Aiming at the challenging problem of the traditional warp knitting machine electronic jacquard control system with complex structure of multiple circuit boards layered cascade, such as large physical space occupation, high power consumption, and independent high-voltage power supply voltage, we proposed an embedded circuit and control strategy design for the piezoelectric jacquard needle (PJN) with adaptive boost and energy recovery functions. Firstly, the electromechanical dynamics model of PJN was established. Secondly, the fuzzy PI double closed-loop control algorithm driven by a finite state machine is proposed. Thirdly, with the help of a Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET), the PJN is integrated with the drive circuit. The drive circuit of PJN uses an energy storage inductor to replace the current limiting resistor of the traditional drive circuit, which can not only limit the forward charging current of the PJN and reduce energy loss but also can use the energy absorbed from the low-voltage power supply to adaptively boost the power supply of the PJN to the high voltage required for working conditions. The simulation results show that the new PJN drive circuit has an adaptive self-boost function. The PWM signal modulated by the fuzzy PI double closed-loop control algorithm can efficiently and accurately control the adaptive boost power supply and the voltage across the PJN. The mode of the circuit can be correctly switched through the sequential logic of the finite state machine and realize the energy recovery function.

At present, traditional motors and electromagnetic drives can no longer meet the drive requirements of special equipment for precision motion [

“Jacquard” is an important pattern knitting method in warp knitting production. The jacquard control method of warp knitting machine has developed from the early mechanical faucet type and electromagnetic field type to the piezoelectric ceramic type. At present, the piezoelectric ceramic jacquard technology that can accurately and independently control each yarn guide needle in the warp knitting machine is developing rapidly. Piezoelectric jacquard needles (PJNs) realize the three-dimensional, rich and complex jacquard effect of warp knitted fabrics. The PJN is a revolutionary change to the traditional electronic traverse jacquard technology and has received extensive attention from the textile academic and engineering circles. Kumaravelu et al. [

This article focuses on a design method of an adaptive boost energy-saving fuzzy control system driven by a finite state machine for the PJN. First of all, the electromechanical dynamics model of the PJN was established, and the mathematical relationship between the positive and negative offset displacement of the PJN and the applied voltage was analyzed. Secondly, a fuzzy PI double closed-loop control algorithm driven by a finite state machine is proposed. Third, we construct the driver circuit of the PJN based on MOSFET, which integrates an adaptive self-boost circuit of the high-voltage power supply with the working circuit of the PJN.

The PJN generally consists of two Pb-based Lanthanum-doped Zirconate Titanate (PZT) piezoelectric ceramic wafers and a glass fiber ceramic substrate, as shown in Figure

Model of the PJN

Next, we consider the mathematical relationship between the bending and expansion of the PJN and its applied voltage

From equation (

From equation (

The drive circuit model includes six MOSFETs such as

Drive circuit model of the PJN.

The complete working process of the drive circuit of the PJN includes 6 working modes, that is,

The state transition process of 6 modes.

The key waveform of the drive circuit of the PJN.

Equivalent circuits in different modes. (a) Initial boost. (b) The inductor (L) charge. (c) Inductor L discharge and energy recovery. (d)

All MOSETs are turned off through the input control terminal, and the low-voltage power supply

When

According to the conservation of energy, we can get

According to formula (

When

Mode

In mode

In order to ensure

Mode

According to the warp knitting process, the PJN will return to the equilibrium modes

Generally, the circuit for multimode switching operation needs to adopt an independent controller in each working mode. In this paper, a finite-state machine is used to drive a fuzzy PI controller so that it can adapt to different circuit modes, as shown in Figure

The fuzzy PI controller driven by the finite state machine.

The fuzzy PI controller adopts double closed-loop control, where

The proportional and integral adjustment parameters of the outer-loop fuzzy PI controller are _{i} and _{i}, then

Fuzzy control rules of

N | Z | P | |
---|---|---|---|

N | N/Z | N/Z | N/Z |

Z | N/P | P/P | P/P |

P | P/Z | P/Z | P/Z |

In this paper, the fuzzy domain of input and output is divided into three fuzzy subsets

The relationship between the clarification variables

The input and output membership functions of the fuzzy inference are shown in Figures

Membership function of fuzzy controller input and output and the adaptive adjustment of

Driving circuit of the PJN.

Diagram of the relationship between inputs and outputs in fuzzy reasoning. (a) Correspondence between

The Mamdani reasoning rule is used to perform fuzzy reasoning as follows:

We use the center of gravity method with smoother output, simpler calculation, and higher accuracy to perform the following defuzzification:

In order to verify the effectiveness of the design strategy of the adaptive boost energy-saving fuzzy control system driven by the finite-state machine, a PJN drive circuit was built in MATLAB/Simulink, as shown in Figure

In order to ensure that

Adaptive boost energy-saving fuzzy control system of the PJN.

The finite-state machine model is shown in Figure

Finite-state machine model.

The adaptive self-boost performance, jacquard needle drive function, and energy-saving effect of the PJN drive circuit are shown in Figures

Curve of

This paper integrates the self-boosting circuit of PJN high-voltage power supply with the PJN high-frequency working circuit to improve the circuit integration. The logic switching sequence of each functional mode of the circuit is dispatched by a finite-state machine. The adopted PI fuzzy double closed-loop control algorithm can realize the controlled voltage value of the circuit in each mode smoothly and quickly reaching the target value. The new PJN drive circuit designed in this paper uses energy storage inductors instead of the current limiting resistors of the traditional drive circuit, making the circuit have a self-boosting function, without external high-voltage power supply, only low-voltage power supply, effectively reducing the complexity of the circuit. The energy storage capacity of the inductor can be used for energy recovery, achieving energy saving and low power consumption. The design strategy proposed in this paper provides a theoretical basis for the design of the embedded jacquard miniaturized control system of the warp knitting machine. It would be interesting to consider Takagi–Sugeno fuzzy neural networks to analyze the stability and provide self-learning capabilities in the control problem of the driving circuit of the PJN for future work.

The data used to support the findings of this study are included within the article.

The authors declare that there are no conflicts of interest regarding the publication of this paper.

This work was supported by the Key Industrial Guidance Projects of Fujian Province (2019H0034).