Identification Of Inertial Parameters Research Articles

A novel inertial parameter identification method for the ship-mounted Stewart platform based on a wave compensation platform

To meet the requirement of time-varying inertial parameter identification of control algorithms for shipborne Stewart wave compensation platforms, this study proposes an innovative inertial parameter identification method based on a non-inertial system. Unlike traditional Stewart identification methods with a fixed frame, the proposed method does not require designing excitation trajectories to consider the condition number of the observation matrix. Namely, the proposed method requires only performing two-dimensional dynamic parameter collection for parameter identification in a six-dimensional non-inertial motion. In addition, a coupling error dimension reduction method is developed to improve the identification accuracy under the condition of coupling motion. Finally, experiments are conducted to verify the effectiveness of the proposed identification method. The experimental results show that under the coupling motion conditions, the coupling errors of the corrected singularity points are reduced by 7.9–28.1% compared to before correction, and the axial force average error correction is below 5.32%.

An innovative inertial parameters identification method for non-cooperative space targets based on electrostatic interaction

Inertial characteristics of non-cooperative targets are crucial for space capture and subsequent on-orbit servicing. Previous methods for identifying inertial parameters involve proximity operations, which are associated with the risk of collision with non-cooperative targets. This paper introduces a long-range, contactless method for identifying the inertial parameters of a non-cooperative target during the pre-capture phase. Specifically, electrostatic interaction is used as an external excitation to alter the target’s motion. A force estimation algorithm that uses measurements from visual and potential sensors is proposed to estimate the electrostatic interaction and eliminate the need for force sensors. Furthermore, a recursive estimation–identification framework is presented to concurrently estimate the coupled motion state, weak electrostatic interaction, and inertial parameters of the target. The simulation results show that the proposed method extends the identification distance to 170 times that of the previous method while maintaining high identification precision for all parameters.

A Novel Methodology for Inertial Parameter Identification of Lightweight Electric Vehicle via Adaptive Dual Unscented Kalman Filter

A Novel Methodology for Inertial Parameter Identification of Lightweight Electric Vehicle via Adaptive Dual Unscented Kalman Filter

Inertial Parameter Identification for Closed-Loop Mechanisms: Adaptation of Linear Regression for Coordinate Partitioning

Abstract This study investigates the use of linear-regression-based identification in rigid multibody system applications. A multibody system model, originally described with differential-algebraic equations (DAE), is transformed into a set of ordinary differential equations using coordinate partitioning. This allows the identification framework (where the system is described with ordinary differential equations) to be applied to rigid multibody systems described with nonminimal coordinates. The methodology is demonstrated via numerical and experimental validation on a slider–crank mechanism. The results show that the presented methodology is capable of accurately identifying the system's inertial parameters even with a short motion trajectory used for training. The presented linear-regression-based identification approach opens new opportunities to develop more accurate multibody models. The resulting updated multibody models can be considered especially useful for state-estimation and the control of multibody systems.

Identification of time-varying inertia tensor parameters of a space solar power satellite based on a robust weighted recursive algorithm under impulsive disturbance

Considering changes in the structural size and mass caused by the on-orbit assembly process in a planar space solar power satellite, this study proposes an improved robust recursive algorithm to identify the time-varying inertia tensor parameters of the system. In this algorithm, to decrease the effect of impulsive disturbance on inertial parameter identification during the on-orbit assembly and improve computational accuracy, robust weighted factor and multi-innovation theory are involved. Then, the time-varying inertia tensor parameters of this large flexible structure are identified based on the distributed measurement results of multiple attitude sensors. The simulation results indicate that the proposed algorithm improves the identification accuracy and has better robustness than the original method under the impulsive disturbance. The relative values of the average relative error caused by the impulsive disturbance under the proposed algorithm are approximately 27.8–73.0% less than those under the original method. Additionally, the results also demonstrate that the proposed algorithm has higher noise immunity than the classical recursive least-squares method in a time-varying system. When the impulsive disturbance and zero-mean Gaussian white noise are applied to the system, compared with the conventional recursive least-squares method, the proposed algorithm yields lower absolute and relative values of the average relative error by approximately 4.2% and 44.9%, respectively.

