A robust adaptive control of a parallel robot

The work presented in this article deals with the robust adaptive control tracking of a 6 degree of freedom parallel robot, called C5 parallel robot. The proposed approach is based on the coupling of sliding modes and multi-layers perceptron neural networks (MLP-NNs). It does not require the inverse dynamic model for deriving the control law. The MLP-NN is added in the control scheme to estimate the gravitational and frictional forces along with the non-modelled dynamic effects. The nonlinearity problem, present in neural networks, is resolved using Taylor series expansion. The proposed approach allows to adjust the parameters of neural network and sliding mode control terms by taking into account a reference model and the closed-loop stability in the Lyapunov sense. We implemented our approach on the C5 parallel robot of LISSI laboratory and performed experiments to observe its effectiveness and the robust behaviour of the controller against external disturbances.     

Uncertainty estimation in robot kinematic calibration

Currently, the results of a robot calibration procedure are expressed generally in terms of the position and orientation error for a set of locations and orientations, which have been obtained from the previously identified kinematic parameters. In this work, a technique is presented to evaluate the calibration uncertainty for a robot arm calibrated using the circle point analysis method. The method developed is based on the probability distribution propagation calculation recommended by the Guide to the Expression of Uncertainty of Measurement and on the Monte Carlo method. This method makes it possible to calculate the uncertainty in the identification of each single robot parameter, and thus, to estimate the robot positioning uncertainty due to the calibration uncertainty, rather than based on a single set locations and orientations that are previously defined for a unique set of identified parameters. Additionally, this technique allows for the establishment of the best possible conditions for the data capture test, which identifies parameters and determines which of them have the least possible calibration uncertainty. This determination is based on the variables involved in the data capture process by propagating their influence up to the final robot accuracy.     

A scheme for robust trajectory control of space robots

This paper presents a scheme for robust trajectory control of free-floating space robots. The idea is based on the overwhelming robust trajectory control of a ground robot on a flexible foundation and robust foundation disturbance compensation presented elsewhere. No external jets/thrusters are required or used in the scheme. An example of a three-link robot mounted on a free-floating space platform is considered for demonstrating the efficacy of the control scheme. Bond graph technique has been used for the purpose of modeling and simulation. Robustness of the control scheme is guaranteed since the controller does not require the knowledge of the manipulator parameters.     

A neural network-based approach for an assembly cell control

In several robotics applications, the robot must interact with the workspace, and thus its motion is constrained by the task. In this case, pure position control will be ineffective since forces appearing during the contacts must also be controlled. However, simultaneous position and force control called hybrid control is then required. Moreover, the nonlinear plant dynamics, the complexity of the dynamic parameters determination and computation constraints makes more difficult the synthesis of control laws. In order to satisfy all these constraints, an effective hybrid force/position approach based on artificial neural networks for multi-inputs/multi-outputs systems is proposed. This approach realizes, simultaneously, an identification and control of systems, and it is implemented according to two phases: At first, a neural observer is trained off-line on the basis of the data acquired during contact motion, in order to realize a smooth transition from free to contact motion. Then, an online learning of the neural controller is implemented using neural observer parameters so that the closed-loop system maintains a good performance and compensates for uncertain/unknown dynamics of the robot and the environment. A typical example on which we shall focus is an assembly task. Experimental results on a C5 links parallel robot demonstrate that the robot's skill improves effectively and the force control performances are satisfactory, even if the dynamics of the robot and the environment change.     

Vision-based Control of a Delta Parallel Robot via Linear Camera-Space Manipulation

One of the open problems to control a parallel robot in real-time is the larger number of parameters to be incorporated in the control model when compared to serial robots. This paper presents an innovative vision-based method to control a delta-type parallel robot based on Linear Camera-Space Manipulation. The proposed method is a simple and robust technique capable of achieving real-time control of robots without relying on the calibration of either the robot or the environment parameters. To document the robustness of this technique, a sensitivity analysis was performed in simulation where the effect of two sources of error on the end-point positioning are considered. Such sources are the variability of each link’s parameters, and the uncertainty of the visual measurements. Experimental results on a Clavel’s delta parallel robot show that end-point positioning errors obtained with Linear Camera-Space Manipulation are less than 1.5 mm, demonstrating a low sensitivity to parameter uncertainty in qualitative agreement with the simulation results. The results show that the developed approach is advantageous to control parallel robots for industrial applications in real-time and can obviate to a number of open problems common with the control of parallel robots.     

