Neuro-fuzzy modelling and control of robotic manipulators

The work reported in this thesis aims to design and develop a new neuro-fuzzy control system for robotic manipulators using Machine Learning Techniques, Fuzzy Logic Controllers, and Fuzzy Neural Networks. The main idea is to integrate these intelligent techniques to develop an adaptive position controller for robotic manipulators. This will finally lead to utilising one or two coordinated manipulators to perform upper-limb rehabilitation. The main target is to benefit from these intelligent techniques in a systematic way that leads to an efficient control and coordination system. The suggested control system possesses self-learning features so that it can maintain acceptable performance in the presence of uncertain loads. Simulation and modelling stages were performed using dynamical virtual reality programs to demonstrate the ideas of the control and coordination techniques. The first part of the thesis focuses on the development of neuro-fuzzy models that meet the above requirement of mimicking both kinematics and dynamics behaviour of the manipulator. For this purpose, an initial stage for data collection from the motion of the manipulator along random trajectories was performed. These data were then compacted with the help of inductive learning techniques into two sets of if-then rules that form approximation for both of the inverse kinematics and inverse dynamics of the manipulator. These rules were then used in fuzzy neural networks with differentiation characteristics to achieve online tuning of the network adjustable parameters. The second part of the thesis introduces the proposed adaptive neuro-fuzzy joint-based controller. To achieve this target, a feedback Fuzzy-Proportional-Integral-Derivative incremental controller was developed. This controller was then applied as a joint servo-controller for each robot link in addition to the main neuro-fuzzy feedforward controller used to compensate for the dynamics interactions between robot links. A feedback error learning scheme was applied to tune the feedforward neuro-fuzzy controller online using the error back-propagation algorithm. The third part of the thesis presents a neuro-fuzzy Cartesian internal model control system for robotic manipulators. The neuro-fuzzy inverse kinematics model of the manipulator was used in addition to the joint-based controller proposed and the forward mathematical model of the manipulator in an adaptive internal model controller structure. Feedback-error learning scheme was extended to tune both of the joint-based neuro-fuzzy controller and the neuro-fuzzy internal model controller online. The fourth part of the thesis suggests a simple fuzzy hysteresis coordination scheme for two position-controlled robot manipulators. The coordination scheme is based on maintaining certain kinematic relationships between the two manipulators using reference motion synchronisation without explicitly involving the hybrid position/force control or modifying the existing controller structure for either of the manipulators. The key to the success of the new method is to ensure that each manipulator is capable of tracking its own desired trajectory using its own position controller, while synchronizing its motion with the other manipulator motion so that the differential position error between the two manipulators is reduced to zero or kept within acceptable limits. A simplified test-bench emulating upper-limb rehabilitation was used to test the proposed coordination technique experimentally

Fuzzy logic control of telerobot manipulators

Telerobot systems for advanced applications will require manipulators with redundant 'degrees of freedom' (DOF) that are capable of adapting manipulator configurations to avoid obstacles while achieving the user specified goal. Conventional methods for control of manipulators (based on solution of the inverse kinematics) cannot be easily extended to these situations. Fuzzy logic control offers a possible solution to these needs. A current research program at SRI developed a fuzzy logic controller for a redundant, 4 DOF, planar manipulator. The manipulator end point trajectory can be specified by either a computer program (robot mode) or by manual input (teleoperator). The approach used expresses end-point error and the location of manipulator joints as fuzzy variables. Joint motions are determined by a fuzzy rule set without requiring solution of the inverse kinematics. Additional rules for sensor data, obstacle avoidance and preferred manipulator configuration, e.g., 'righty' or 'lefty', are easily accommodated. The procedure used to generate the fuzzy rules can be extended to higher DOF systems

Using multiple sensors for printed circuit board insertion

As more and more activities are performed in space, there will be a greater demand placed on the information handling capacity of people who are to direct and accomplish these tasks. A promising alternative to full-time human involvement is the use of semi-autonomous, intelligent robot systems. To automate tasks such as assembly, disassembly, repair and maintenance, the issues presented by environmental uncertainties need to be addressed. These uncertainties are introduced by variations in the computed position of the robot at different locations in its work envelope, variations in part positioning, and tolerances of part dimensions. As a result, the robot system may not be able to accomplish the desired task without the help of sensor feedback. Measurements on the environment allow real time corrections to be made to the process. A design and implementation of an intelligent robot system which inserts printed circuit boards into a card cage are presented. Intelligent behavior is accomplished by coupling the task execution sequence with information derived from three different sensors: an overhead three-dimensional vision system, a fingertip infrared sensor, and a six degree of freedom wrist-mounted force/torque sensor

Optimizing Membership Function Tuning for Fuzzy Control of Robotic Manipulators Using PID-Driven Data Techniques

In this study, a method for optimizing membership function tuning for fuzzy control of robotic manipulators using PID-driven data techniques is presented. Traditional approaches for designing membership functions in fuzzy control systems often rely on the experience and knowledge of the system designer, which can lead to suboptimal performance. By utilizing data collected from a PID control system, the proposed method aims to enhance the precision and controllability of robotic manipulators through improved fuzzy logic control. A Mamdani-type fuzzy logic controller was developed and its performance was simulated in Simulink, demonstrating the effectiveness of the proposed optimization technique. The results indicate that the method can outperform conventional P control systems in terms of overshoot reduction while maintaining comparable transient response specifications. This research highlights the potential of the PID-driven data-based approach for optimizing membership function tuning in fuzzy control systems and offers valuable insights for the development and evaluation of fuzzy logic control in robotic manipulators. Future work may focus on further optimization of the tuning process, evaluation of system robustness under various operating conditions, and exploring the integration of other artificial intelligence techniques for improved control performance

Control strategies for robotic manipulators

This survey is aimed at presenting the major robust control strategies for rigid robot manipulators. The techniques discussed are feedback linearization/Computed torque control, Variable structure compensator, Passivity based approach and Disturbance observer based control. The first one is based on complete dynamic model of a robot. It results in simple linear control which offers guaranteed stability. Variable structure compensator uses a switching/relay action to overcome dynamic uncertainties and disturbances. Passivity based controller make use of passive structure of a robot. If passivity of a feedback system is proved, nonlinearities and uncertainties will not affect the stability. Disturbance observer based controllers estimate disturbances, which can be cancelled out to achieve a nominal model, for which a simple controller can then be designed. This paper, after explaining each control strategy in detail, finally compares these strategies for their pros and cons. Possible solutions to cope with the drawbacks have also been presented in tabular form. © 2012 IEEE

Intelligent active force control of a three-link manipulator using fuzzy logic

The paper presents a novel approach to estimate the inertia matrix of a robot arm using a fuzzy logic (FL) mechanism in order to trigger the active force control (AFC) strategy. A comprehensive study is performed on a rigid three-link manipulator subjected to a number of external disturbances. The robustness and effectiveness of the proposed control scheme are investigated considering the trajectory track performance of the robotic arm taking into account the application of external disturbances and that the arm is commanded to describe a reference trajectory given a number of initial and operating conditions. The results show that the FL mechanism used in the study successfully computes appropriate estimated inertia matrix value to execute the control action. The proposed scheme exhibits a high degree of robustness and accuracy as the track error is bounded within an acceptable range of value even under the influence of the introduced disturbances