Online identification of inertial parameters of a robot with partially combined links using IMU sensing

Online identification of inertial parameters of a robot with partially combined links using IMU sensing

An Open PLC-Based Robot Control System for 3D Concrete Printing

Three-dimensional concrete printing technology is currently a very topical and developing subject. There is a large number of applications worldwide where this technology can be used. In connection with this technology, the development of custom industrial robotic systems and their control is essential. Conventional closed-loop control system platforms do not provide sufficiently flexible solutions. This paper presents a control system for a unique printing robot that, thanks to its openness and unified platform, will enable simple and fast analysis and testing of key aspects in terms of control and guidance of the printing robot for additive manufacturing applications in the construction industry. The aim of this paper is to introduce the concept of an open PLC-based control system and to demonstrate its usefulness in the task of designing and implementing model-based control. All steps, from the analysis of the printing robot itself and identification of inertial parameters to the actual design and implementation of the control, can be executed in a unified Matlab/Simulink environment using various add-ons and toolboxes thanks to the open control system platform. This solution brings significant savings in terms of programming and prototyping time. The open control system was used to control an experimental model of a printing robot, serving as a test bed for the final version of the printing robot, and the results obtained were evaluated.

Filtering‐based concurrent learning adaptive attitude tracking control of rigid spacecraft with inertia parameter identification

SummaryThis paper investigates the attitude tracking control problem of rigid spacecraft with inertia parameter identification. Based on the relative attitude and angular velocity error dynamics, a basic adaptive backstepping based attitude tracking control scheme is firstly designed such that asymptotic attitude tracking can be achieved. However, the parameter identification error cannot decay to zero if the persistent excitation (PE) condition is not satisfied. To solve this issue, a filtering‐based concurrent learning adaptive backstepping control scheme is then proposed, by incorporating torque filtering technique with concurrent learning technique. A more mild rank condition, which consists of some collectable historical data, is provided to guarantee the convergence of parameter identification error. In addition, a valid data collection algorithm is given. It should be mentioned that a distinctive feature of the proposed filtering‐based concurrent learning adaptive control scheme is that the convergence rate of attitude tracking errors can be improved from asymptotical to exponential. Finally, simulation results are provided to illustrate the effectiveness of the proposed control schemes.

Real-Time Observability-Aware Inertia Parameter Estimation for Quadrotors

This study focuses on the system identification of a quadrotor under an unknown payload with a time-optimal trajectory. A model-based control scheme should be utilized for a quadrotor, which does not necessarily guarantee robustness to model uncertainty. Thus, accurate system identification is required during flight. However, inertia parameter identification for the control scheme is vulnerable to sensor noise without a trajectory that produces rich data. We utilized Kalman filter (KF), which estimates the angular velocity, to reduce noise. In the process model of KF, the variance attributed to model uncertainty is derived, and the derived variance plays a pivotal role in adjusting Kalman gain. Recursive least squares (RLS) was utilized to identify the inertia parameter. However, all inertia parameters cannot always be observed with a time-optimal trajectory. Thus, this study proposes a criterion that distinguishes between observable and unobservable parameters and the correction law depending on the criteria. The correction law prevents RLS from correcting the unobservable parameters. We call this method the observability-aware RLS with KF. This study compared RLS, RLS with a low-pass filter (LPF), and observability-aware RLS with KF. While RLS with LPF shows a sharp increase or decrease in MOI, COM offset, and sensor location, ours does not. RLS without any filtering has an inaccurate estimation of MOI and the height of the sensor location. However, our approach is sufficiently accurate to be applied to model-based control.

