A Neural Networks-Based Feedback Robust Adaptive Controller for Robots

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.

Presents an approach and a systematic design methodology to adaptive motion control based on neural networks (NNs) for high-performance robot manipulators, for which stability conditions and performance evaluation are given. The neurocontroller includes a linear combination of a set of off-line trained NNs, and an update law of the linear combination coefficients to adjust robot dynamics and payload uncertain parameters. A procedure is presented to select the learning conditions for each NN in the bank. The proposed scheme, based on fixed NNs, is computationally more efficient than the case of using the learning capabilities of the neural network to be adapted, as that used in feedback architectures that need to propagate back control errors through the model to adjust the neurocontroller. A practical stability result for the neurocontrol system is given. That is, we prove that the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the NN bank and the design parameters of the controller. In addition, a robust adaptive controller to NN learning errors is proposed, using a sign or saturation switching function in the control law, which leads to global asymptotic stability and zero convergence of control errors. Simulation results showing the practical feasibility and performance of the proposed approach to robotics are given.

In a robot control system there are dynamics and sensor faults, modeling errors, and system and measurement noises. This paper addresses several techniques to control the robot manipulator under the uncertainty model and sensor failure. First, a neural network is proposed to control the robot to follow the generated path. This network model is able to compensate the structured and unstructured dynamic uncertainties of the robot by using both online and off-line training. The PD controller, computed torque controller and adaptive controller with and without exact model are used to control the robot and compared with the proposed method. Next, a residual generator for detection and isolation of the sensor failure is designed. Assuming one fault at a time, the proposed method can detect and isolate the fault sensor. The simulation study based on a 2-DOF planar robot is included.

This paper presents a robust adaptive fuzzy neural controller (AFNC) suitable for motion control of multilink robot manipulators. The proposed controller has the following salient features: (1) self-organizing fuzzy neural structure, i.e., fuzzy control rules can be generated or deleted automatically according to their significance to the control system and the complexity of the mapped system and no predefined fuzzy rules are required; (2) fast online learning ability, i.e., no prescribed training models are needed for online learning and weights of the fuzzy neural controller are modified without any iterations; (3) fast convergence of tracking errors, i.e., manipulator joints can track the desired trajectories very quickly; (4) adaptive control, i.e., structure and parameters of the AFNC can be self-adaptive in the presence of disturbances to maintain high control performance; and (5) robust control, where asymptotic stability of the control system is established using the Lyapunov theorem. Experimental evaluation conducted on an industrial selectively compliant assembly robot arm demonstrates that excellent tracking performance can be achieved under time-varying conditions.

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.

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.

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

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.

Robust motion and force control of constrained manipulators by learning

Brain–machine interface control of a manipulator using small-world neural network and shared control strategy

In this paper, a neural network based adaptive state feedback control scheme is proposed to solve the problem of trajectory tracking in the joint space for robotic manipulators with the presence of high uncertainty in the system parameters. First nonlinear behavior of the robot is approximately eliminated by applying a linearizing control, the closed loop dynamics is stabilized using static compensator. Then the parametric uncertainty is parameterized and eliminated using a single hidden layer neural network. The adaptation law for the NN and the boundedness of the error signals are proved from Lyapunov theorem of stability. The performance of the proposed controller is validated on two-link manipulator and demonstrated through computer simulation.

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

Presents an approach to the design and real-time implementation of an adaptive controller for a robotic manipulator based on digital signal processors. The Texas Instruments DSP (TMS320C31) chips are used in implementing real-time adaptive control algorithms to provide enhanced motion control performance for robotic manipulators. In the proposed scheme, adaptation laws are derived from the direct model reference adaptive control principle based on the improved Lyapunov second method. The proposed adaptive controller consists of an adaptive feedforward and feedback controller and PI-type time-varying auxiliary control elements. The proposed control scheme is simple in structure, fast in computation, and suitable for real-time control. Moreover, this scheme does not require any accurate dynamic modeling nor values of manipulator parameters and payload. Performance of the proposed adaptive controller is illustrated by simulation and experimental results for an industrial robot with four joints in the joint space and Cartesian space.

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. >