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Fahmy, Ashraf
Languages: English
Types: Doctoral thesis
Subjects: TA
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.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 2.1. FLS Basic Structure and Design Elements 2.1.1. Fuzzification Process 2.1.2. Knowledge Base Data Base Rule Base Choice of the FLS Input/output Variables Derivation of the Fuzzy Rules Functional Implementation of Fuzzy Rules 2.1.3. Decision Making Logic FLS Inference Strategies FLS Inference Mechanisms 2.1.4. Defuzzification Strategies 2.1.5. Models o f FLS
    • 2.2. Fuzzy Neural Networks (FNN) 2.2.1. Feedforward Fuzzy Neural Networks (FFNN) Mamdani-Model-Based FFNN TS-Model-Based FFNN 2.2.2. Recurrent Fuzzy Neural Networks (RFNN) 2.2.3. Self-Organising Fuzzy Neural Networks (SOFNN) 2.2.4. Learning in FFNN Supervised Learning Reinforcement Learning
    • 2.3. Applications o f FLS and FNN in Modelling and Control
    • 2.4. Applications o f FLS and FNN in Robotic Systems Modelling
    • 2.5. Applications o f FLS and FNN in Robotic Systems Control 2.5.1. Conventional Control o f Robotic Manipulators 2.5.2. Fuzzy Control o f Robotic Manipulators 2.5.3. Adaptive Control o f Robotic Manipulators 2.5.4. Internal Model Control o f Robotic Manipulators
    • 2.6. Applications o f FLS and FNN in Robotic Systems Coordination
    • 2.7. Summary Formation o f a Rule Rule Post Processing 3.3.3. Inverse Kinematics and Inverse Dynamics Rules
    • 3.4. Proposed Neuro-Fuzzy Network (DYNAFUZZNN) 3.4.1. Softmin and Softmax Functions 3.4.2. DYNAFUZZNN Proposed Neuro-Fuzzy Network Structure 3.4.3. Neuro-Fuzzy Network Parameters Tuning
    • 3.5. Puma 560® Manipulator Inverse Modelling Results
    • 3.6. Summary
    • 4.1. Proposed Controller Structure 4.1.1. Forward Path Neuro-Fuzzy Controller 4.1.2. Feedback Path Fuzzy-PID-like Incremental Servo Controller 4.1.3. Design Procedures for Fuzzy-PID-like Incremental Controller Fuzzy Proportional Control Element Fuzzy Derivative Control Element Fuzzy Incremental Integral Control Element
    • 4.2. Feedback-Error Learning Scheme
    • 4.3. Comparison Study o f the Results
    • 4.4. Summary 5.4.1. Disturbance Analysis 5.4.2. Sensitivity Analysis Sensitivity to Multiplicative Uncertainties Sensitivity to Additive Uncertainties
    • 5.5. Simulation Results
    • 5.6. Application to Upper-Limb Rehabilitation 5.6.1. Robotized Upper-Limb Rehabilitation 5.6.2. Human Upper-Limb Dynamics Model 5.6.3. Upper-Limb Rehabilitation Using One Robot Manipulator
    • 5.7. Summary
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  • Discovered through pilot similarity algorithms. Send us your feedback.

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