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This paper aims to propose a forcefree control algorithm that is based on a dynamic model with full torque compensation is proposed to improve the compliance and flexibility of the direct teaching of cooperative robots.

Dynamic parameters identification is performed first to obtain an accurate dynamic model. The identification process is divided into two steps to reduce the complexity of trajectory simplification, and each step contains two excitation trajectories for higher identification precision. A nonlinear friction model that considers the angular displacement and angular velocity of joints is proposed as a secondary compensation for identification. A torque compensation algorithm that is based on the Hogan impedance model is proposed, and the torque obtained by an impedance equation is regarded as the command torque, which can be adjusted. The compensatory torque, including gravity torque, inertia torque, friction torque and Coriolis torque, is added to the compensation to improve the effect of forcefree control.

The model improves the total accuracy of the dynamic model by approximately 20% after compensation. Compared with the traditional method, the results prove that the forcefree control algorithm can effectively reduce the drag force approximately 50% for direct teaching and realize a flexible and smooth drag.

The entire algorithm is verified by the laboratory-developed six degrees-of-freedom cooperative robot, and it can be applied to other robots as well.

A full torque compensation is performed after parameters identification, and a more accurate forcefree control is guaranteed. This allows the cooperative robot to be dragged more smoothly without external sensors.

The chapter considers parameter identification, estimation, and model diagnostics in medium scale DSGE models from a frequency domain perspective using the framework developed in Qu and Tkachenko (2012). The analysis uses Smets and Wouters (2007) as an illustrative example, motivated by the fact that it has become a workhorse model in the DSGE literature. For identification, in addition to checking parameter identifiability, we derive the non-identification curve to depict parameter values that yield observational equivalence, revealing which and how many parameters need to be fixed to achieve local identification. For estimation and inference, we contrast estimates obtained using the full spectrum with those using only the business cycle frequencies to find notably different parameter values and impulse response functions. A further comparison between the nonparametrically estimated and model implied spectra suggests that the business cycle based method delivers better estimates of the features that the model is intended to capture. Overall, the results suggest that the frequency domain based approach, in part due to its ability to handle subsets of frequencies, constitutes a flexible framework for studying medium scale DSGE models.

This paper aims to propose a novel system identification and resonance suppression strategy for motor-driven system with high-order flexible manipulator.

In this paper, first, a unified mathematical model is proposed to describe both the flexible joints and the flexible link system. Then to suppress the resonance brought by the system flexibility, a model based high-order notch filter controller is proposed. To get the true value of the parameters of the high-order flexible manipulator system, a fuzzy-Kalman filter-based two-step system identification algorithm is proposed.

Compared to the traditional system identification algorithm, the proposed two-step system identification algorithm can accurately identify the unknown parameters of the high order flexible manipulator system with high dynamic response. The performance of the two-step system identification algorithm and the model-based high-order notch filter is verified via simulation and experimental results.

The proposed system identification method can identify the system parameters with both high accuracy and high dynamic response. With the proposed system identification and model-based controller, the positioning accuracy of the flexible manipulator can be greatly improved.

The purpose of this paper is to propose a new approach for aerodynamic parameter identification of hypersonic vehicles, which is based on Pigeon-inspired optimization (PIO) algorithm, with the objective of overcoming the disadvantages of traditional methods based on gradient such as New Raphson method, especially in noisy environment.

The model of hypersonic vehicles and PIO algorithm is established for aerodynamic parameter identification. Using the idea, identification problem will be converted into the optimization problem.

A new swarm optimization method, PIO algorithm is applied in this identification process. Experimental results demonstrated the robustness and effectiveness of the proposed method: it can guarantee accurate identification results in noisy environment without fussy calculation of sensitivity.

The new method developed in this paper can be easily applied to solve complex optimization problems when some traditional method is failed, and can afford the accurate hypersonic parameter for control rate design of hypersonic vehicles.

In this paper, the authors converted this identification problem into the optimization problem using the new swarm optimization method – PIO. This new approach is proved to be reasonable through simulation.

This paper aims to propose numerical-based and experiment-based identification processes, accounting for uncertainties to identify structural parameters, in a wave propagation framework.

A variant of the inhomogeneous wave correlation (IWC) method is proposed. It consists on identifying the propagation parameters, such as the wavenumber and the wave attenuation, from the frequency response functions. The latters can be computed numerically or experimentally. The identification process is thus called numerical-based or experiment-based, respectively. The proposed variant of the IWC method is then combined with the Latin hypercube sampling method for uncertainty propagation. Stochastic processes are consequently proposed allowing more realistic identification.

The proposed variant of the IWC method permits to identify accurately the propagation parameters of isotropic and composite beams, whatever the type of the identification process in which it is included: numerical-based or experiment-based. Its efficiency is proved with respect to an analytical model and the Mc Daniel method, considered as reference. The application of the stochastic identification processes shows good agreement between simulation and experiment-based results and that all identified parameters are affected by uncertainties, except damping.

The proposed variant of the IWC method is an accurate alternative for structural identification on wide frequency ranges. Numerical-based identification process can reduce experiments’ cost without significant loss of accuracy. Statistical investigations of the randomness of identified parameters illustrate the robustness of identification against uncertainties.

