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Abstract

Multi-motor synchronous drive system is increasingly widely used in industry and manufacturing, where its control structure and control strategy affect the quality and efficiency of production. In order to solve the contradiction between fastness and overshoot, and the difficulty in determining the compensation law in the conventional PID, cross-coupling control, and master-slave control strategies used in multi-motor control, this paper proposes a self-coupling PID control strategy based on ring adjacent compensation to reduce the complexity of the control structure. Furthermore, this paper analyzes the self-coupling PID parameter tuning rules and establishes the control structure of the ring coupling strategy, and proves its validity mathematically. The simulation results verify that the proposed strategy provides a fast response speed, high control precision, good disturbance rejection, and synchronization performance.

Keywords

Multi-motor drive system ring adjacent coupling compensation self-coupling PID synchronization error

Introduction

With the development of modern industrial technology, the using of multi-motor synchronous control is increasing, such as electric cars,^1,2 aerospace, and so on,^3,4 especially for the high-precision high-speed systems, and cooperative control is the central issue.^5,6 In China and abroad, the number of studies that focus on multi-motor synchronous control technology is growing more and more. However, the early synchronous control is primarily based on the mechanical connection, which is simple, but the accuracy of synchronization is low.⁷ With the development of motor transmission technology, information technology, and automatic control technology, the synchronous mode of mechanical connection transmission has been replaced by an electrical servomotor.^8,9 Currently, multi-motor cooperative control mainly comprises non-coupling control and coupling control. Where, the Non-coupling control includes master command control, master-slave control, and virtual axis control. Literatures^10,11 is using master-slave control, and this scheme also has the problem of large-signal transmission delay; However, in literatures^12,13 proposed virtual axis control, which realizes the synchronous control of multiple motors, but this control strategy has some problems such as given signal delay. Coupling control includes cross-coupling, adjacent coupling, deviation coupling, etc. In literatures^14,15 the cross-coupling control was proposed. However, this scheme is suitable for dual-motor systems, but for multi-motor systems, the structure is complex and the compensation effect is not ideal. While, adjacent coupling control to overcome the defect that the cross-coupling structure is only suitable for double motors, but there are many synchronous error controllers and a large amount of computation was occurred; the deviation coupling control has high coupling degree, low control delay, and good synchronization ability, but the compensator is complex. Literature¹⁶ proposes a data comparison method and a compensation algorithm, but the calculated amount is significant. Literature¹⁷ proposes a synchronous control strategy that combines adjacent cross-coupled structures with sliding mode variable structure control, but each compensator requires many speed signals and has sliding mode jitter.

In order to improve the precision, stability, and robustness of cooperative control, a number of researchers have proposed different methods by combining modern control methods with existing control strategies.¹⁸ In literatures^19,20 the author presents sliding mode control, but there is sliding mode jitter; Literature²¹ proposes adaptive neural network. However, there is a contradiction between control accuracy and parameter estimation; Literature²² presents active disturbance rejection control, but there are many parameter tuning; Literature²³ proposes iterative learning control, but there is a problem with initial state selection; Literature²⁴ uses the fuzzy control algorithm to adjust the motor torque given value in real-time to improve the synchronization performance, but due to the simple fuzzy processing method, the useful information of the system is lost, and the dynamic tracking ability is very weak; Literature²⁵ proposes a variable domain fuzzy PID control based on a master-slave control structure. However, it involves multiple parameters, and the effectiveness relies on fuzzy rules. In literature,²⁶ a synchronization controller based on the mean relative coupling structure is proposed to address the coupling issue between synchronization and tracking. However, the synchronization compensation relies on the mean value.

In order to realize the cooperative control of multi-motors and ensure that each motor has good tracking, disturbance rejection, and synchronization in the operation process, based on the self-coupling PID control theory and deviation coupling control structure, this paper proposes self-coupling PID for multi-motor cooperative control based on circular adjacent compensation. The tracking and disturbance rejection of the system is improved by the self-coupling PID control, and the synchronization performance is improved by using the ring adjacent compensator. The self-coupling PID parameter tuning rules are analyzed, and the Lyapunov equation is established to analyze the stability of the compensation system. Finally, the numerical simulation test is used to verify the effectiveness of the control strategy.

The main contribution of this paper is to propose a self-coupled PID control strategy based on ring adjacent compensation for a multi-motor synchronous control system, as well as to develop a Romberg torque observer with minimal dependence on input information. The proposed ring compensation structure outperforms master-slave control, cross-coupling control, and virtual spindle control, providing new ideas for synchronous control problems.