Unifying Kinematic Modeling, Identification, and Control of a Gough–Stewart Parallel Robot Into a Vision-Based Framework

In this paper, it is shown that computer vision, used as an exteroceptive redundant metrology mean, simplifies the control of a Gough-Stewart parallel robot. Indeed, contrary to the usual methodology, where the robot is modeled independently from the control law which will be implemented, we take into account that vision will be used for control, from the early modeling stage. Hence, kinematic modeling and projective geometry are fused into a control-devoted projective kinematic model. Thus, a novel vision-based kinematic modeling of such a robot is proposed through the observation of its legs. Inspired by the geometry of lines, this model unifies and simplifies both identification and control. Indeed, it has a reduced parameter set, and allows us to propose a linear solution to its calibration. Using the same model, a visual servoing scheme is presented, where the attitudes of the nonrigidly linked legs are servoed, rather than the end-effector pose. Finally, theoretical results concerning the stability of this control law are provided     

Robust force control strategy based on the virtual environment concept

The main goal of this paper is to present a force control strategy based on the virtual environment concept. This concept is a way to increase the robustness of force control schemes with respect to a variation of the environment characteristics. We first propose this approach, then we analyze it and, finally, we adapt it to a classical external force control scheme. Experimental results with a DELTA fast parallel robot are presented to prove the efficiency of this method.     

Adaptive control of a 3-DOF parallel manipulator considering payload handling and relevant parameter models

Model-based control improves robot performance provided that the dynamics parameters are estimated accurately. However, some of the model parameters change with time, e.g. friction parameters and unknown payload. Particularly, off-line identification approaches omit the payload estimation (due to practical reasons). Adaptive control copes with some of these structural uncertainties. Thus, this work implements an adaptive control scheme for a 3-DOF parallel manipulator. The controller relies on a novel relevant-parameter dynamic model that permits to study the cases in where the uncertainties affect: (1) rigid body parameters, (2) friction parameters, (3) actuator dynamics, and (4) a combination of the former cases. The simulations and experiments verify the performance of the proposed controller. The control scheme is implemented on the modular programming environment Open Robot Control Software (OROCOS). Finally, an experimental setup evaluates the controller performance when the robot handles a payload.     

Decentralized adaptive tracking control of robot manipulators

An asymptotically stable decentralized adaptive control scheme is presented to enable accurate trajectory tracking without requiring specific knowledge about the robot dynamics. The scheme is based on expressing the robot dynamics as the product of individual joint quantities, and bounds on certain robot parameters. Parameter adaptation laws are derived using the Lyapunov theory, and asymptotic stability of tracking errors, and boundedness of parameter estimates are established. The control system is shown to be robust to torque disturbances affecting the system and to a class of unmodeled dynamics. The structure of the controller and the performance of the closed-loop system are analyzed. Simulations results using the complete dynamic model of a six degree of freedom industrial robot are presented to demonstrate the excellent tracking performance of the proposed adaptive control scheme. © 1996 John Wiley & Sons, Inc.     

Design of Adaptive Robot Control System Using Recurrent Neural Network

The use of a new Recurrent Neural Network (RNN) for controlling a robot manipulator is presented in this paper. The RNN is a modification of Elman network. In order to solve load uncertainties, a fast-load adaptive identification is also employed in a control system. The weight parameters of the network are updated using the standard Back-Propagation (BP) learning algorithm. The proposed control system is consisted of a NN controller, fast-load adaptation and PID-Robust controller. A general feedforward neural network (FNN) and a Diagonal Recurrent Network (DRN) are utilised for comparison with the proposed RNN. A two-link planar robot manipulator is used to evaluate and compare performance of the proposed NN and the control scheme. The convergence and accuracy of the proposed control scheme is proved.     

