The control of a manipulator using cerebellar model articulation controllers

Thesis (Master)--Izmir Institute of Technology, Mechanical Engineering, Izmir, 2003Includes bibliographical references (leaves: 72-74)Text in English; Abstract: Turkish and Englishviii, 91 leavesThe emergence of the theory of artificial neural networks has made it possible to develop neural learning schemes that can be used to obtain alternative solutions to complex problems such as inverse kinematic control for robotic systems. The cerebellar model articulation controller (CMAC) is a neural network topology commonly used in the field of robotic control which was formulated in the 1970s by Albus. In this thesis, CMAC neural networks are analyzed in detail. Optimum network parameters and training techniques are discussed. The relationship between CMAC network parameters and training techniques are presented. An appropriate CMAC network is designed for the inverse kinematic control of a two-link robot manipulator

Neural Network Based Vibration Control of Seismically Excited Civil Structures

This study proposes a neural network based vibration control system designed to attenuate structural vibrations induced by an earthquake. Classical feedback control algorithms are susceptible to parameter changes. For structures with uncertain parameters they can even cause instability problems. The proposed neural network based control system can identify the structural properties of the system and avoids the above mentioned problems. In the present study it is assumed that a full state of the structure is known, which means the at each floor horizontal displacements and rotations about the vertical axis are measured. Additionally, it is assumed the acceleration signal coming from the earthquake is also available. The proposed neural control strategy is compared with the classical linear quadratic regulator (LQR) not only in terms of displacement responses, but also required control forces. Moreover, the influence of different weighting matrices on performance of the proposed control strategy has been presented.The effectiveness of the neuro-controller has been demonstrated on two numerical examples: a simple single degree of freedom (DOF) structure and a multi-DOF structure representing a twelve story building. Both structures under consideration have been excited with El Centro acceleration signal. The results of numerical simulations on the SDOF system indicate that using neuro-controller it would be possible to obtain smaller amplitudes as compared with the LQ regulator, but it would require higher control effort

Pole -mounted sonar vibration prediction using CMAC neural networks

The efficiency and accuracy of pole-mounted sonar systems are severely affected by pole vibration, Traditional signal processing techniques are not appropriate for the pole vibration problem due to the nonlinearity of the pole vibration and the lack of a priori knowledge about the statistics of the data to be processed. A novel approach of predicting the pole-mounted sonar vibration using CMAC neural networks is presented. The feasibility of this approach is studied in theory, evaluated by simulation and verified with a real-time laboratory prototype, Analytical bounds of the learning rate of a CMAC neural network are derived which guarantee convergence of the weight vector in the mean. Both simulation and experimental results indicate the CMAC neural network is an effective tool for this vibration prediction problem

An Adaptive Tool-Based Telerobot Control System

Modern telerobotics concepts seek to improve the work efficiency and quality of remote operations. The unstructured nature of typical remote operational environments makes autonomous operation of telerobotic systems difficult to achieve. Thus, human operators must always remain in the control loop for safety reasons. Remote operations involve tooling interactions with task environment. These interactions can be strong enough to promote unstable operation sometimes leading to system failures. Interestingly, manipulator/tooling dynamic interactions have not been studied in detail. This dissertation introduces a human-machine cooperative telerobotic (HMCTR) system architecture that has the ability to incorporate tooling interaction control and other computer assistance functions into the overall control system. A universal tooling interaction force prediction model has been created and implemented using grey system theory. Finally, a grey prediction force/position parallel fuzzy controller has been developed that compensates for the tooling interaction forces. Detailed experiments using a full-scale telerobotics testbed indicate: (i) the feasibility of the developed methodologies, and (ii) dramatic improvements in the stability of manipulator – based on band saw cutting operations. These results are foundational toward the further enhancement and development of telerobot

Workshop on Fuzzy Control Systems and Space Station Applications

The Workshop on Fuzzy Control Systems and Space Station Applications was held on 14-15 Nov. 1990. The workshop was co-sponsored by McDonnell Douglas Space Systems Company and NASA Ames Research Center. Proceedings of the workshop are presented

Biped Locomotion: Stability analysis, gait generation and control

Ph.DDOCTOR OF PHILOSOPH