An intelligent control method based on fuzzy logic for a robotic testing system for the human spine.
In previous biomechanical studies of the human spine, we implemented a hybrid controller to investigate load-displacement characteristics. We found that measurement errors in both position and force caused the controller to be less accurate than predicted. As an alternative to hybrid control, a fuzzy logic controller (FLC) has been developed and implemented in a robotic testing system for the human spine. An FLC is a real-time expert system that can emulate part of a human operator's knowledge by using a set of action rules. The FLC provides simple but robust solutions that cover a wide range of system parameters and can cope with significant disturbances. It can be viewed as a heuristic and modular way of defining a nonlinear, table-based control system. In this study, an FLC is developed which uses the force difference and the change in force difference as the input parameters, and the displacement as the output parameter. A rule-table based on these parameters is designed for the controller Experiments on a physical model composed of springs demonstrate the improved performance of the proposed method.
Tian
Lianfang
L
Spine Tissue Engineering Laboratory, Musculoskeletal Research Center, Department of Orthopedic Surgery, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA.
D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D004548:Elasticity; D005103:Expert Systems; D005246:Feedback / Q000502:physiology; D017143:Fuzzy Logic; D006801:Humans; D009068:Movement / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D010808:Physical Examination / Q000295:instrumentation / Q000379:methods*; D010812:Physical Stimulation / Q000379:methods; D012371:Robotics / Q000295:instrumentation / Q000379:methods*; D013131:Spine / Q000502:physiology*; D013314:Stress, Mechanical