Quick-response attitude takeover control using multiple servicing spacecraft based on inertia properties identification

Multi-spacecraft is essential to on-orbit servicing (OOS) which leads to spacecraft life prolongation thus reducing space debris. However, the abrupt change of inertia parameters caused by the attachment of each servicing spacecraft is a critical obstacle to the stable and high-accuracy attitude takeover control of a target spacecraft during the entire servicing process using multiple servicing spacecraft. Therefore, a quick-response attitude takeover control method based on inertia properties identification is proposed in this paper. Combining the Euler dynamical equations of combined spacecraft with space environment moment model, a novel iterative identification equation integrating inertia matrix is established to eliminate ill-conditioned identification and realize the precise identification of inertia parameters using only single sampling data for each update. With the quickly and precisely estimated inertia parameters, a Lyapunov-based attitude controller and an optimal torque allocation method are designed to actualize the high-accuracy and globally stable attitude takeover control with the minimum energy consumption. The proposed method can actualize the quick-response and stable attitude takeover control with low computational cost, even at the moment of attachment of servicing spacecraft. Hence, it is greatly appropriate for the OOS missions with multiple servicing spacecraft. The ground experiments and numerical simulations were conducted for demonstrating the feasibility and effectiveness of the proposed method. The experiment result indicated that the trajectory tracking error could converge to ±0.02° and ±0.02°/s and the parameters identification error was less than 5.5%.

Neural network-based reinforcement learning control for combined spacecraft attitude tracking maneuvers

This paper proposes a novel reinforcement learning-based attitude tracking control strategy for combined spacecraft takeover maneuvers with completely unknown dynamics. One major issue in the context of combined spacecraft attitude takeover control is that the accurate dynamic model is highly nonlinear, complex and costly to identify online, which makes it impractical for control design. To address this issue, we take the advantage of the Q-learning algorithm to acquire the control strategy directly from system input/output measurement data in a model-free manner, and thus the online inertia parameter identification procedure is avoided. More specifically, first, the attitude tracking is formulated as a regulation problem by introducing an argumented system, where the system dynamic model is still required in control design. Then, in order to achieve a model-free control strategy, an online policy-iteration (PI) Q-learning procedure is derived to solve the Bellman optimality equation by utilizing the generated measurement data. In theoretical analysis, it is proved that the iteration sequences of Q value function and control strategy can converge to the optimal ones. In addition, rigorous proof of the stability and monotonicity guarantees of the proposed control strategy are also provided. Furthermore, for the purpose of online implementation, off-policy learning scheme is employed to find the optimal Q value function approximator with neural network structure after data-collection phase. Numerical simulations are exhibited to validate the effectiveness of the proposed strategy.

Concurrent Learning Adaptive Finite-Time Control for Spacecraft With Inertia Parameter Identification Under External Disturbance

This article investigates the postcapture control problem for the combined spacecraft formed after a service spacecraft captures a noncooperative target by manipulators. Two concurrent learning adaptive finite-time controllers are developed to achieve trajectory tracking and inertia parameter identification simultaneously. As a very common uncertainty affecting the spacecraft motion, the external disturbance should be considered, but it is ignored in most studies identifying inertia parameters. In this article, the presented controllers are designed according to the specific knowledge of the external disturbance: one controller is designed based on the disturbance bound that is assumed to be known; the other identifies the disturbance parameters together with the inertia parameters, presupposing that the nonlinearities involved in the external disturbance are known. Both of the two controllers require no a priori knowledge of the mass and inertia matrix of the combined spacecraft and take the input saturation into account. Sufficient conditions to identify inertia parameters are also given with no need of persistent excitation. The convergence of the closed-loop system is proved within the Lyapunov framework. Numerical results show that the proposed controllers can track the desired position and attitude trajectories in finite time, and, at the same time, the inertia parameters converge to their real values.

