Motion control of robot manipulators under sensor failure

Real-time motion planning and control of multi-joint robot manipulators are studied using neural networks. The proposed neural network approach consists of two modules: one for real-time collision-free motion planning, the other for real-time fine control of the robot manipulators. The motion planning module is a biologically inspired, parallel connected, topologically organised neural network, where each neuron is characterised by a shunting equation, whose state space is the robot configuration joint space. This module is capable of planning a real-time optimal path for robot manipulators through the dynamic activity landscape of the neural network. The motion control module involves of a feedforward neural network together with a traditional PD feedback loop. It is capable of achieving real-time fine motion control of robot manipulators under significant uncertainties and without any prior knowledge of the robot dynamics. This module can quickly compensate sudden changes in the robot dynamics. The real-time fine control of robot manipulators is achieved through the online learning of the neural network. Both the motion planning and motion control modules are computationally efficient, and their global stabilities are proved using Lyapunov stability theory.

Various advanced control strategies are applied to a direct-drive SCARA robot and studied in computer simulations. Besides computed torque control and direct adaptive control, heuristic optimal control, a new path control scheme for robotic manipulators, is included in the comparison study. PD control, the traditional robot control method, is used for generating a comparing baseline. While all schemes are applied for the same tracking task, the effect of modelling errors and measurement noise is considered in robot performance evaluation. Simulation results show that (1) without model errors, all advanced control schemes can achieve higher tracking accuracy than PD control; (2) with a random measurement error of 1%, computed torque and direct adaptive control methods are inferior to PD control; (3) heuristic control proves to be the most robust control scheme in case of mixed model and measurement errors.

Robotics has been a paradigm for science in the last few decades. At first, scientists’ efforts were devoted to the solution of the problem of planning and control of themotion of robot manipulators. However, the motion control of robot manipulators in unstructured environments is today an attractive scientific problem. An interesting solution for motion control is the use of sensor information, such as computer vision, in the system’s feedback. Several works and tools have been developed in recent years in this field (Corke; 2005), (Chaumette and Hutchinson; 2006). The more typical approaches consider visual perception for servoing and for the so called look and move (Hutchinson et al.; 1996). Visual servoing (Kelly et al.; 2000) can be classified into two approaches: camera-in-hand or camera-to-hand (Flandin et al.; 2000). In camera-to-hand robotic systems, multiple cameras or a single camera fixed in the worldcoordinate frame capture images of both, the robot and its environment. The tracking of the object with visual feedback can be made in 2D or 3D. An interesting solution for the visual servoing of camera-in hand robot manipulators in 2D can be found in e. g. (Bonfe et al.; 2002) and (Hernandez et al.; 2008) where stability demonstration of a decoupled controller have been presented. For 3D tracking some solutions reported are look and move controller, with one camera (Sim et al.; 2002) or more than one camera (Xie et al.; 2005). A 3D visual servoing with stability analysis in continuous time is presented by (Hernandez et al.; 2008a) and (Kelly et al.; 2006) present a direct visual servoing with transpose Jacobian control technique for regulation of robot manipulators in the 3D Cartesian space. In a similar vein (Enescu et al.; 2006) present mobile robot navigation for person tracking using a stereo head-camera. In this chapter, we consider the control problem of camera-in-hand robot manipulators in 2D and 3D. In both cases only one camera is mounted on the robot’s arm, which supplies visual information of the environment, with the aim of moving the manipulator by maintaining the image of the tracked object (a sphere) in the centre of the image plane, despite the possible movements of the object. In the 3D control the constant radius is used as a feature too. In this work, the proposed control system considers two loops in cascade, an internal loop solving the robots’ joint control, and an external loop implementing a dynamic look and move visual controller. A stability analysis in discrete time is developed under the conditions that it is possible to approximate the dynamic effect of the internal loop as an external loop time delay (Corke; 1996) and (Bonfe et al.; 2002). The more classic presentations of servovisual control are velocity controllers, based in the term feature Jacobian,(Chaumette and Hutchinson; 2006) 2

Inspite of so much research, key technical problem, naming chattering of conventional, simple and robust SMC is still a challenge to the researchers and hence limits its practical application. However, newly developed soft computing based techniques can provide solution. In order to have advantages of conventional and heuristic soft computing based control techniques, in this paper various commonly used intelligent techniques, neural network, fuzzy logic and adaptive neuro fuzzy inference system (ANFIS) have been combined with sliding mode controller (SMC). For validation, proposed hybrid control schemes have been implemented for tracking a predefined trajectory by robotic manipulator, incorporating structured and unstructured uncertainties in the system. After reviewing numerous papers, all the commonly occurring uncertainties like continuous disturbance, uniform random white noise, static friction like coulomb friction and viscous friction, dynamic friction like Dhal friction and LuGre friction have been inserted in the system. Various performance indices like norm of tracking error, chattering in control input, norm of input torque, disturbance rejection, chattering rejection have been used. Comparative results show that with almost eliminated chattering the intelligent SMC controllers are found to be more efficient over simple SMC. It has also been observed from results that ANFIS based controller has the best tracking performance with the reduced burden on the system. No paper in the literature has found to have all these structured and unstructured uncertainties together for motion control of robotic manipulator.

Observer-Based Adaptive Sliding Control of Robotic Manipulators

In this article we present the stability analysis of a class of PD-type controllers for position and motion control of robot manipulators. The main feature of this class of controllers is that the proportional and derivative gains can be nonlinear functions of the robot states. These controllers can be obtained from control strategies that adjust the controller gains depending on the robot states. It is shown that global asymptotic stability of the control system is achieved provided that the P and D gains have suitable structure. As an outcome, we propose a global regulator constrained to deliver torques within prescribed limits of the actuator's capabilities. Experimental results on a two degrees of freedom direct drive arm show the usefulness of the proposed scheme. © 1996 John Wiley & Sons, Inc.

