Dynamic parameter identification of actuation redundant parallel robots using their power identification model: Application to the DualV

Offline robot dynamic identification methods are based on the use of the inverse dynamic identification model (IDIM), which calculates the joint forces/torques (estimated as the product of the known control signal (the input reference of the motor current loop) with the joint drive gains) that are linear in relation to the dynamic parameters, and on the use of the linear least squares technique to calculate the parameters (IDIM-LS technique). However, as actuation-redundant parallel robots are overactuated, their IDIM has an infinity of solution for the force/torque prediction, depending on the value of the desired overconstraint that is a priori unknown in the identification process. As a result, the IDIM cannot be used as it is for such a class of parallel robots. This paper proposes a procedure for the dynamic parameter identification of actuation-redundant parallel robots. The procedure takes advantage of two possible modified formulations for the IDIM of actuation-redundant robots that can be used for identification purpose. The modified IDIM formulations project some or all input torques/forces onto the robot bodies, thus leading to a unique solution of the model that can then be used in the identification process. A systematic and straightforward way to compute these modified IDIM is presented. The identification of the inertial parameters of a planar parallel robot with actuation redundancy, the DualV, is then carried out using these modified IDIM. Experimental results show the validity of the methods.

This paper deals with a new iterative online dynamic identification method of robot. The robot is closed-loop controlled with a computed torque control (CTC) law that linearizes and decouples the non-linear and coupled dynamics of the robot. Usually, the parameters of the Inverse Dynamic Model (IDM), which calculates the CTC, are identified with an off-line identification method which uses the Inverse Dynamic Identification Model (IDIM) to calculate the joint force/torque as a linear relation in the dynamic parameters. This allows using linear least-squares techniques to estimate the parameters that minimize the 2-norm error between the actual joint force/torque and the calculated joint force/torque (IDIM-LS method). This method requires well tuned band-pass filtering of the joint position to estimate the joint velocity and acceleration and cannot take into account variations of the parameters (friction, payload) in CTC. The new method overcomes these 2 drawbacks. The IDIM-LS solution is periodically calculated over a moving time window to update the IDM of the CTC, and the IDIM is calculated with the noise-free data of the trajectory generator, which avoids using the noisy derivatives of the actual joint position. This method called IDIM-ILIC, works like an Iterative Learning Identification and Control procedure. An experimental setup on a prismatic joint validates the procedure with stationary parameters and with a variation of the payload.

Off-line dynamic identification requires the use of a model linear in relation to the robot dynamic parameters and the use of linear least squares technique to calculate the parameters. Most of time, the used model is the Inverse Dynamic Identification Model (IDIM). However, the computation of its symbolic expressions is extremely tedious. In order to simplify the procedure, the use of the Power Identification Model (PIM), which is dramatically simpler to obtain and that contains exactly the same dynamic parameters as the IDIM, was previously proposed. However, even if the identification of the PIM parameters for a 2 degrees-of-freedom (DOF) planar serial robot was successful, its fails to work for 6 DOF industrial robots. This paper discloses the reasons of this failure and presents a methodology for the identification of the robot dynamic parameters using the PIM. The method is experimentally validated on an industrial 6 DOF Stäubli TX-40 robot.

This paper deals with a new iterative learning dynamic identification method of robot controlled with a Computed Torque Control (CTC) law. The parameters of the Inverse Dynamic Model (IDM) used to compute the CTC, are periodically calculated to minimize the quadratic error between the actual joint force/torque and a joint force/torque calculated with the Inverse Dynamic Identification Model (IDIM), linear in relation to the parameters. Usually the parameters are estimated off-line and the IDIM is calculated with the joint position and its noisy derivatives and cannot take into account on line variations of the parameters (IDIM-LS method). The new method called IDIM-ILIC (IDIM with Iterative Learning Identification and Control) overcomes these 2 drawbacks. The parameters are periodically calculated over a moving time window to update the IDM of the CTC, and the IDIM is calculated with the noise-free data of the trajectory generator, which avoids using the noisy derivatives of the actual joint position. An experimental setup on a prismatic joint validates the procedure with stationary parameters and with a variation of the payload.