Adaptive robust fuzzy control for path tracking of a wheeled mobile robot

This paper proposes an adaptive robust fuzzy control scheme for path tracking of a wheeled mobile robot with uncertainties. The robot dynamics including the actuator dynamics is considered in this work. The presented controller is composed of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot complete dynamics, an adaptive robust input to overcome the uncertainties, and a stabilizing control input. The stability and the convergence of the tracking errors are guaranteed using the Lyapunov stability theory. When the controller is designed, the different parameters for two actuator models in the dynamic equation are taken into account. The proposed control scheme does not require the accurate parameter values for the actuator parameters as well as the robot parameters. The validity and robustness of the proposed control scheme are demonstrated through computer simulations. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Adaptive pole placement pitch angle control of a flapping-wing flying robot

An adaptive pole placement control scheme for the adaptive pitch angle control of a bird-like flapping-wing flying robot is designed and implemented. The salient aims of this work are notably twofold: first, since the dynamics of bird-like flapping-wing robots are still not well understood and hence obfuscate the process of deriving a high-fidelity aerodynamical model, we instead elect to designate the system identification component of the control scheme to provide real-time estimates of the low level robot parameters. Input and output data are collated during flight and the recursive least squares method is applied to obtain real-time parameter estimates. Estimated parameters are subsequently used in designing the control law using adaptive pole placement via the polynomial method where we prescribe the desired closed-loop characteristic equation. Secondly, even if the dynamics of the robot varies over time, it is accounted by the adaptive controller without any need to perform tuning since proportional gain values are spontaneously generated. Numerical simulations are first used to assist the design and validate the correct operation of the control scheme. It is then implemented on a real bird-like flapping-wing flying robot; experimental results obtained exhibit close congruence with simulation results.     

A finite element formulation for dynamic parameter identification of robot manipulators

In this paper a finite element based approach is described for the automatic generation of models suitable for dynamic parameter identification. The method involves a nonlinear finite element formulation in which both links and joints are considered as specific finite elements [6, 7]. Since the identification procedure considers rigid-link robot models, the inertial properties of the link elements are described using a lumped mass formulation. The parameters to be identified are masses, first-order moments and inertial tensor components of the links. The equations of motion are written in a form which is linear in the dynamic parameters. This formulation is obtained by employing Jourdain’s principle of virtual power. The parameters are estimated using a linear least squares technique. Singular value decomposition of the regression matrix is used to find the minimum parameter set. Simulation results obtained from the 6 DOF PUMA 560 robot based on the estimated parameters show that the method yields accurate responses.     

受限机器人神经形态的变结构位置/力控制

本文基于Ｊｅａｎ和Ｆｕ（１９９３）建立的受限机器人模型的降型阶形式，利用变结构系统理论，设计了具有未知动态的受限机器人轨道／力追踪控制，提出的学习方法仅仅利用了机器人动态模型的一般结构，不需要其精确信息，计算迅速，易于实现，仿真结果验证了提出的方法的有效性。     

Query based model learning and stable tracking of a robot arm using radial basis function network

In the context of a robot manipulator, a generalized neural emulator over the complete workspace is very difficult to obtain because of dimensionally insufficient training data. A query based learning algorithm is proposed in this paper that can generate new examples where control inputs are independent of states of the system. This algorithm is centered around the concept of network inversion using an extended Kalman filtering based algorithm. This is a novel idea since robot manipulator is an open loop unstable system and generation of control input independent of state is a research issue for neural model identification. Two trajectory independent stable control schemes have been designed using the neural emulator. One of the control schemes uses forward-inverse-modeling approach to update the controller parameters adaptively following Lyapunov function synthesis technique. The proposed scheme is trajectory independent unlike the back-propagation scheme. The second type of controller predicts the minimum variance estimate of control action using recall process (network inversion) and the control law is derived following a Lyapunov function synthesis approach so that the closed loop system consisting of controller and neural emulator remains stable. The simulation experiments show that the model validation approach is efficient and the proposed control schemes guarantee stable accurate tracking.     

