Andrzej Przybył

Novel Online Speed Profile Generation for Industrial Machine Tool Based on Flexible Neuro-Fuzzy Approximation

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.

A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects

In this paper we propose a new approach to nonlinear modelling. It uses capabilities ofthe so-called flexible neuro-fuzzy systems and evolutionary algorithms. The aim of our method is not only to achieve appropriate accuracy of the model, but also to ensure the possibility of interpretability of the knowledge within it. The proposed approach was achieved by, among others, appropriate selection of operational criteria applied to evolutionary model creation. It allows to extract interpretable fuzzy rules in the cases which use the learning data e.g. from identification. The possibility of interpretation of knowledge accumulated in the model seems to be important in practice, because it guarantees operation predictability and facilitates production of efficient and accurate control methods. Our method was tested with the use of well-known simulation problems from the literature. HighlightsWe propose a new algorithm to nonlinear modelling.Our method uses flexible neuro-fuzzy systems and population based algorithms.Our method includes various criteria of the interpretability of the fuzzy rules.Our method uses Wards clustering method to initial population generation.

Online speed profile generation for industrial machine tool based on neuro-fuzzy approach

The paper presents the online smooth speed profile generator used in trajectory interpolation in milling machines. Smooth kinematics profile is obtained by imposing limit on the jerk - which is the first derivative of acceleration. This generator is based on the neurofuzzy look-ahead function and is able to adapt online the actual feedrate to changing external conditions. Such an approach improves the machining quality, reduces the tools wear and shortens total machining time.

A New Approach to Designing Interpretable Models of Dynamic Systems

In the process of designing automatic control system it is very important to have an accurate model of the controlled process. Approaches to modelling dynamic systems presented in the literature are often approximate, uninterpretable (acting as a black box), not appropriate to work in real-time, so it is not possible to create a hardware emulator on the basis of these approaches. The paper presents a new method to create model of nonlinear dynamic systems which gives a real opportunity for the interpretation of accumulated knowledge. By combining methods of control theory with fuzzy logic rules a good accuracy of the model can be achieved with use of a small number of fuzzy rules. Our method is based on the evolutionary strategy \(\left({\mu,\lambda} \right)\).

A new method to construct of interpretable models of dynamic systems

The paper presents a new method to create model of nonlinear dynamic systems which gives a real opportunity for the interpretation of accumulated knowledge. By combining methods of control theory with fuzzy logic rules a good accuracy of the model can be achieved with use of a small number of fuzzy rules.

A new approach to design of control systems using genetic programming

In this paper a new approach to automatic design of control systems is proposed. It is based on a knowledge about modelling object and capabilities of the genetic programming. In particular, a new type of the problem encoding, new evolutionary operators (tuning operator and mutation operator) and new initialization method are proposed. Moreover, we present a modified block schema of genetic algorithm and modification of genetic operators: insertion, pruning, crossover were introduced. Combination of mentioned elements allows us to simplify a design of control systems. It also provides a lot of possibilities in the selection of the control system parameters and its structure. Our method was tested on the model of quarter car active suspension system. DOI: http://dx.doi.org/10.5755/j01.itc.44.4.10214

A New Algorithm for Identification of Significant Operating Points Using Swarm Intelligence

The paper presents a novel algorithm for identification of significant operating points from non-invasive identification of nonlinear dynamic objects. In the proposed algorithm to identify the unknown parameters of nonlinear dynamic objects in different significant operating points, swarm intelligence supported by a genetic algorithm is used for optimization in continuous domain. Moreover, we propose a new weighted approximation error measure which eliminates the problem of the measurements obtained from non-significant areas. This measure significantly accelerates the process of the parameters identification in comparison with the same algorithm without weights. Performed simulations prove efficiency of the novel algorithm.

New Method for Nonlinear Fuzzy Correction Modelling of Dynamic Objects

In the paper a method to use the equivalent linearization technique of the nonlinear state equation with the coefficients generated by the fuzzy rules for current operating point is proposed. On the basis of the evolutionary strategy and properly defined identification procedure, the fuzzy rules are automatically designed to maximize the accuracy of the resulting linear model.

Hybrid State Variables - Fuzzy Logic Modelling of Nonlinear Objects

In this paper a new hybrid method for modelling of nonlinear dynamic systems is proposed. It uses fuzzy logic system together with state variables technique to obtain the local linear approximation performed continuously for successive operating points. This approach provides good accuracy and allows the use of very convenient and well-known method from linear control theory to analyse the obtained model.

Genetic Algorithm for Observer Parameters Tuning in Sensorless Induction Motor Drive

The paper presents an example of genetic algorithm applied to speed adaptive flux observer parameters tuning in sensorless induction motor drive. The algorithm concerns an optimal gain matrix and PI controller gain coefficients tuning. The gain matrix is implemented in a neural network. Two methods of weights obtaining in the network is proposed. Presented approach selects an optimal operating point of the observer in the presence of measurement noise and inaccurate knowledge of mathematical model parameters.