A parameter identification approach using optimal exciting trajectories for a class of industrial robots

In this paper, the problem of finding optimal exciting trajectories for parameter identification of industrial robots is investigated. A cost function of maximizing the minimum singular value of a recursive matrix is used in the optimization procedure. The optimal exciting trajectories obtained is insensitive with respect to the parameter perturbation. The identification accuracy and convergence speed or parameters is improved.     

The parameter identification of a population model of China

A mathematical model for predicting and controlling the future trends of the population of China is given. The model used is a discrete bilinear system with 101 dimensions. The model is decomposed, transformed and its parameters are estimated by nonlinear optimization and curve-fitting techniques. Results for both time-invariant and time-varying parameter estimation are obtained. Finally, a successful validation of the population prediction over the past 25 years is given, in which the predicting accuracy of the total population for each year is within about 1%. This is despite the fact that the available statistical data for these years, which include two censuses, are limited, incomplete and contain numerous contradictions.     

Trajectory tracking control of flexible-joint robots

Inverse dynamics control of flexible-joint robots is addressed. It is shown that, in a flexible-joint robot, the acceleration level inverse dynamic equations are singular because the control torques do not have an instantaneous effect on the end-effector accelerations due to the elastic media. Implicit numerical integration methods that account for the higher order derivative information are utilized for solving the singular set of differential equations. The trajectory tracking control law presented linearizes and decouples the system and yields an asymptotically stable fourth order error dynamics for each end-effector degree of freedom. A 3R spatial robot with all joints flexible is simulated to illustrate the performance of the proposed algorithm.     

Pattern recognition: An alternative to parameter identification in adaptive control

Practical models used in identification of process control processes must be too simplistic to give precise control information. However, these models can be used for adaptation if they are continuously readapted. But the identification then lacks the precision which might justify the analytic elaboration. One alternative has been to use pattern recognition as a means for allowing a computer system to characterize transient response computing readapted parameters which cause the control behavior to approach a desired transient ‘shape’. The paper summarizes work using pattern features as a basis for practice and theory.     

基于运动学和动力学的关节空间轨迹规划

在机器人轨迹优化设计的研究中,轨迹规划是实现机器人高速、高精度运行的核心,合理的轨迹规划不仅要满足机器人的运动学要求,而且要满足机器人的动力学要求,在驱动电机不变的情况下,增加机器人的速度和载荷是轨迹规划的难点。利用先进的python软件对机器人的运行轨迹进行深入的分析,在关节空间内采用五次插值多项式进行运动轨迹拟合,并将动力学模型加入到轨迹规划中。生成的拟合曲线表明,机器人在关节空间和工作空间内的位移、速度、加速度、加加速度曲线连续可导,各时间段的峰值力矩、峰值功率趋于同一数值,能有效的提高运动部件的速度和寿命。     

Gradient dynamic optimization with Legendre chaos

The polynomial chaos approach for stochastic simulation is applied to trajectory optimization, by conceptually replacing random variables with free variables. Using the gradient method, we generate with low computational cost an accurate parametrization of optimal trajectories.     

A dynamic simulator for humanoid robots

Computer simulation is an essential step in the design and construction of various mechanical structures, including biped robots, because it enables rapid testing and virtual prototyping during the construction phase. Although many different simulators are available, this article gives an overview and a motivation for building a new dynamic multibody simulator. The simulator is especially adapted to humanoid robot Archie, developed at the IHRT Institute at the Technical University of Vienna. In addition, it is shown how the simulator can be used not only in the controller design, but also in the online control loop to extend the available sensors: a virtual sensors principle. This work was presented in part at the First European Workshop on Artificial Life and Robtics, Vienna, Austria, July 12–13, 2007     

Joint stiffness identification of six-revolute industrial serial robots

Although robots tend to be as competitive as CNC machines for some operations, they are not yet widely used for machining operations. This may be due to the lack of certain technical information that is required for satisfactory machining operation. For instance, it is very difficult to get information about the stiffness of industrial robots from robot manufacturers. As a consequence, this paper introduces a robust and fast procedure that can be used to identify the joint stiffness values of any six-revolute serial robot. This procedure aims to evaluate joint stiffness values considering both translational and rotational displacements of the robot end-effector for a given applied wrench (force and torque). In this paper, the links of the robot are assumed to be much stiffer than its actuated joints. The robustness of the identification method and the sensitivity of the results to measurement errors and the number of experimental tests are also analyzed. Finally, the actual Cartesian stiffness matrix of the robot is obtained from the joint stiffness values and can be used for motion planning and to optimize machining operations.     

