Identification of dynamic parameters and feedforward control for a conventional industrial manipulator

Dynamic models play a critical role in the design of model-based controllers, which has a significant effect on the dynamic characteristics of motion equipment. This paper mainly focuses on the dynamic modeling and parameter identification for a gantry-type automated fiber placement (AFP) machine. First, a dynamic modeling process combining prismatic axes and revolute axes is conducted by Newton-Euler method, in which the effects of friction and hydraulic balance system are also considered. Then, as the convenience for parameter identification and the application in linearity control, the methods of dynamic model linearization and determination of minimum inertial parameters based on the multi-body system (MBS) theory are proposed, with which a dynamic model in the form of linearized minimum inertial parameters is consequently established. To identify the parameters in the model, key issues regarding excitation trajectory, filtering, and identification algorithm are discussed in detail. Finally, corresponding experiments are performed on the AFP machine, and experimental results show that there is a good agreement between the prediction of the model and the measurement in actuality. Data analysis shows that except for Z-axis, the relative error rates of the others are not greater than 5%, which proves the effectiveness of the established dynamic model and the identified parameters.

To address slow convergence speed and low convergence accuracy in the inverse model (IM) learning and training of the controlled object during BP neural network (BP-NN) feedforward compensation control for tracking a step signal, which results in poor dynamic and steady-state performance of the control system. This study introduces an improved Adam algorithm (IADAM)-enabled BP-NN feedforward compensation control method. This method introduces a dynamic learning rate adjustment mechanism based on the Adam algorithm, which adjusts the learning rate in phases based on the NN training parameter gradients. When the gradient exceeds a preset threshold, the learning rate is dynamically increased via an adjustment function to accelerate IM convergence. When the gradient is below the threshold, the maximum historical second-order moment estimate of the gradient is retained to keep the learning rate at a lower value, thereby improving IM convergence accuracy. The control system is composed of a PID controller, a neural network controller (NNC), and an IADAM-based neural network identifier (IADNNI). The PID controller provides online learning samples. The IADNNI learns the IM of the controlled system online, while the NNC shares the same structure and parameters as the IADNNI, generates real-time compensation control signals. Experimental results from a DC servo motor position control platform reveal that the proposed control method achieves zero overshoot in tracking both continuous step signals and square signals, while the settling time is reduced by 60.05% and 39.53%, respectively, compared to the ADAM + BP + PID control method. resulting in significant improvements in both dynamic and steady-state performance.

Efficient and high-precision identification of dynamic parameters is the basis of model-based robot control. Firstly, this paper designed the structure and control system of the developed lower extremity exoskeleton robot. The dynamics modeling of the exoskeleton robot is performed. The minimum parameter set of the identified parameters is determined. The dynamic model is linearized based on the parallel axis theory. Based on the beetle antennae search algorithm (BAS) and particle swarm optimization (PSO), the beetle swarm optimization algorithm (BSO) was designed and applied to the identification of dynamic parameters. The update rule of each particle originates from BAS, and there is an individual’s judgment on the environment space in each iteration. This method does not rely on the historical best solution in the PSO and the current global optimal solution of the individual particle, thereby reducing the number of iterations and improving the search speed and accuracy. Four groups of test functions with different characteristics were used to verify the performance of the proposed algorithm. Experimental results show that the BSO algorithm has a good balance between exploration and exploitation capabilities to promote the beetle to move to the global optimum. Besides, the test was carried out on the exoskeleton dynamics model. This method can obtain independent dynamic parameters and achieve ideal identification accuracy. The prediction result of torque based on the identification method is in good agreement with the ideal torque of the robot control.

A three-degree-of-freedom flight simulator platform with a translational and two rotational movement capabilities is studied. Based on the Lagrange method the dynamic model is established and then simplified by eliminating the sub-diagonal elements of the matrixes. A washout algorithm is employed to qualify the platform for flight simulation in limited space. In order to improve the modeling and experimental accuracies further when some dynamic parameters of an actual platform are unknown, the stepwise identification method is proposed to solve this problem. By dividing the driving torque into the load torque, reducer driving torque, screw driving toque and integrated friction torque, and main dynamic parameters are identified by applying proper trajectories and estimated by the weighed least square method. The procedures of identification are verified through comparing the driving forces from the identified model and those of servo motor encoders.

Accurate dynamic modeling and control parameters design of an industrial hybrid spray-painting robot

Load jumping and mode transitions both cause the unstable dynamic states for compound power-split hybrid Unmanned Ground Vehicles (UGVs), and these phenomena lead to vibrations of the transmission system and longitudinal buffeting of the vehicle. This study presents a feed-forward compensation control strategy for load jumping and mode transitions to reduce the corresponding torsional vibration in hybrid UGVs. The proposed method injects an appropriate harmonic current into a permanent magnet synchronous motor (PMSM) to generate a harmonic torque that is opposite to the load torque, which improves the dynamic response quality of the vehicle load. First, the multimode structure of a hybrid UGV and mode switching vibration and shock are investigated, as well as a feed-forward compensate control architecture is proposed. Second, two models are established the PMSM dynamic model based on the electromagnetic coupling principle and a 2-degree-of-freedom torsional vibration model of transmission system by simplifying the vehicle system. Third, the harmonic current injection method is proposed, and the harmonic current equation is derived. Based on the field-oriented control algorithm, a double closed-loop controller is designed for the torque and speed of the PMSM, and the internal model control method is applied to design the current controller. The simulation results show that the proposed strategy effectively suppresses jerk and that the harmonics current transfers the energy from the mechanical vibrations of the system into electric power fluctuations.