Parallel genetic programming: Synchronous and asynchronous migration

Genetic programming (GP) has been used successfully as a technique for constructing robot control programs. Depending on the number of evaluations and the cost of each evaluation however, GP may require a substantial amount of processing time to find a feasible solution. The advent of parallel GP has brought the execution time of GP to a more acceptable level. This paper investigates parallel GP with a mobile robot navigation problem. The parallel implementations are based on a coarse-grained model. A technique for distributing the task of serial GP is proposed. In particular, this technique shows that the total amount of work can be reduced while maintaining the quality of the solutions. Asynchronous and synchronous implementations are examined. We compare the performance in terms of both the solution quality and the execution time. The timing analysis is investigated to give an insight into the behavior of parallel implementations. The results show that the parallel algorithm with asynchronous migration using 10 processors is 33 times faster than the serial algorithm. This work was presented in part at the 5th International Symposium on Artificial Life and Robotics, Oita, Japan, January 26–28, 2000.     

Kinematic and dynamic identification of parallel mechanisms

In this paper, we provide a comprehensive method to perform the physical model identification of parallel mechanisms. This includes both the kinematic identification using vision and the identification of the dynamic parameters. A careful attention is given to the issues of identifiability and excitation. Experimental results obtained on a H4 parallel robot show that kinematic identification yields an improvement in the static positioning accuracy from some 1 cm down to 1 mm, and that dynamic parameters are globally estimated with less than 10% relative error yielding a similar error on the control torque estimation.     

神经网络并联辨识算法的收敛性研究

神经网络可用来建立非线性动态系统的模型，其辨识模型可分为串联并联辨识模型和并联辨识模型两种，后者的思路源于基于参考模型自适应方案的输出误差辨识模型，对观测扰动有较强的抑制能力。本文对这种神经网络并联辨识结构的收敛性进行了研究，指出在网络参数满足一定条件时并联预测过程收敛，且并联辨识算法具有局部收敛性，仿真实验验证了上述结论。     

Design of a sliding mode controller for an automatic guided vehicle and its implementation

In this paper, a control scheme that combines a kinematic controller and a sliding mode dynamic controller with external disturbances is proposed for an automatic guided vehicle to track a desired trajectory with a specified constant velocity. It provides a method of taking into account specific mobile robot dynamics to convert desired velocity control inputs into torques for the actual mobile robot. First, velocity control inputs are designed for the kinematic controller to make the tracking error vector asymptotically stable. Then, a sliding mode dynamic controller is designed such that the mobile robot’s velocities converge to the velocity control inputs. The control law is obtained based on the backstepping technique. System stability is proved using the Lyapunov stability theory. In addition, a scheme for measuring the errors using a USB camera is described. The simulation and experimental results are presented to illustrate the effectiveness of the proposed controller.     

On the Experiment Design for Direct Dynamic Parameter Identification of Parallel Robots

Accurate robot dynamic models require the estimation and validation of the dynamic parameters through experiments. To this end, when performing the experiments, the system has to be properly excited so that the unknown parameters can be accurately estimated. The experiment design basically consists of optimizing the trajectory executed by the robot during the experiment. Due to the restricted workspace with parallel robots this task is more challenging than for serial robots; thus, this paper is focused on the experiment design aimed at dynamic parameter identification of parallel robots. Moreover, a multicriteria algorithm is proposed in order to reduce the deficiencies derived from the single-criterion optimization. The results of the identification using trajectories based on a single criterion and the multicriteria approaches are compared, showing that the proposed optimization can be considered as a suitable procedure for designing exciting trajectories for parameter identification.