This paper deals with two robot identification methods recently introduced. The first one is based on the use of the Inverse Dynamic Identification Model (IDIM) and the Instrumental Variable (IV). The second one is the Direct and Inverse Dynamic Identification Models (DIDIM) method, which is a closed-loop output error method minimizing the quadratic error between the actual and simulated joint torques. Both methods rely on the simulation of the Direct Dynamic Model (DDM). They are compared with a six degrees of freedom industrial robot. The experimental results show that the DIDIM method has the advantage of requiring less data preprocessing. Nevertheless, the IDIM-IV method appears to be more robust to modelling errors in the simulation which are not located in the identified dynamic model.

This brief deals with the identification of industrial robots. The usual identification method is based on the use of the inverse dynamic identification model and the least-squares technique. Another approach is the direct and inverse dynamic identification models (DIDIM) method, which is a closed-loop output error (CLOE) method minimizing the quadratic error between the actual and simulated joint torques. In this brief, the DIDIM method is compared with the usual CLOE method, which minimizes the quadratic error between the actual and simulated joint positions. The comparison is carried out on six degrees of freedom industrial robot. The experimental results show that the DIDIM method needs three iterations to identify 60 parameters while the usual CLOE method needs 20 iterations.

An automated instrumental variable method for rigid industrial robot identification

In this article, an orthogonal 6-degree-of-freedom (DOF) parallel robot with redundant actuation is studied as an earthquake motion simulator. Taking the practical simulation of earthquake waves into consideration, the general characteristics of natural earthquakes are analysed and complexity and variety of seismic waves, three-dimensional and multi-DOF movement, and strong devastating force are regarded as the three obvious features in this article. Based on the characteristics of this orthogonal 6-DOF parallel robot with redundant actuation and the features of earthquakes, the feasibility of using this parallel robot as an earthquake motion simulator is analysed from three aspects: orthogonal 6-DOF structure, decoupling feature, and redundant actuation module. In order to simulate an earthquake motion using this parallel robot, its inverse kinematics and dynamics models are derived. The control system of this earthquake simulator is developed based on the PXIbus development platform. The computed-torque control algorithm based on the inverse dynamics is used in the controller of this equipment. A typical three-directional earthquake motion, the El Centro earthquake, is simulated on the end-effector of this parallel robot by means of its mathematical models and control system. Three main motion parameters of simulated seismic waves, displacements, velocities, and accelerations, are measured, respectively, by laser tracker and acceleration sensors. The experimental results show this equipment is appropriate to be used as an earthquake simulator.

The objective of this paper is to determine whether a planar parallel manipulator with redundant actuation has better tracking accuracy than a planar parallel manipulator without redundant actuation. The effects of the redundant actuation on tracking accuracy of parallel manipulators are studied by using two different experimental platforms. The first platform is the planar five-bar parallel manipulator with normal actuation, and the other one is the planar parallel manipulator with redundant actuation. The dexterity pictures and the kinematic configurations of the two platforms validate the kinematic advantages from the redundant actuation. In order to study the dynamic advantages of the redundant actuation further, a nonlinear adaptive controller is presented for the two platforms. The experimental comparison is implemented on two actual parallel manipulator platforms, and from the experimental results, one can find the tracking accuracy of the parallel manipulator with redundant actuation can be improved above 38% than that of the five-bar parallel manipulator without redundant actuation.