Ring adjacent compensation control structure

The structure of the multi-motor synchronous control system based on ring-adjacent compensation is illustrated in Figure 1. In the system, each permanent magnet synchronous motor adopts the structure of a speed controller, current controller, and adjacent compensator. The current closed-loop adopts a vector control scheme of $i_{d} = 0$ . The control strategy controls the error between the actual speed of each motor and the given speed by the speed controller and then compensates by considering the speed and load of the motor and the adjacent motor. The change in speed and load of any motor will compensate for the adjacent motor. All motors are coupled to one another and ultimately form a ring. The ring adjacent compensation is only performed between two adjacent motors. When a motor is disturbed during the system’s operation, the speed change will cause a synchronization error between the motor and its two adjacent motors. In the loop compensation control strategy, the synchronization error will be feedback to the adjacent motor and the disturbed motor itself through the compensation module. The speed controller of each motor is combined with the speed compensation module to improve the following performance, immunity, and steady-state performance of the system.

Figure 1.

The structure of multi-motor synchronous control system with circular adjacent compensation.

The expected speed of each motor and each controller link in the system is same. The compensation amount of each compensation module is only related to the speed and a load torque of the two adjacent motors. The complexity of the compensator is independent of the motors number. The ring adjacent compensation control is more suitable for a multi-motor synchronous system in compared with the cross-coupling control strategy and the deviation coupling control strategy. In the $dq$ coordinate system, the mechanical motion equation of the permanent magnet synchronous motor rotor is

{\begin{matrix} \overset{\cdot}{ω} = \frac{1}{J} [T_{em} - T_{l} - B ω] \\ \overset{\cdot}{θ} = ω \end{matrix}

(1)

When the vector control $i_{d} = 0$ , the electromagnetic torque of the motor is $T_{em} = n_{p} L_{md} i_{f} i_{q}$ , then the motion equation is

\overset{\cdot}{ω} = \frac{n_{p}}{J} L_{md} i_{f} i_{q} - \frac{p_{n}}{J} T_{l} - \frac{B}{J} ω

(2)

Where, $ω$ is motor speed, $P_{n}$ is polar logarithm, $L_{md}$ is d-axis excitation inductance; $i_{f}$ is equivalent excitation current converted to the stator side, $B$ is the coefficient of viscosity, and $T_{L}$ is load torque, $J$ is moment of inertia.

Design of multi-motor speed synchronization control

This paper mainly studies the design of the speed loop and the ring adjacent compensator of the permanent magnet synchronous motor, while the current loop adopts the conventional hysteresis control structure, which will not be repeated in this paper.

Design of self-coupling PID controller

The speed-controlled object controlled by the current loop is a second-order system, and its affine nonlinear system is expressed as

{\begin{matrix} {\overset{\cdot}{x}}_{1} = x_{2} \\ {\overset{\cdot}{x}}_{2} = f (x_{1}, x_{2}, u) + d_{1} \\ y = x_{1} \end{matrix}

(3)

Where, $x_{2}$ and $x_{1}$ are measurable state variables, $u$ is the control quantity, $y$ is the output of the system, $f (x_{1}, x_{2}, u)$ is a known or unknown uncertain function, $d_{1}$ is an external bounded disturbance. For a second-order nonaffine nonlinear uncertain system expressed in equation (3), let the combined disturbance of the system be the unknown uncertain dynamics and external disturbance, that is

d = f (x_{1}, x_{2}, u) + d_{1} - b_{0} u

(4)

where $b_{0}$ is the estimated gain of the controlled object, then equation (3) can be rewritten as

{\begin{matrix} {\overset{\cdot}{x}}_{1} = x_{2} \\ {\overset{\cdot}{x}}_{2} = d + b_{0} u \\ y = x_{1} \end{matrix}

(5)

It can be seen from equation (5) that a class of non-affine nonlinear uncertain systems is transformed into a class of linear uncertain affine systems by introducing a comprehensive disturbance, and the control problem of the nonlinear uncertain system is transformed into the control problem of the linear uncertain affine system.^27,28 For the system expressed by equation (3) or (5), the control error is defined as

e_{1} = r - y

(6)

The integral of error is

e_{0} = \int e_{1} dt

(7)