Inertial Parameter Identification in Robotics: A Survey

This work aims at reviewing, analyzing and comparing a range of state-of-the-art approaches to inertial parameter identification in the context of robotics. We introduce “BIRDy (Benchmark for Identification of Robot Dynamics)”, an open-source Matlab toolbox, allowing a systematic and formal performance assessment of the considered identification algorithms on either simulated or real serial robot manipulators. Seventeen of the most widely used approaches found in the scientific literature are implemented and compared to each other, namely: the Inverse Dynamic Identification Model with Ordinary, Weighted, Iteratively Reweighted and Total Least-Squares (IDIM-OLS, -WLS, -IRLS, -TLS); the Instrumental Variables method (IDIM-IV), the Maximum Likelihood (ML) method; the Direct and Inverse Dynamic Identification Model approach (DIDIM); the Closed-Loop Output Error (CLOE) method; the Closed-Loop Input Error (CLIE) method; the Direct Dynamic Identification Model with Nonlinear Kalman Filtering (DDIM-NKF), the Adaline Neural Network (AdaNN), the Hopfield-Tank Recurrent Neural Network (HTRNN) and eventually a set of Physically Consistent (PC-) methods allowing the enforcement of parameter physicality using Semi-Definite Programming, namely the PC-IDIM-OLS, -WLS, -IRLS, PC-IDIM-IV, and PC-DIDIM. BIRDy is robot-agnostic and features a complete inertial parameter identification pipeline, from the generation of symbolic kinematic and dynamic models to the identification process itself. This includes functionalities for excitation trajectory computation as well as the collection and pre-processing of experiment data. In this work, the proposed methods are first evaluated in simulation, following a Monte Carlo scheme on models of the 6-DoF TX40 and RV2SQ industrial manipulators, before being tested on the real robot platforms. The robustness, precision, computational efficiency and context of application the different methods are investigated and discussed.

Identification of Inertial Parameters for Position and Force Control of Surgical Assistance Robots

Surgeries or rehabilitation exercises are hazardous tasks for a mechanical system, as the device has to interact with parts of the human body without the hands-on experience that the surgeon or physiotherapist acquires over time. For various gynecological laparoscopic surgeries, such as laparoscopic hysterectomy or laparoscopic pelvic endometriosis, Uterine Manipulators are used. These medical devices allow the uterus to be suitably mobilized. A gap needs to be filled in terms of the precise handling of this type of devices. In this sense, this manuscript first describes the mathematical procedure to identify the inertial parameters of uterine manipulators. These parameters are needed to establish an accurate position and force control for an electromechanical system to assist surgical operations. The method for identifying the mass and the center of mass of the manipulator is based on the solution of the equations for the static equilibrium of rigid solids. Based on the manipulator inertial parameter estimation, the paper shows how the force exerted by the manipulator can be obtained. For this purpose, it solves a matrix system composed of the torques and forces of the manipulator. Different manipulators have been used, and it has been verified that the mathematical procedures proposed in this work allow us to calculate in an accurate and efficient way the force exerted by these manipulators.

A Newton iterative optimization combined with window loop calculation algorithm for estimating accelerometer bias based on gravitational apparent motion with excitation of swinging motion.

The initial alignment method, including the identification of inertial device error parameters, has always been a key issue in an inertial navigation system (INS). This study focuses on the error caused by the random noise of inertial devices that can be compensated by the reconstruction of gravitational apparent motion in an inertial frame under the condition of swinging motion. Attitude angles and accelerometer bias can also be estimated. However, the analysis and simulation results indicate that the existing methods cannot estimate the gyroscope bias. The accelerometer and the gyroscope bias will change over a long time, which will lead to long-term parameter identification accuracy decline or even failure. In this paper, a parameter identification algorithm based on Newton iterative optimization combined with a window loop calculation is designed to solve these problems. Simulation and turntable tests indicate that the proposed new algorithm can fulfill the initial alignment of strapdown INS under the swinging condition and estimate accelerometer bias effectively. Moreover, the new algorithm improves data utilization, which also has better time sensitivity, and the calculated alignment errors can nearly approach zero.

A Parameter Identification Method for Stewart Manipulator Based on Wavelet Transform

Aiming at inertial and viscous parameter identification for the Stewart manipulator regardless of the influence of Coulomb friction, a simple and effective dynamical parameter identification method based on wavelet transform and joint velocity analysis is proposed in this paper. Compared with previously known identification methods, the advantages of the new approach are that (1) the excitation trajectory is easy to design, and (2) it can not only identify the inertial matrix, but also the viscous matrix accurately regardless of the influence of Coulomb friction. Comparison is made among the identification method proposed in this paper, another identification method proposed previously, and the true value calculated with a formula. The errors from results of different identification methods demonstrate that the method proposed in this paper shows great adaptability and accuracy.

