Abstract

An electromagnetic isolation system can dynamically adjust the output characteristic parameters of the system in real time through the active control strategy, which has strong adaptability to the external environment. In order to control the electromagnetic vibration isolation system effectively, an active control method is presented based on the linear quadratic regulator (LQR) approach and the coevolutionary niche genetic algorithm (NGA). In this paper, the dynamical equation and state equation of the electromagnetic isolation system are built, which include the nonlinear relationship between electromagnetic force and coil current and gap. The LQR approach is employed to maintain a steady state of an isolated object on the vibration isolation system. Meanwhile, a coevolutionary niche genetic algorithm is put forward to optimize the parameters in Q and R matrices. Simulation and experimental results demonstrate that the electromagnetic isolation system with the LQR approach and coevolutionary NGA can effectively isolate the vibration and maintain a steady state for an isolated object in comparison with the passive isolation system.

Highlights

  • With the development of science and technology, a lot of precision measuring instruments, such as scanning probe microscope, laser interferometer, and so on, have found wide applications in actual engineering

  • The electromagnetic isolation system has a steady-state error, it is small enough to be ignored. e passive isolation system is stable at 0.51 s, and the outputs are both 0.0063 which is approximately 700 times bigger than the output value of the active isolation system. e simulation result demonstrates that the linear quadratic regulator (LQR) approach and the weight matrices calculated by the proposed coevolutionary niche genetic algorithm can effectively control vibration and ensure the stability of the isolated object when the electromagnetic isolation system is subjected to the step signal

  • In order to verify the effectiveness of the proposed LQR approach and the coevolutionary niche genetic algorithm, experiments are conducted on the electromagnetic isolation system. e physical photo of the vibration isolation system with two electromagnetic isolation units is shown in Figure 8. e proposed control approach based on the LQR and the coevolutionary NGA is implemented on the STM 32 single-chip microcontroller, which is integrated with data acquisition equipment on the development board. e control cycle of the controller is 0.01 s. ere are two displacement sensors in the electromagnetic isolation system to achieve displacement signals which are input to the objective function in LQR approach

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Summary

Introduction

With the development of science and technology, a lot of precision measuring instruments, such as scanning probe microscope, laser interferometer, and so on, have found wide applications in actual engineering. In order to ensure the normal operation of equipment, various vibration isolation systems and vibration control technologies have been developed for vibration reduction and noise reduction of working machinery, stability control of precision measuring instrument and test bed, improvement of vehicle comfort, and so on [6, 7]. Active vibration isolation systems operate with external power sources and flexible control methods to control vibration for the purpose of keeping machinery performing at its best precision. Compared with the passive vibration isolation system, active vibration isolation system can dynamically adjust the output characteristic parameters of the system in real time according to the given control strategy, which has strong adaptability to the external environment. The electromagnetic vibration isolation system, a typical active isolation system with variable noncontact dynamical parameters, flexible control methods, and fast response during operation, has been widely used in the field of vibration control.

Isolation Structure and Nonlinear Relationship
Linear Quadratic Regulator of the Isolation System
The Coevolutionary Niche Genetic Algorithm
Simulation and Experimentation
Conclusion
Full Text
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