Direct identification of structural parameters from dynamic responses with neural networks

A novel neural network-based strategy is proposed and developed for the direct identification of structural parameters (stiffness and damping coefficients) from the time-domain dynamic responses of an object structure without any eigenvalue analysis and extraction and optimization process that is required in many identification algorithms for inverse problems. Two back-propagation neural networks are constructed to facilitate the process of parameter identifications. The first one, called emulator neural network, is to model the behavior of a reference structure that has the same overall dimension and topology as the object structure to be identified. After having been properly trained with the dynamic responses of the reference structure under a given dynamic excitation, the emulator neural network can be used as a nonparametric model of the reference structure to forecast its dynamic response with sufficient accuracy. However, when the parameters of the reference structure are modified to form a so-called associated structure, the dynamic responses forecast by the network will differ from the simulated responses of the associated structure. Their difference can be assessed with a proposed root mean square (RMS) difference vector for both velocity and displacement responses. With the associated structural parameters and their corresponding RMS difference vectors, another network, called parametric evaluation neural network, can be trained. In this study, several 5-story frames are considered as example object structures with simulated displacement and velocity time histories that mimic the measured dynamic responses in practice. The performance of the proposed strategy has been demonstrated quite satisfactorily; the error for the estimation of each stiffness or damping coefficient is less than 10% even in the presence of 7% noise. Numerical simulations show that the accuracy of the identified parameters can be significantly improved by injecting noise in the training patterns for the parametric evaluation neural network. The proposed strategy is extremely efficient in computation and thus has potential of becoming a practical tool for near real time monitoring of civil infrastructures.     

An improved dynamic modeling of a multibody system with spherical joints

A dynamic modeling of multibody systems having spherical joints is reported in this work. In general, three intersecting orthogonal revolute joints are substituted for a spherical joint with vanishing lengths of intermediate links between the revolute joints. This procedure increases sizes of associated matrices in the equations of motion, thus increasing computational burden of an algorithm used for dynamic simulation and control. In the proposed methodology, Euler parameters, which are typically used for representation of a rigid-body orientation in three-dimensional Cartesian space, are employed to represent the orientation of a spherical joint that connects a link to its previous one providing three-degree-of-freedom motion capability. For the dynamic modeling, the concept of the Decoupled Natural Orthogonal Complement (DeNOC) matrices is utilized. It is shown in this work that the representation of spherical joints motion using Euler parameters avoids the unnecessary introduction of the intermediate links, thereby no increase in the sizes of the associated matrices with the dynamic equations of motion. To confirm the efficiency of the proposed representation, it is illustrated with the dynamic modeling of a spatial four-bar Revolute-Spherical–Spherical-Revolute (RSSR) mechanism, where the CPU time of the dynamic modeling based on proposed methodology is compared with that based on the revolute joints substitution. Finally, it is explained how a complex suspension and steering linkage can be modeled using the proposed concept of Euler parameters to represent a spherical joint.     

Time-optimal and jerk-continuous trajectory planning for robot manipulators with kinematic constraints

In this paper a high smooth trajectory planning method is presented to improve the practical performance of tracking control for robot manipulators. The strategy is designed as a combination of the planning with multi-degree splines in Cartesian space and multi-degree B-splines in joint space. Following implementation, under the premise of precisely passing the via-points required, the cubic spline is used in Cartesian space planning to make either the velocities or the accelerations at the initial and ending moments controllable for the end effector. While the septuple B-spline is applied in joint space planning to make the velocities, accelerations and jerks bounded and continuous, with the initial and ending values of them configurable. In the meantime, minimum-time optimization problem is also discussed. Experimental results show that, the proposed approach is an effective solution to trajectory planning, with ensuring a both smooth and efficiency tracking performance with fluent movement for the robot manipulators.     

Dealing with internal and external perturbations on walking robots

Up to now, walking robots have been working outdoors under favorable conditions and using very large stability margins to cope with natural environments and intrinsic robot dynamics that can cause instability in these machines when they use statically-stable gaits. The result has been very slow robots prone to tumble down in the presence of perturbations. This paper proposes a novel gait-adaptation method based on the maximization of the Normalized Dynamic Energy Stability Margin. This method enables walking-machine gaits to adapt to internal (robot dynamics) and external (environmental) perturbations, including the slope of the terrain, by finding the gait parameters that maximize robot stability. The adaptation method is inspired in the natural gait adaptation carried out by humans and animals to balance external forces or the effect of sloping terrain. Experiments with the SILO4 quadruped robot are presented and show how robot stability is more robust when the proposed approach is used for different external forces and sloping terrains. Using the proposed gait-adaptation approach the robot is able to withstand external forces up to 58% the robot weight and 25-degree slopes.