Then

{\begin{matrix} {\overset{\cdot}{e}}_{0} = e_{1} \\ {\overset{\cdot}{e}}_{1} = e_{2} = \overset{\cdot}{r} - \overset{\cdot}{y} \\ {\overset{\cdot}{e}}_{2} = \overset{\cdot\cdot}{r} - \overset{\cdot\cdot}{y} = \overset{\cdot\cdot}{r} - {\overset{\cdot}{x}}_{2} = \overset{\cdot\cdot}{r} - d - b_{0} u \end{matrix}

(8)

For the system expressed by equation (5), the auto-coupled PID control model^29,30 is defined by equation (8) as

u = (\overset{\cdot\cdot}{r} + k_{i} e_{0} + k_{p} e_{1} + k_{d} e_{2}) / b_{0}

(9)

Then, ${\overset{\cdot}{e}}_{2} = - d - k_{i} e_{0} - k_{p} e_{1} - k_{d} e_{2} = {\overset{\cdot}{e}}_{2} = - d - k_{i} \int e_{1} dt - k_{p} e_{1} - k_{d} {\overset{\cdot}{e}}_{1}$ . The closed-loop control system based on self-coupling PID is shown in Figure 2.

Figure 2.

Closed-loop control system model based on self-coupled PID.

The main innovation of the strategy is that there is only a one-speed factor $Z_{c}$ , and its main feature is that the physical links of the three different properties such as proportion, integral, and differential are closely coupled to form the control signal through $Z_{c}$ .

Control system performance analysis

Theorem 1: Assuming that the sum disturbance defined by equation (4) is bounded: $| d | < \infty$ when the three roots are all in the left half of the S plane, the closed-loop control system composed of the SC-PI controller defined by equation (9) is stable in a wide range, and the SC-PI control system has good disturbance rejection and robustness.

Proving: Substitute the SC-PID controller defined by equation (9) into the controlled error system shown in equation (8) to obtain a closed-loop control system as

\begin{matrix} {\begin{matrix} s e_{0} (s) - e (0) = e_{1} (s) \\ s e_{1} (s) - e_{1} (0) = e_{2} (s) \\ s e_{2} (s) - e_{2} (0) = - d (s) - k_{i} e_{0} (s) - k_{p} e_{1} (s) \\ - k_{d} s e_{1} (s) + k_{d} e_{1} (0) \end{matrix} \end{matrix}

(10)

Since $e_{0} (0) = 0$ then

\begin{matrix} {\begin{matrix} s e_{0} (s) = e_{1} (s) \\ s e_{1} (s) - e_{1} (0) = \frac{1}{s} [e_{2} (0) - d (s) - k_{i} e_{0} (s) \\ - k_{p} e_{1} (s) - k_{d} s e_{1} (s) + k_{d} e_{1} (0)] \\ s e_{2} (s) - e_{2} (0) = - d (s) - k_{i} e_{0} (s) - k_{p} e_{1} (s) \\ - k_{d} s e_{1} (s) + k_{d} e_{1} (0) \end{matrix} \end{matrix}

(11)

It can be obtained from equation (11) as

\begin{matrix} e_{1} (s) = \frac{sd (s) + s^{2} e_{1} (0) + s e_{2} (0) + k_{d} e_{1} (0)}{s^{3} + k_{d} s^{2} + k_{p} s + k_{i}} \\ = - \frac{sd (s)}{s^{3} + k_{d} s^{2} + k_{p} s + k_{i}} + \frac{s^{2} e_{1} (0) + s e_{2} (0) + k_{d} e_{1} (0)}{s^{3} + k_{d} s^{2} + k_{p} s + k_{i}} \end{matrix}

(12)

The first term is the zero-state response and the second term is the zero-input response. From equation (12), the transfer function of the closed-loop system can be obtained as

H (s) = \frac{s}{s^{3} + k_{d} s^{2} + k_{p} s + k_{i}}

(13)

Let equation (13) have three identical real roots, three different real roots (namely $z_{1}$ , $z_{2}$ , and $z_{3}$ , respectively), one real root $z_{1}$ , or two identical real roots $z_{1} = z_{2}$ (or complex roots $z_{2} = z_{3} = α + j β$ ), the corresponding unit impulse excitation response forms are as follows

h (t) = (0.5 z_{c} t^{2} - t) e^{- z_{c} t} = f (t) e^{- z_{c} t}, t > 0

(14)

h (t) = η_{1} e^{- z_{1} t} + η_{2} e^{- z_{2} t} + η_{3} e^{- z_{3} t}, t > 0

(15)

h (t) = η_{1} e^{- z_{1} t} + t e^{- z_{2} t} or η_{1} e^{- z_{1} t} + \cos ω t e^{- α t}, t > 0

(16)

In addition, according to the principle that the zero-input response of the system has a fixed relationship with $h (t)$ , it can be obtained that if each feature root has a negative real part, when $| d | < \infty$ , the system has good robustness against total disturbance.

