(s):  
Wenhui Si ◽  
Lingyan Zhao ◽  
Jianping Wei ◽  
Zhiguang Guan
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Control Method ◽  
Joint Space ◽  
Robot Kinematics ◽  
Rbf Neural Networks ◽  
Task Space ◽  
Actuator Dynamics ◽  
Rigid Link ◽  
On Line

Extensive research efforts have been made to address the motion control of rigid-link electrically-driven (RLED) robots in literature. However, most existing results were designed in joint space and need to be converted to task space as more and more control tasks are defined in their operational space. In this work, the direct task-space regulation of RLED robots with uncertain kinematics is studied by using neural networks (NN) technique. Radial basis function (RBF) neural networks are used to estimate complicated and calibration heavy robot kinematics and dynamics. The NN weights are updated on-line through two adaptation laws without the necessity of off-line training. Compared with most existing NN-based robot control results, the novelty of the proposed method lies in that asymptotic stability of the overall system can be achieved instead of just uniformly ultimately bounded (UUB) stability. Moreover, the proposed control method can tolerate not only the actuator dynamics uncertainty but also the uncertainty in robot kinematics by adopting an adaptive Jacobian matrix. The asymptotic stability of the overall system is proven rigorously through Lyapunov analysis. Numerical studies have been carried out to verify efficiency of the proposed method.

Effects of Dynamic Model Errors in Task-Priority Operational Space Control

Robotica ◽  
2021 ◽  
pp. 1-12
Author(s):  
Paolo Di Lillo ◽  
Gianluca Antonelli ◽  
Ciro Natale
Keyword(s):  
Inverse Kinematics ◽  
Degrees Of Freedom ◽  
Inverse Dynamics ◽  
Null Space ◽  
Control Algorithms ◽  
Model Errors ◽  
Operational Space ◽  
Dynamic Level ◽  
Concurrent Tasks ◽  
Modeling Errors

SUMMARY Control algorithms of many Degrees-of-Freedom (DOFs) systems based on Inverse Kinematics (IK) or Inverse Dynamics (ID) approaches are two well-known topics of research in robotics. The large number of DOFs allows the design of many concurrent tasks arranged in priorities, that can be solved either at kinematic or dynamic level. This paper investigates the effects of modeling errors in operational space control algorithms with respect to uncertainties affecting knowledge of the dynamic parameters. The effects on the null-space projections and the sources of steady-state errors are investigated. Numerical simulations with on-purpose injected errors are used to validate the thoughts.

Three-dimensional asymmetric maximum weight lifting prediction considering dynamic joint strength

2021 ◽  
pp. 095441192098703
Author(s):  
Rahid Zaman ◽  
Yujiang Xiang ◽  
Jazmin Cruz ◽  
James Yang
Keyword(s):  
Experimental Data ◽  
Degrees Of Freedom ◽  
Inverse Dynamics ◽  
Three Dimensional ◽  
Optimization Method ◽  
Weight Lifting ◽  
Joint Torque ◽  
Maximum Weight ◽  
Angular Velocities ◽  
Asymmetric Lifting

In this study, the three-dimensional (3D) asymmetric maximum weight lifting is predicted using an inverse-dynamics-based optimization method considering dynamic joint torque limits. The dynamic joint torque limits are functions of joint angles and angular velocities, and imposed on the hip, knee, ankle, wrist, elbow, shoulder, and lumbar spine joints. The 3D model has 40 degrees of freedom (DOFs) including 34 physical revolute joints and 6 global joints. A multi-objective optimization (MOO) problem is solved by simultaneously maximizing box weight and minimizing the sum of joint torque squares. A total of 12 male subjects were recruited to conduct maximum weight box lifting using squat-lifting strategy. Finally, the predicted lifting motion, ground reaction forces, and maximum lifting weight are validated with the experimental data. The prediction results agree well with the experimental data and the model’s predictive capability is demonstrated. This is the first study that uses MOO to predict maximum lifting weight and 3D asymmetric lifting motion while considering dynamic joint torque limits. The proposed method has the potential to prevent individuals’ risk of injury for lifting.

On the Inherent Characteristics of the Dynamics of Robot Manipulators

1991 ◽  
Author(s):  
Q. Tu ◽  
J. Rastegar
Keyword(s):  
Path Length ◽  
Degrees Of Freedom ◽  
Inverse Dynamics ◽  
Robot Manipulators ◽  
Control Point ◽  
Coordinate Space ◽  
Relative Significance ◽  
Harmonic Content ◽  
Trajectory Pattern ◽  
Dynamics Models

Abstract The inherent characteristics of the (nonlinear) dynamics of robot manipulators are studied. The study is based on a new method, referred to as the trajectory pattern method. The inverse dynamics models of the manipulator are divided into classes of inverse dynamics models, each corresponding to a different trajectory pattern. For each trajectory pattern, the structure of the resulting inverse dynamics model is fixed and is used to study the characteristics of the dynamics of the manipulator by examining the harmonic content of the required actuation torques (forces) and the relative significance of each harmonic. The harmonic content of the actuating torques is shown to be a function of the path length in the joint coordinate space and the harmonic content of the selected trajectory pattern, but is independent of the number of degrees-of-freedom of the manipulator. The relative contribution of each harmonic is a function of the path length, direction of motion, the position of the path of motion within the workspace of the manipulator, and the magnitude of the fundamental frequency. The study provides a systematic approach to path and trajectory planning from the vibration control point of view. As an example, the characteristics of the dynamics of a spatial 3R manipulator is studied for motions with two different path lengths, starting from a specified point and extending in different directions.