An overview of standards development for robots and robotic systems

This paper is a summary of the standards development work which has been completed, is in process, or is under consideration in the area of robot and robotic systems standards including general areas such as environment, general requirements, data communications, general electrical and control requirements, control/machine interface, and safety. The intent of this paper is to show the interrelationship of various standards groups and organizations who are doing or considering doing work in this field and how the various standards projects or published standards are interrelated. Both national and international work is addressed. The paper also provides information on how the work in robots standards fits with other work being done in the area of industrial automation.     

Immune-based algorithms for dynamic optimization

The main problem with biologically inspired algorithms (like evolutionary algorithms or particle swarm optimization) when applied to dynamic optimization is to force their readiness for continuous search for new optima occurring in changing locations. Immune-based algorithm, being an instance of an algorithm that adapt by innovation seem to be a perfect candidate for continuous exploration of a search space. In this paper we describe various implementations of the immune principles and we compare these instantiations on complex environments.     

New dynamic fuzzy structure and dynamic system identification

In this study, a new fuzzy system structure that reduces the number of inputs is proposed for dynamic system identification applications. Algebraic fuzzy systems have some disadvantages due to many inputs. As the number of inputs increase, the number of parameters in the training process increase and hence the classical fuzzy system becomes more complex. In the conventional fuzzy system structure, the past information of both inputs and outputs are also regarded as inputs for dynamic systems, therefore the number of inputs may not be manageable even for single input and single output systems. The new dynamic fuzzy system module (DFM) proposed here has only a single input and a single output. We have carried out identification simulations to test the proposed approach and shown that the DFM can successfully identify non-linear dynamic systems and it performs better than the classical fuzzy system.     

A modified PSO algorithm with dynamic parameters for solving complex engineering design problem

This paper proposed a new approach of particle swarm optimization (PSO). The proposed modified PSO algorithm is equipped with some specially designed mechanisms of adaptively updating algorithm parameters to preserve the diversity of the swarm and to keep the balance between exploration and exploitation searches. All these mechanisms help the algorithm to avoid the premature convergence and to strengthen its robustness. Experiments are conducted on different complicated, unimodal and multimodal test functions, as well as a typical engineering inverse problem, the TEAM Workshop problem 22. The numerical results illustrate that the proposed PSO shows better performance as compared to other well developed evolutionary algorithms.     

An application of model predictive control to the dynamic economic dispatch of power generation

Two formulations exist for the problem of the optimal power dispatch of generators with ramp rate constraints: the optimal control dynamic dispatch (OCDD) formulation based on control system models, and the dynamic economic dispatch (DED) formulation based on optimization. Both are useful for the dispatch problem over a fixed time horizon, and they were treated as equivalent formulations in literature. This paper first shows that the two formulations are in fact different and both formulations suffer from the same technical deficiency of ramp rate violation during the periodic implementation of the optimal solutions. Then a model predictive control (MPC) approach is proposed to overcome such a technical deficiency. Furthermore, it is shown that the MPC solutions, which are based on the OCDD framework, converge to the optimal solution of an extended version of the DED problem and they are robust under certain disturbances and uncertainties. Two standard examples are studied: the first one of a ten-unit system shows the difference between the OCDD and DED, and possible ramp rate violations, and the second one of a six-unit system shows the convergence and robustness of the MPC solutions, and the comparison with OCDD as well.     

Explicit expression of the parameter bias in identification of Laguerre models from step responses

This paper delivers an analysis of the least-square estimation of the Laguerre coefficients of a linear discrete-time system from step response data. The original contribution consists in an explicit formula for the bias error on the estimated coefficients due to the under-modelling of the system. The formula, jointly with some a-priori information on the neglected dynamics, can be used to construct bounds on this error. The results presented in this paper are illustrated with a simulation example.     

An entropic optimization approach for a parameter identification problem in quasibrittle fracture

The cohesive crack model is a widely used idealization to represent simply and reliably the quasibrittle fracture behavior of concrete-like materials. However, knowledge of the parameters characterizing this model is of prime importance and cannot all be obtained directly from experiments. Typically, recourse is made to some inverse numerical approach. Our particular formulation can be elegantly cast as an instance of a challenging optimization problem known as a mathematical program with equilibrium (or, more precisely in our case, complementarity) constraints (MPEC). The present paper investigates application of an entropic optimization approach to solve the MPEC, and compares its performance with our previously proposed Fischer–Burmeister smoothing scheme. We use actual experimental data for the comparison.