Gross motion control of robotic manipulators typically requires significant on‐line computations to compensate for nonlinear dynamics due to gravity, Coriolis, centripetal, and friction nonlinearities. One controller proposed by Luo and Saridis avoids these computations by feeding back joint acceleration and torque. This study implements the controller on a Puma 600 robotic manipulator. Joint acceleration measurement is obtained by measuring linear accelerations of each joint, and deriving a computationally efficient transformation from the linear measurements to the angular accelerations. Torque feedback is obtained by using the previous torque sent to the joints. The implementation has stability problems on the Puma 600 due to the extremely high gains inherent in the feedback structure. Since these high gains excite frequency modes in the Puma 600, the algorithm is modified to decrease the gain inherent in the feedback structure. The resulting compensator is stable and insensitive to high frequency unmodeled dynamics. Moreover, a second compensator is proposed which uses acceleration and torque feedback, but still allows nonlinear terms to be fed forward. Thus, by feeding the increment in the easily calculated gravity terms forward, improved responses are obtained. Both proposed compensators are implemented, and the real time results are compared to those obtained with the computed torque algorithm.

We present a new technique to the design and real-time implementation of fuzzy control system basedon digital signal processors in order to improve the precision and robustness for system of industrial robot in this paper. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C80 is used in implementing real time fuzzy control to provide an enhanced motion control for robot manipulators. In this paper, a Self-Organizing Fuzzy Controller for the industrial robot manipulator with a actuator located at the base is studied. A fuzzy logic composed of linguistic conditional statements is employed by defining the relations of input-output variables of the controller. In the synthesis of a Fuzzy Logic Controller, one of the most difficult problems is the determination of linguistic control rules from the human operators. To overcome this difficult Self-Organizing Fuzzy Controller is proposed for a hierarchical control structure consisting of basic and high levels that modify control rules. The proposed Self-Organizing Fuzzy Controller scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the SOFC is illustrated by simulation and experimental results for a Dual-Arm robot with eight joints.

Robust motion and force control of constrained manipulators by learning

A Neural Networks-Based Feedback Robust Adaptive Controller for Robots

This paper studies the coordinated motion control of robotic manipulators driven by single-rod hydraulic actuators. We present a physical model based adaptive robust control (ARC) strategy to explicitly take into account the strong coupling among various hydraulic cylinders (or joints). The property that the adjoint matrix and determinant of the inertial matrix could be linearly parametrized by certain suitably selected parameters is fully exploited and the overparametrizing method is used. Furthermore, the proposed ARC controller guarantees a prescribed output tracking transient performance and final tracking accuracy while achieving asymptotic output tracking in the presence of parametric uncertainties only. Simulation and experimental results on a three degree-of-freedom hydraulic robot arm are presented to illustrate the proposed control algorithm.

The authors propose a general robust servosystem design method based on the two-degrees-of-freedom (TDOF) controllers. With this approach, it is possible to specify the command input response to an arbitrary reference model while keeping a strong robustness to system variations and a high suppression performance with respect to disturbances. These can be designed independently of the command input response. The TDOF method is generalized to unstable plants, and two types of practical parameterizations for the TDOF controller are proposed in order to design the speed and position servosystems, respectively. Their excellent control performances have been confirmed by experimental results. The application of one of the proposed servocontrollers to the motion control of robotic manipulators is described. >

Robotic manipulators are complex mechanical systems that exhibit highly nonlinear dynamics and subject to various forms of disturbances such as friction, external forces and other unmodelled dynamics. Mathematical models representing robot dynamics are extensively used for design, simulation and control purposes, and can be derived through analytical and experimental methods. Dynamic behaviours predicted by mathematical models often deviate from the observed dynamics of robot manipulators because of external disturbances, parametric uncertainties, and unmodelled dynamics. The observer‐based control that eliminates the need for highly accurate system modelling is an appealing approach for robot control. This paper introduces an extended state observer‐based structure that can be either utilized as a stand‐alone controller or implemented within a model‐based adaptive control scheme. The proposed control scheme allows the implementation of extended state observers independently of the availability and quality of the dynamic model. The stability of the proposed controllers in presence of model uncertainties and generalized disturbances is investigated through the Lyapunov analysis. The experiments performed on a six‐DoF industrial robot validate the theoretical stability results. Evaluation of performances of the proposed controllers in various operating conditions are presented in a comparative manner. Experimental results show that the extended state observer‐based controller outperforms the adaptive controller in trajectory tracking performances.

Motion Control for Robot Manipulators with Flexible Joints

Fine motion control of robotic manipulators has become a desired goal in the last few years as a result of new robot morphology and the definition of new tasks involving high velocity motions and end effector tracking precision. In order to achieve better performance of robotic manipulators the artificial intelligence can be introduced into the control system. One way for accomplishing this is application of neural nets. Main advantage claimed for neural based controllers is their ability to learn and generalize from partial data and ability to perform parall processing. Many papers present possibilities of neural nets application for control [Hunt,92],[Sontag 93],[Anstaklis,92]. They have discussed the use in adaptive control, predictive control, optimal control, gain sheduling in PID with variable gain. A large area of neural nets application for control is control of nonlinear systems. Neural nets can be applied as a forward or inverse model of manipulator. Such neural net can learn a behaviour of the real system, without knowledge about robot’s model structure.