Trajectory Tracking of Underactuated Multibody Systems

2011 ◽  
Author(s):  
Stefan Reichl ◽  
Wolfgang Steiner
Keyword(s):  
Trajectory Tracking ◽  
Multibody Systems ◽  
Degrees Of Freedom ◽  
Inverse Dynamics ◽  
Feedforward Control ◽  
Equations Of Motion ◽  
Three Dimensional ◽  
Differential Algebraic Equations ◽  
State Variables ◽  
Algebraic Equations

This work presents three different approaches in inverse dynamics for the solution of trajectory tracking problems in underactuated multibody systems. Such systems are characterized by less control inputs than degrees of freedom. The first approach uses an extension of the equations of motion by geometric and control constraints. This results in index-five differential-algebraic equations. A projection method is used to reduce the systems index and the resulting equations are solved numerically. The second method is a flatness-based feedforward control design. Input and state variables can be parameterized by the flat outputs and their time derivatives up to a certain order. The third approach uses an optimal control algorithm which is based on the minimization of a cost functional including system outputs and desired trajectory. It has to be distinguished between direct and indirect methods. These specific methods are applied to an underactuated planar crane and a three-dimensional rotary crane.

scholarly journals A three-dimensional musculoskeletal model of the dog

2020 ◽  
Author(s):  
Heiko Stark ◽  
Martin S. Fischer ◽  
Alexander Hunt ◽  
Fletcher Young ◽  
Roger Quinn ◽  
...  
Keyword(s):  
Experimental Data ◽  
Body Size ◽  
Degrees Of Freedom ◽  
Inverse Dynamics ◽  
Musculoskeletal Model ◽  
Muscle Synergy ◽  
Locomotor System ◽  
Wide Range ◽  
Joint Load

AbstractDogs are an interesting object of investigation because of the wide range of body size, body mass, and physique. In the last several years, the number of clinical and biomechanical studies on dog locomotion has increased. However, the relationship between body structure and joint load during locomotion, as well as between joint load and degenerative diseases of the locomotor system (e.g. dysplasia), are not sufficiently understood. In vivo measurements/records of joint forces and loads or deep/small muscles are complex, invasive, and sometimes ethically questionable. The use of detailed musculoskeletal models may help in filling that knowledge gap. We describe here the methods we used to create a detailed musculoskeletal model with 84 degrees of freedom and 134 muscles. Our model has three key-features: Three-dimensionality, scalability, and modularity. We tested the validity of the model by identifying forelimb muscle synergies of a beagle at walk. We used inverse dynamics and static optimization to estimate muscle activations based on experimental data. We identified three muscle synergy groups by using hierarchical clustering. Predicted activation patterns exhibited good agreement with experimental data for most of the forelimb muscles. We expect that our model will speed up the analysis of how body size, physique, agility, and disease influence joint neuronal control and loading in dog locomotion.

scholarly journals Earthquake Ground Motion Matching on a Small Electric Shaking Table Using a Combined NN-PDFF Controller

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Selma H. Larbi ◽  
Nouredine Bourahla ◽  
Hacine Benchoubane ◽  
Khireddine Choutri ◽  
Mohammed Badaoui
Keyword(s):  
Control Function ◽  
Coupling Effect ◽  
Experimental Tests ◽  
Shaking Table ◽  
Earthquake Ground Motion ◽  
Feedback Signal ◽  
Linear Motors ◽  
Acceleration Time Histories ◽  
Time Histories ◽  
Acceleration Tracking

Replicating acceleration time histories with high accuracy on shaking table platforms is still a challenging task. The complex interference between the components of the system, the inherent nonlinearities, and the coupling effect between the specimen and the shaking table are among other reasons that most affect the control performance. In this paper, a neural network- (NN-) based controller has been developed and experimentally implemented to improve the acceleration tracking performance of an electric shaking table. The latter is a biaxial shaking table driven by linear motors and controlled by a proportional-derivative-feedforward (PDFF) controller that is very efficient in reproducing displacement waveforms on the detriment of the simulation of the prescribed acceleration ground motions. In order to bypass this shortcoming, a control scheme combining the PDFF as a basic control function with a NN controller which filters the shaking table feedback signal and acts on the drive signal by compensating for acceleration distortions is proposed in this study. Several experimental tests have been carried out to build a database for offline training, validating, and testing of the proposed NN control model. Subsequently, the well-trained NN is implemented in the inner control loop of the shaking table to compensate, in parallel with the PDFF controller, the distortions during the replication of acceleration signals. Results of tests using earthquake records showed an enhancement in signal matching when integrating the NN model for both bare and loaded conditions of the shaking table. The tracking errors, estimated using the relative root-mean-square error, between the measured and the desired signal, are significantly reduced in time and frequency domains with the additional NN online controller.

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