Model-free Control Scheme Research Articles

Intelligent control for handling motion nonlinearity in a retrofitted machining table

For low cost automation, a traditional manually operated milling machine with a lead-screw transmission system was retrofitted with an AC servo-motor. This old fashioned machining table has nonlinear time-varying behaviour caused by obvious backlash and irregular coulomb friction of the sliding surfaces. It is difficult to design an appropriate classical controller for this complicated dynamic system. Hence intelligent model-free self-organising fuzzy control and neural network control strategies equipped with learning ability are employed to control this machining table, to improve both the adaptability and the path tracking accuracy. These control approaches can be implemented without the trial and error process for selecting initial parameters and fuzzy rules. The experimental results show that these control strategies achieve satisfactory transient response and tracking accuracy under the influence of /spl sim/0.4 mm of backlash on each axis and large stick-slip friction.

Freeway traffic control using fuzzy logic controllers

A major cause of freeway congestion before the traffic density becomes critical is the shock wave due to the speed differences between consecutive vehicles. Such disturbance can be reduced if we can impose homogeneous speed control on the vehicles. In this paper, a two-level model-free control scheme using neural-network-based fuzzy logic controllers is proposed which regulates the speed of the freeway through speed advisory boards. Using information from both measurement data and expert knowledge (e.g., environmental information and psychological factors), it is expected that this controller will outperform the conventional ones.