To obtain higher performance for an industrial hybrid robot, the dynamic control method is utilized to control the robot. For dynamic control, the control performance is directly affected by the accuracy of the dynamic model. This paper investigated a method to establish and identify an accurate dynamic model. First, based on the Lagrangian dynamic equation and the Stribeck friction model, the unidentified dynamic model of the five-DOF hybrid robot is established. Second, identification experiments are carried out. Each of the driving joints performs frequent reciprocating motions individually. In the meantime, the moving speed is gradually increased to obtain driving torques of the respective joint at different moving speeds. Then the dynamic parameters with lower coupling are identified by using the standard deviation index and the least squares methods until all parameters are gradually determined. Finally, the hybrid robot moves a typical trajectory, while the currents of each joint are collected to obtain the driving forces. The actual driving forces, the identified dynamic model, and the unidentified dynamic model are compared. The results show that the identified method could significantly improve the accuracy of the dynamic model. The method proposed in this paper is general and can be applied to the other robots without adding any sensors.

This paper presents a new hybrid method, which integrates the structured laser light, decoupled dynamic models, and evolutionary optimization strategy, to identify dynamic parameters of autonomous underwater vehicles. This is the first research that investigates the utilization of laser scanned images for the system identification of underwater vehicles. The AUV’s equations of motions and the dynamic parameter identification using the AUV’s decoupled motions will be illustrated. The working principles to calculate the AUV’s positons using the structured laser light will be explained with respect to the surge and pitch motions. The evolutionary optimization strategy used to generate the AUV’s dynamic parameters will be presented.

A common issue of the parallel robot is that has a large number of dynamic parameters, which requires a lot of processing time, whether in dynamic modeling, identification or control. The optimization and estimation of inertial parameters of a large DoF number of robotic system is crucial to tune the model-based control law in order to improve the robot accuracy. In this paper, we present an optimized number of dynamic parameters, called base inertial parameters. As a result, only 90 base inertial parameters affect the evr@ simulator instead of 210 standard one. Torque signals evaluation from experimental parallel platform and the developed analytical form show the effectiveness of the obtained results.

Aiming at the shortcomings of the current dynamic parameter identification methods in fitting nonlinear friction characteristics, the satisfaction of physically-consistent constraints, and the efficiency of dynamic calculation of the minimum inertial set, a physical consistent dynamic full parameter identification method based on the nonlinear friction model is proposed. Firstly, according to the characteristics of each component of the robot's dynamic equation, the joint trajectory that can cancel the non-friction moment term during the forward and reverse rotation of the joint is designed, and the friction moment is separated from the measured moment; Secondly, the nonlinear friction model is used to model the friction torque, and the friction parameters are obtained by solving the nonlinear least square problem; Finally, the regularized weighted least squares problem with physically-consistent constraints is solved by convex optimization method, and the full parameter set of robot dynamics including friction parameters and inertia parameters is obtained. The experimental results show that the RMS error of the proposed dynamic parameter identification method is the smallest compared with the CAD numerical calculation method and the penalty optimization identification method.

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.

Rigid instruments with limited flexibility utilized in the traditional endoscopic sinus surgery maybe damage the tissue when surgeon operates in the narrow space of nose specific area. Flexible continuum manipulator would solve this issue. However, current manipulators may not be able to address the intricate and varied surgical demands effectively, and they may not meet the surgical requirements adequately in terms of the flexibility, precision, and stiffness. This article introduced a novel modular continuum manipulator designed to offer exceptional adaptability, precision, and adjustable stiffness. The various sizes of the manipulators could be optimized according to different surgical scenario while taking its flexibility into account. Moreover, the model-based estimation and feedforward compensation control were applied to enhance its precision, taking into account the coupling effect of two sections and friction loss. Additionally, this study introduced a theoretical analysis on the feasibility of implementing variable stiffness for the manipulator, and the coupling was also lessened via increasing the stiffness of the first section. The experiments demonstrated that the maximum average deviation observed was 3.43 mm, which is equivalent to just 2.5% of the total length of the manipulator. Furthermore, the stiffness could be enhanced by up to 75%. Therefore, the proposed flexible manipulator has a potential for the nasal minimally invasive surgery.

To address the impact of nonlinear friction on the dynamic and static performance of permanent magnet synchronous motors (PMSM) in servo systems, an improved Dung Beetle Optimizer (IDBO) method is proposed for identifying Lu-Gre friction model parameters and implementing feedforward compensation. By introducing segmented PWLCM chaotic mapping, along with an improved dynamic sine and adaptive weight factor update for the Dung Beetle iterative model position, the convergence speed is accelerated while balancing the capabilities of global and local search. To identify the LuGre friction model parameters, the improved DBO reduces the identification error from 10% to within 2%, thereby enhancing accuracy. Finally, a feedforward compensation controller is designed and integrated into the speed loop of the servo system motor. Simulation experiments demonstrate that feedforward compensation based on the LuGre model effectively improves the accuracy of the servo system and reduces the impact of nonlinear friction on both dynamic and static performance.

A new breed of robots whose characteristics, objectives and operational modes drastically differ from more conventional industrial robots is getting to be more and more of interest. Conventional robotics, which has been successful in improving productivity and relieving human workers from tedious routine tasks in manufacturing, now faces a new challenge of automating areas of labor other than those directly linked to production.

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.