Study Of Servo Control System Of The Spinal Decompression Equipment

Spinal disease is very common in surgery. With advancing in biotechnology,spinal decompression has evolved into one of the effective non-surgical treatmentfor lumbar intervertebral disc prolapse. Based on theories and experiments, thefollowing respects will be studied deeply in this paper: the spinal decompressioncontrol system and control algorithm.Firstly the pathology of the lumbar disc prolapse is described, the researchstatus of traction device control system is summarized, and a new kind of spinaldecompression traction servo control system is put forward in this paper. Then thecontrol system structure and the basic principle of the traction control are introduced,and the spinal decompression control system and control algorithm are studieddeeply.In the process of traction treatment, one of the key technologies to ensuretraction effect is the stability of the traction-force control. In this research, thecomputer and movement control card are used as controller, the weighing senor isused as the detecting element, the AC servo motor is used as the executive element,and then the spinal decompression equipment servo control system is studied infurther. Through establishing the mathematical model of the drive, AC servo motor,speed-reducer and human spine, the mathematical model of the traction controlsystem is established. In the MATLAB/SIMULINK module, the simulation study isdone to the spinal decompression servo control system, using the conventional PIDcontrol algorithm. Ultimately it is determined that the patient weight, traction angleand the patient interference are the main factors which affect the spinaldecompression servo control effect. Then the PID neural network control algorithmwhich has adaptive ability is put forward, and a deep research on this algorithm isdone.Finally, an actual test is conducted to the traction force servo control system and control algorithm. The PID neural network was compared with conventional PIDcontrol to the traction control effect. The experimental results showed that the PIDneural network has the property of high force tracking accuracy and anti-interferenceability, as well as the better adaptability to parameter change, compared withconventional PID control, achieving good control effect. The correctness andfeasibility of the spine traction system and control algorithm are proved.