When a manipulator captures an unknown space object, inertia parameters of endpoint payload should be timely obtained to handle possible unexpected parameter variations and monitor the system’s operating conditions. Therefore, this study aims to present an identification method for estimating the inertia parameter of the payload carried by a flexible two-link space manipulator.

The original nonlinear dynamics model of the manipulator is linearized at a selected working point. Subsequently, the system modal frequencies with and without payload are determined using the subspace identification algorithm, and the difference of these frequencies is computed. Furthermore, by adjusting the structural configuration of the manipulator, multiple sets of frequency differences are obtained. Therefore, the inertia parameters of the payload, i.e. the mass and the moment of inertia, can be derived from the frequency differences by solving a least-squares problem.

The proposed method can effectively estimate the payload parameters and has satisfactory identification accuracy.

The approach’s implementation provides a practical reference for determining inertia parameters of an unknown space target in the capture process.

The study proposes a novel method for identifying the inertia parameters of the payload of a flexible two-link space manipulator using the estimated system frequencies.

The purpose of this paper is to present a method to obtain the inertia parameter of a captured unknown space target.

An inertia parameter identification method is proposed in the post-capture scenario in this paper. This method is to resolve parameter identification with two steps: coarse estimation and precise estimation. In the coarse estimation step, all the robot arms are fixed and inertia tensor of the combined system is first calculated by the angular momentum conservation equation of the system. Then, inertia parameters of the unknown target are estimated using the least square method. Second, in the precise estimation step, the robot arms are controlled to move and then inertia parameters are once again estimated by optimization method. In the process of optimization, the coarse estimation results are used as an initial value.

Numerical simulation results prove that the method presented in this paper is effective for identifying the inertia parameter of a captured unknown target.

The presented method can also be applied to identify the inertia parameter of space robot.

In the classic momentum-based identification method, the linear momentum and angular momentum of system, both considered to be conserved, are used to identify the parameter of system. If the elliptical orbit in space is considered, the conservation of linear momentum is wrong. In this paper, an identification based on the conservation of angular momentum and dynamics is presented. Compared with the classic momentum-based method, this method can get a more accurate identification result.

Parameter identification is a technique which aims at determining material or other process parameters based on a combination of experimental and numerical techniques. In recent years, heuristic approaches, such as genetic algorithms (GAs), have been proposed as possible alternatives to classical identification procedures. The present work shows that particle swarm optimization (PSO), as an example of such methods, is also appropriate to identification of inelastic parameters. The paper aims to discuss these issues.

PSO is a class of swarm intelligence algorithms which attempts to reproduce the social behaviour of a generic population. In parameter identification, each individual particle is associated to hyper-coordinates in the search space, corresponding to a set of material parameters, upon which velocity operators with random components are applied, leading the particles to cluster together at convergence.

PSO has proved to be a viable alternative to identification of inelastic parameters owing to its robustness (achieving the global minimum with high tolerance for variations of the population size and control parameters), and, contrasting to GAs, higher convergence rate and small number of control variables.

PSO has been mostly applied to electrical and industrial engineering. This paper extends the field of application of the method to identification of inelastic material parameters.

This paper aims to propose a novel method to identify the parameters of robotic manipulators using the torque exerted by the robot joint motors (measured by current sensors).

Previous studies used additional sensors like force sensor and inertia measurement unit, or additional payload mounted on the end-effector to perform parameter identification. The settings of these previous works were complicated. They could only identify part of the parameters. This paper uses the torque exerted by each joint while performing Fourier periodic excited trajectories. It divides the parameters into a linear part and a non-linear part, and uses linear least square (LLS) parameter estimation and dual-swarm-based particle swarm optimization (DPso) to compute the linear and non-linear parts, respectively.

The settings are simpler and can identify the dynamic parameters, the viscous friction coefficients and the Coulomb friction coefficients of two joints at the same time. A SIASUN 7-Axis Flexible Robot is used to experimentally validate the proposal. Comparison between the predicted torque values and ground-truth values of the joints confirms the effectiveness of the method.

The proposed method identifies two joints at the same time with satisfying precision and high efficiency. The identification errors of joints do not accumulate.

The purpose of this paper is to discuss the characteristics of several stochastic simulation methods applied in computation issue of structure health monitoring (SHM).

On the basis of the previous studies, this research focusses on four promising methods: transitional Markov chain Monte Carlo (TMCMC), slice sampling, slice-Metropolis-Hasting (M-H), and TMCMC-slice algorithm. The slice-M-H is the improved slice sampling algorithm, and the TMCMC-slice is the improved TMCMC algorithm. The performances of the parameters samples generated by these four algorithms are evaluated using two examples: one is the numerical example of a cantilever plate; another is the plate experiment simulating one part of the mechanical structure.

Both the numerical example and experiment show that, identification accuracy of slice-M-H is higher than that of slice sampling; and the identification accuracy of TMCMC-slice is higher than that of TMCMC. In general, the identification accuracy of the methods based on slice (slice sampling and slice-M-H) is higher than that of the methods based on TMCMC (TMCMC and TMCMC-slice).

The stochastic simulation methods evaluated in this paper are mainly two categories of representative methods: one introduces the intermediate probability density functions, and another one is the auxiliary variable approach. This paper provides important references about the stochastic simulation methods to solve the ill-conditioned computation issue, which is commonly encountered in SHM.