Single‐loop model prediction control of PMSM with moment of inertia identification

In order to improve the dynamic response performance and robustness of the permanent magnet synchronous motor control system and optimize the parameter adjustment of the control system, a motor control method based on single‐loop model prediction and moment of inertia parameter identification with forgetting factor is proposed. First, the motor state equation in the prediction model is designed, and the single‐loop model predictive control method is proposed. Then, comparing the effects of various parameters of the motor on the running stability of the motor, a least‐squares identification method based on forgetting factor is added to identify the moment of inertia online and optimize the prediction model in real time. The simulation and experimental results show that the proposed method has good dynamic performance for the permanent magnet synchronous motor adapting to the rotational speed and load variation. At the same time, the addition of the moment of inertia identification enhances the accuracy and system robustness of the motor model parameters during the model prediction process. This method provides an effective way of engineering control. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

New Methodology for Inertial Identification of Low Mobility Mechanisms Considering Dynamic Contribution

Knowledge of dynamic parameters of mechanical systems is required in different applications, particularly in the simulation and control problems. In this paper, the standard identification methods are discussed and a new methodology for identification of inertial parameters is raised when the closed chain has low mobility. The methodology includes formulating a symbolic model based on the transfer of inertial properties and a reduction using dynamic contribution indices based on CAD approximations. The new method is applied to the front suspension of an electrical vehicle. After applying the procedure, a model with few parameters that allows accurately reproducing the dynamic behaviour of the system is obtained. A novel methodology has been developed that allows the identification of dynamic parameters in low mobility mechanical systems.

Geometric Robot Dynamic Identification: A Convex Programming Approach

Recent work has shed light on the often unreliable performance of constrained least-squares estimation methods for robot mass-inertial parameter identification, particularly for high degree-of-freedom systems subject to noisy and incomplete measurements. Instead, differential geometric identification methods have proven to be significantly more accurate and robust. These methods account for the fact that the mass-inertial parameters reside in a curved Riemannian space, and allow perturbations in the mass-inertial properties to be measured in a coordinate-invariant manner. Yet, a continued drawback of existing geometric methods is that the corresponding optimization problems are inherently nonconvex, have numerous local minima, and are computationally highly intensive to solve. In this paper, we propose a convex formulation under the same coordinate-invariant Riemannian geometric framework that directly addresses these and other deficiencies of the geometric approach. Our convex formulation leads to a globally optimal solution, reduced computations, faster and more reliable convergence, and easy inclusion of additional convex constraints. The main idea behind our approach is an entropic divergence measure that allows for the convex regularization of the inertial parameter identification problem. Extensive experiments with the 3-DoF MIT Cheetah leg, the 7-DoF AMBIDEX tendon-driven arm, and a 16-link articulated human model show markedly improved robustness and generalizability vis-a-vis existing vector space methods while ensuring fast, guaranteed convergence to the global solution.

Estimation and exploitation of objects ’ inertial parameters in robotic grasping and manipulation: A survey

Inertial parameters characterise an object’s motion under applied forces, and can provide strong priors for planning and control of robotic actions to manipulate the object. However, these parameters are not available a-priori in situations where a robot encounters new objects. In this paper, we describe and categorise the ways that a robot can identify an object’s inertial parameters. We also discuss grasping and manipulation methods in which knowledge of inertial parameters is exploited in various ways. We begin with a discussion of literature which investigates how humans estimate the inertial parameters of objects, to provide background and motivation for this area of robotics research. We frame our discussion of the robotics literature in terms of three categories of estimation methods, according to the amount of interaction with the object: purely visual, exploratory, and fixed-object. Each category is analysed and discussed. To demonstrate the usefulness of inertial estimation research, we describe a number of grasping and manipulation applications that make use of the inertial parameters of objects. The aim of the paper is to thoroughly review and categorise existing work in an important, but under-explored, area of robotics research, present its background and applications, and suggest future directions. Note that this paper does not examine methods of identification of the robot’s inertial parameters, but rather the identification of inertial parameters of other objects which the robot is tasked with manipulating.