Dynamic analysis and driving force optimization of a 5-DOF parallel manipulator with redundant actuation

<p>This paper presents an inverse dynamic model estimation based on an artificial neural network of a complete new parallel robot manipulator prototype 6- PGK with six degrees of freedom, built at Petru Maior University of Tirgu-Mures. The model estimation of the parallel robot manipulator is performed with a feedforward artificial neural network. In the control engineering domain there are control structures that need the direct or inverse model of the process for ensuring the process control at the imposed performances. Usually, the determination of the direct/inverse mathematical model is a difficult or impossible task to be achieved. In these cases different non-parametric or parametric, off-line or on-line identification methods are used. A solution that may support the on-line parametric methods is represented by the feedforward artificial neural networks. By implementing feedforward artificial neural networks as a nonlinear autoregressive model with exogenous inputs, the authors investigate the possibility of choosing the optimum parameters that characterize the neural network so that it approximates as better as possible the model of the 6-PGK prototype robot. Finally an innovative algorithm is developed for obtaining the optimal configuration parameters set of the feedforward artificial neural network. The proposed algorithm helps in setting the optimal parameters of the neural network that offer high opportunities to provide satisfactory identification of the robot model. Experimental results obtained by a structure derived from the proposed solution demonstrate a good approximation related to the studied system, which is characterized by nonlinearities and high complexity.</p>

Identification of dynamic parameters of a 3-DOF RPS parallel manipulator

This paper presents a decoupled nonsingular terminal sliding mode controller (DNTSMC) for a novel 3-DOF parallel manipulator with actuation redundancy. According to kinematic analysis, the inverse dynamic model for a novel 3-DOF redundantly actuated parallel manipulator is formulated in the task space using Lagrangian formalism and decoupled into three entirely independent subsystems under generalized coordinates to significantly reduce system complexity. Based on the dynamic model, a decoupled sliding mode control strategy is proposed for the parallel manipulator; the idea behind this strategy is to design a nonsingular terminal sliding mode controller for each subsystem, which can drive states of three subsystems to the original equilibrium points simultaneously by two intermediate variables. Additionally, a RBF neural network is used to compensate the cross-coupling force and gravity to enhance the control precision. Simulation and experimental results show that the proposed DNTSMC can achieve better control performances compared with the conventional sliding mode controller (SMC) and the DNTSMC without compensator.

This paper discusses the problem of generating trajectory for a 2-dof parallel manipulator with actuator redundancy. We investigate how to transform the speed and torque limits of the actuators to the velocity and acceleration limits in the end-effector space where the desired trajectory is normally specified. How these limits vary over the workspace is also investigated in details for the particular platform. With these limits, the automatic trajectory generation algorithm for traditional x-y table is modified to suit our parallel manipulator with actuator redundancy. Our trajectory generation algorithm can automatically generate symmetric straight line point-to-point trapezoidal trajectory for the parallel manipulator given the start point, the end point, and the desired traveling time. If it is impossible for the parallel manipulator to travel the distance within the desired traveling time under the velocity and acceleration limits, the shortest time trajectory will be generated instead.

Spatial object manipulation is subject to various parameters, which can be optimized by means of suitable motion strategies. In addition, corresponding strategies can be adapted to specified handling devices enabling efficient motion design with respect to kinematic and dynamic characteristics of particular manipulators. Further optimization is provided by the application of robot redundancy, whose resolution can be adapted to efficient motion planning. In this context, parallel kinematic systems featuring kinematic redundancy or a redundant actuator concept can be operated with an optimal set of actuator parameters allowing a resource-efficient object manipulation. This contribution is devoted to the conception and modeling of redundantly actuated parallel kinematic manipulators (RA-PKM) in order to realize optimal configuration strategies and motion design. Accordingly, the structure selection and the dimensional synthesis of a translational RA-PKM are presented based on parametric kinematic and dynamic modeling. Corresponding models provide an application-oriented transformation from intuitive CAD design software to technical computing and simulation software. The developed manipulator is suitable for the comparison of different redundant and non-redundant actuator configurations as well as optimal trajectories. Concluding analyses exemplarily refer to a non-redundant 3-arm and a redundant n-arm PRPaR system.