Neural network-based control of flexible-link manipulators

The problem of modeling and control of flexible-link manipulators has received much attention in the past several years. There are a number of potential advantages arising from the use of light-weight flexible-link manipulators, such as faster operation, lower energy consumption, and higher load-carrying capacity. However, structural flexibility causes many difficulties in modeling the manipulator dynamics and guaranteeing stable and efficient motion of the manipulator end-effector. Control difficulties are mainly due to the non-colocated nature of the sensor and actuator position, which results in unstable zero dynamics. Further complications arise because of the highly nonlinear nature of the system and the difficulty involved in accurately modeling various friction and backlash terms. Control strategies that ignore these problems generally fail to provide satisfactory closed-loop performance. This dissertation presents experimental evaluation on the performance of neural network-based controllers for tip position tracking of flexible-link manipulators. The controllers are designed by utilizing the output redefinition approach to overcome the problem caused by the non-minimum phase characteristic of the flexible-link system. Four different neural network schemes are proposed. The first two schemes are developed by using a modified version of the "feedback-error-learning" approach to learn the inverse dynamics of the flexible manipulator. The neural networks are trained and employed as online controllers. Both schemes require only a linear model of the system for defining the new outputs and for designing conventional PD-type controllers. This assumption is relaxed in the third and fourth schemes. In the third scheme, the controller is designed based on tracking the hub position while controlling the elastic deflection at the tip. In the fourth scheme which employs two neural networks, the first network (referred to as the output neural network) is responsible for specifying an appropriate output for ensuring minimum phase behavior of the system. The second neural network is responsible for implementing an inverse dynamics controller. Both networks are trained online. Finally, the four proposed neural network controllers are implemented oil a single flexible-link experimental test-bed. Experimental and simulation results are presented to illustrate the advantages and improved performance of the proposed tip position tracking controllers over the conventional PD-type controllers in the presence of unmodeled dynamics such as hub friction and stiction and payload variations

A neural network-based controller for a single-link flexible manipulator using the inverse dynamics approach

This thesis presents an intelligent strategy for controlling the tip position of a flexible-link manipulator. Motivated by the well-known inverse dynamics control approach for rigid-link manipulators, two multi-layer feedforward neural networks are developed to learn the nonlinearities of the system dynamics. The re-defined output scheme is used by feeding back this output to guarantee the minimum phase behavior of the resulting closed-loop system. No a priori knowledge about the nonlinearities of the system is needed where the payload mass is also assumed to be unknown. The weights of the networks are adjusted using a modified on-line error backpropagation algorithm that is based on the propagation of the redefined output error, derivative of this error and the tip deflection of the manipulator. Numerical simulations as well as real-time controller implementation on an experimental setup are carried out. The results achieved by the proposed neural network-based controller are compared in simulations and experimentally with conventional PD-type and inverse dynamics controls to substantiate and demonstrate the advantages and the promising potentials of this scheme

Multiobjective control of a four-link flexible manipulator: A robust H∞ approach

Copyright [2002] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This paper presents an approach to robust H∞ control of a real multilink flexible manipulator via regional pole assignment. We first show that the manipulator system can be approximated by a linear continuous uncertain model with exogenous disturbance input. The uncertainty occurring in an operating space is assumed to be norm-bounded and enter into both the system and control matrices. Then, a multiobjective simultaneous realization problem is studied. The purpose of this problem is to design a state feedback controller such that, for all admissible parameter uncertainties, the closed-loop system simultaneously satisfies both the prespecified H∞ norm constraint on the transfer function from the disturbance input to the system output and the prespecified circular pole constraint on the closed-loop system matrix. An algebraic parameterized approach is developed to characterize the existence conditions as well as the analytical expression of the desired controllers. Third, by comparing with the traditional linear quadratic regulator control method in the sense of robustness and tracking precision, we provide both the simulation and experimental results to demonstrate the effectiveness and advantages of the proposed approach

Data-driven mode shape selection and model-based vibration suppression of 3-RRR parallel manipulator with flexible actuation links

The mode shape function is difficult to determine in modeling manipulators with flexible links using the assumed mode method. In this paper, for a planar 3-RRR parallel manipulator with flexible actuation links, we provide a data-driven method to identify the mode shape of the flexible links and propose a model-based controller for the vibration suppression. By deriving the inverse kinematics of the studied mechanism in analytical form, the dynamic model is established by using the assumed mode method. To select the mode shape function, the software of multi-body system dynamics is used to simulate the dynamic behavior of the mechanism, and then the data-driven method which combines the DMD and SINDy algorithms is employed to identify the reasonable mode shape functions for the flexible links. To suppress the vibration of the flexible links, a state observer for the end-effector is constructed by a neural network, and the model-based control law is designed on this basis. In comparison with the model-free controller, the proposed controller with developed dynamic model has promising performance in terms of tracking accuracy and vibration suppression

Parametric motion control of robotic arms: A biologically based approach using neural networks

A neural network based system is presented which is able to generate point-to-point movements of robotic manipulators. The foundation of this approach is the use of prototypical control torque signals which are defined by a set of parameters. The parameter set is used for scaling and shaping of these prototypical torque signals to effect a desired outcome of the system. This approach is based on neurophysiological findings that the central nervous system stores generalized cognitive representations of movements called synergies, schemas, or motor programs. It has been proposed that these motor programs may be stored as torque-time functions in central pattern generators which can be scaled with appropriate time and magnitude parameters. The central pattern generators use these parameters to generate stereotypical torque-time profiles, which are then sent to the joint actuators. Hence, only a small number of parameters need to be determined for each point-to-point movement instead of the entire torque-time trajectory. This same principle is implemented for controlling the joint torques of robotic manipulators where a neural network is used to identify the relationship between the task requirements and the torque parameters. Movements are specified by the initial robot position in joint coordinates and the desired final end-effector position in Cartesian coordinates. This information is provided to the neural network which calculates six torque parameters for a two-link system. The prototypical torque profiles (one per joint) are then scaled by those parameters. After appropriate training of the network, our parametric control design allowed the reproduction of a trained set of movements with relatively high accuracy, and the production of previously untrained movements with comparable accuracy. We conclude that our approach was successful in discriminating between trained movements and in generalizing to untrained movements

Integral Resonant Control for vibration damping and precise tip-positioning of a single-link flexible manipulator

Peer reviewedPostprin

The dynamic control of robotic manipulators in space

Described briefly is the work done during the first half year of a three-year study on dynamic control of robotic manipulators in space. The research focused on issues for advanced control of space manipulators including practical issues and new applications for the Virtual Manipulator. In addition, the development of simulations and graphics software for space manipulators, begun during the first NASA proposal in the area, has continued. The fabrication of the Vehicle Emulator System (VES) is completed and control algorithms are in process of development