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Pneumatic cylinders are widely used in highly dynamic processes, such as handling and conveying tasks. They must work both reliably and accurately. The positioning accuracy suffers from the stick-slip effect due to strong adhesive forces during the seal contact and the associated high breakaway forces. To achieve smooth motion of the piston rod and increased position accuracy despite highly variable position dynamics, sliding friction and breakaway force must be reduced. This contribution presents a specially designed linear tribometer that has two types of control. Velocity control allows the investigation of sliding friction mechanisms. Friction force control allows investigation of the breakaway force. Due to its bearing type, the nearly disturbance-free detection of stick-slip transients and the dynamic contact behavior of the sliding friction force was possible. The reduction of the friction force was achieved by a superposition of the piston rod's movement by longitudinal ultrasonic vibrations. This led to significant reductions in friction forces at the rubber/metal interface. In addition, the effects of ultrasonic frequency and vibration amplitude on the friction reduction were investigated. With regard to the breakaway force, significant success was achieved by the excitation. The force control made it possible to identify the characteristic movement of the sealing ring during a breakaway process. [ABSTRACT FROM AUTHOR]

The stick-slip is one of negative phenomena caused by friction in servo systems. It is a consequence of complicated nonlinear friction characteristics, especially the so-called Stribeck effect. Much research has been done on control algorithms suppressing the stick-slip, but no simple solution has been found. In this work, a new approach is proposed based on genetic programming. The genetic programming is a machine learning technique constructing symbolic representation of programs or expressions by evolutionary process. In this way, the servo control algorithm optimally suppressing the stick-slip is discovered. The GP training is conducted on a simulated servo system, as the experiments would last too long in real-time. The feedback for the control algorithm is based on the sensors of position, velocity and acceleration. Variants with full and reduced sensor sets are considered. Ideal and quantized position measurements are also analyzed. The results reveal that the genetic programming can successfully discover a control algorithm effectively suppressing the stick-slip. However, it is not an easy task and relatively large size of population and a big number of generations are required. Real measurement results in worse control quality. Acceleration feedback has no apparent impact on the algorithms performance, while velocity feedback is important. [ABSTRACT FROM AUTHOR]