Reference trajectory generation is one of the most important tasks in the control of machine tools. Such a trajectory must guarantee a smooth kinematics profile to avoid exciting the natural frequencies of the mechanical structure or servo control system. Moreover, the trajectory must be generated online to enable some feed rate adaptation mechanism working. This paper presents the online smooth speed profile generator used in trajectory interpolation in milling machines. Smooth kinematic profile is obtained by imposing limit on the jerk-which is the first derivative of acceleration. This generator is based on the neuro-fuzzy system and is able to adapt online the current feed rate to changing external conditions. Such an approach improves the machining quality, reduces the tool wear, and shortens total machining time. The proposed trajectory generation algorithm has been successfully tested and can be implemented on a multiaxis milling machine.

In this work, we consider the flexible neuro-fuzzy systems of the Mamdani-type. When designing such systems to solve approximation problem, we should choose triangular norms used in inference and aggregation operators. This can be done by trial and error. In this work, we propose an algorithm that allows in an automatic way to choose the types of triangular norms in the learning process. The task of this algorithm is also an automatic selection of parameters of all functions describing the system. The algorithm uses an evolutionary strategy for its action and has been tested using well-known approximation benchmarks.