Parameter tuning analysis

In order to stabilize the system, let the three roots be $S_{1, 2, 3} = - Z_{c}$ and $z_{c} > 0$ , then $k_{p} = 3 z_{c}^{2}$ , $k_{i} = z_{c}^{3}$ , and $k_{d} = 3 z_{c}$ can be obtained from

{(s + z_{c})}^{2} = (s^{2} + 2 s z_{c} + z_{c}^{2}) (s + z_{c}) = s^{3} + 3 z_{c} s^{2} + 3 z_{c}^{2} s + z_{c}^{3}

(17)

If the three roots of the system are $s_{1} = - z_{c}$ , $s_{2, 3} - z_{c} \pm j λ$ , where $z_{c}$ and $λ$ are positive real numbers, respectively. Then $k_{p} = 3 z_{c}^{2} + λ^{2}$ , $k_{i} = z_{c}^{3} + z_{c} λ^{2}$ , and $k_{d} = 3 z_{c}$ can be obtained from

\begin{matrix} (s + z_{c}) (s + z_{c} + j λ) (s + z_{c} - j λ) = s^{3} + 3 z_{c} s^{2} \\ + s (3 z_{c}^{2} + λ^{2}) + z_{c}^{3} + z_{c} λ^{2} \end{matrix}

(18)

Therefore, only by determining $z_{c}$ and $λ$ , the parameters of the controller can be determined.

If the three roots of the system are $s_{1} = - z_{c}$ , $s_{2, 3} = - {z_{c}}_{2}$ $s_{2, 3} = - z_{c 2}$ , where $z_{c 1}$ and $z_{c 2}$ are positive real numbers, respectively. Then $k_{p} = 2 z_{c 1} z_{c 2} + z_{c 2}$ , $k_{i} = Z_{c 1} z_{c 2}^{2}$ , and $k_{d} = z_{c 1} + 2 z_{c 2}$ can be obtained from

\begin{array}{l} (s + z_{c 1}) {(s + z_{c 2})}^{2} = s^{3} + (2 z_{c 2} + z_{c 1}) s^{2} \\ + (z_{c 2} + 2 z_{c 1} z_{c 2}) s + z_{c 1} z_{c 2}^{2} \end{array}

(19)

Therefore, only by determining $z_{c 1}$ and $z_{c 2}$ , the parameters of the controller can be determined, that is, two parameters need to be tuned.

If the three roots of the system are $s_{1} = - z_{c 1}$ , $s_{1} = - z_{c 2}$ , and $s_{1} = - z_{c 3}$ where $z_{c 1}$ , $z_{c 2}$ , and $z_{c 3}$ are positive real numbers, respectively. Then $k_{p} = z_{c 1} z_{c 2} + z_{c 2} z_{c 3} + z_{c 1} z_{c 3}$ , $k_{i} = z_{c 1} z_{c 2} z_{c 3}$ , and $k_{d} = z_{c 1} + z_{c 2} + z_{c 3}$ can be obtained from

\begin{matrix} (s + z_{c 1}) (s + z_{c 2}) (s + z_{c 2}) = s^{3} + (z_{c 1} + z_{c 2} + z_{c 1}) s^{2} \\ + (z_{c 2} z_{c 3} + z_{c 1} z_{c 2} + z_{c 1} z_{c 3}) s + z_{c 1} z_{c 2} z_{c 3} \end{matrix}

(20)

The parameters of the controller can be determined only after confirming $z_{c 1}$ , $z_{c 2}$ , and $z_{c 3}$ . At this time, three parameters need to be tuned.

