Abstract

The paper deals with the creation and implementation of a methodology for optimizing the parameters of cascade control of the machine tool axis drives. The first part presents the identification of a dynamic model of the axis based on experimental data from measuring the axis dynamics. The second part describes the controller model, selection of optimization objective functions, and optimization of constraint conditions. The optimization of controllers is tuned by simulation using identified state-space model. Subsequently, the optimization procedure is implemented on the identified model, and the found control parameters are used on a real machine tool linear axis with different loads. The implementation of the proposed complex procedure on a real horizontal machine tool proved the advantage of simultaneous tuning of all parameters using optimization methods. The strategy solves the problem of mutual interaction of all control law parameters disabling effective usability of gradual sequential tuning. The methodology was developed on a speed control loop, the tuning of which is usually the most difficult due to the close interaction with the dynamic properties of the machine mechanics. The whole procedure is also applicable to the position and current control loop.

Highlights

  • Tuning the cascade controllers’ parameters is a demanding process that requires a high level of expertise of the operator performing this activity [1]

  • Static and dynamic machine tool stiffness [9] is influenced by force feedback applied to the PID cascade controller

  • The main objective of the research was to create a methodology for optimization of the cascade regulation controller of the machine tool drive axes

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Summary

Introduction

Tuning the cascade controllers’ parameters is a demanding process that requires a high level of expertise of the operator performing this activity [1]. The reduced state-space model of the machine tool with feed drive is obtained in [19] covering dissipative phenomena (which were identified by measurement) and covering controller too. Identification based on recursive least squares method is used to determine feed drive model including nonlinear friction [21]. This paper deals with the methodology for tuning the cascade control parameters of the machine drive axis. The methodology workflow is as follows: machine drive axis identification, cascade control loop assembly, formulation and processing of optimization task, and application of optimal controller settings.

Identification of dynamic models
Eigensystem realization algorithm method
Multivariable Output Error State-Space method
Cascade control
Current control
Speed control
Position control
Speed control loop model
State-space description of the open-loop system
Formulation of velocity controller tuning optimization task
Transformation between physical and machine parameters without reduction
The overall objective function
Boundary conditions of the optimization task
Simulation testing of developed optimization procedure
Load state hm0
Comparison of load states
Verification of auto-tuning algorithm on real machine
Table without load — hm0
Findings
Conclusions
Full Text
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