Adjacent ring compensation control strategy

Let the synchronization error between the ith motor and the next motor be

ε_{i} = ω_{i} - ω_{i + 1}

(21)

Then the tracking error of the ith motor after being corrected by the compensation module is

E_{i_{b}} = ε_{i}

(22)

According to the Lyapunov theorem, construct the Lyapunov function as

V = \frac{1}{2} E_{ib}^{2} > 0

(23)

If ${\overset{\cdot}{E}}_{ib} = - K_{i} ε_{i} (K_{i} > 0)$ exists, then

\overset{\cdot}{V} = E_{ib} {\overset{\cdot}{E}}_{ib} = - K_{i} E_{ib}^{2} < 0

(24)

Therefore, the system is globally stable when $E_{i_{b}} \to 0$ . When $i = 1$ , equation (25) can be obtained from ${\overset{\cdot}{E}}_{1 b} = - K_{1} ε_{1} = {\overset{\cdot}{ε}}_{1} = {\overset{\cdot}{ω}}_{1} - {\overset{\cdot}{ω}}_{2}$

- K_{1} (ω_{1} - ω_{2}) = {\overset{\cdot}{ω}}_{1} - {\overset{\cdot}{ω}}_{2}

(25)

Assuming that all motor parameters are the same, that is, $J_{1} = J_{2} = \dots J$ , $n_{p_{1}} = n_{p_{2}} = \dots n_{p}$ , and $B_{1} = B_{2} = \dots B$ . The compensation current of the first motor is

\begin{matrix} i_{qb 1} = i_{q 1} - i_{q 2} = \frac{- K_{1} J}{n_{p} L_{md} i_{f}} ω_{1} + \frac{K_{1} J}{n_{p} L_{md} i_{f}} ω_{2} + \frac{1}{L_{md} i_{f}} T_{1} \\ + b \frac{J}{n_{p} L_{md} i_{f}} ω_{1} - \frac{1}{L_{md} i_{f}} T_{2} - b \frac{J}{n_{p} L_{md} i_{f}} ω_{2} \end{matrix}

(26)

As can be seen from the above, the torque $T_{1}$ and $T_{2}$ are required to obtain the compensation current.

Design of load torque observer

According to formula (1), the rotational speed can be measured, while the load torque cannot be measured. Select, $x = [ω T_{i}]^{T}$ $u = T_{e}$ , $y = ω$ . When the sampling period is very small, the load torque is considered constant within the sampling period, and the state space expression is obtained as

\begin{matrix} [\begin{matrix} \overset{\cdot}{ω} \\ {\overset{\cdot}{T}}_{l} \end{matrix}] = [\begin{matrix} - \frac{B}{J} & - \frac{1}{J} \\ 0 & 0 \end{matrix}] [\begin{matrix} ω \\ T_{l} \end{matrix}] + [\begin{matrix} \frac{1}{J} \\ 0 \end{matrix}] T_{e} = Ax + Bu \end{matrix}

(27)

y = [\begin{matrix} 1 & 0 \end{matrix}] [\begin{matrix} ω \\ T_{l} \end{matrix}] = Cx

(28)

The form of the Luenberger observer is constructed as follows

{\begin{matrix} \dot{\hat{x}} = A \hat{x} + Bu + L (y - \hat{y}) \\ \hat{y} = c \hat{x} \end{matrix}

(29)

where $L = {[\begin{matrix} L_{1} & L_{2} \end{matrix}]}^{T}$ . Derivation and finishing can get

\overset{\cdot}{x} = (A - LC) \hat{x} + Bu + Ly

(30)

Substitute equations (27) and (28) into equation (30) to get

[\begin{matrix} \dot{\hat{ω}} \\ {\dot{\hat{T}}}_{l} \end{matrix}] = [\begin{matrix} \begin{matrix} - L_{1} - \frac{B}{J} & - \frac{1}{J} \end{matrix} \\ \begin{matrix} - L_{2} & 0 \end{matrix} \end{matrix}] [\begin{matrix} \hat{ω} \\ {\hat{T}}_{l} \end{matrix}] + [\begin{matrix} \frac{1}{J} \\ 0 \end{matrix}] T_{e} + [\begin{matrix} L_{1} \\ L_{2} \end{matrix}] ω

(31)

\dot{\hat{ω}} = \frac{1}{I} [T_{e} - {\hat{T}}_{l} - B \hat{ω} + J L_{1} (ω - \hat{ω})]

(32)

{\dot{\hat{T}}}_{l} = L_{2} (ω - \hat{ω})

(33)

It can be seen from equation (33) that the load torque can be observed only by the electromagnetic torque of the motor and the actual speed of the motor without additional hardware equipment. The system control process is shown in Figure 3.

Figure 3.

System control process.

Simulation and analysis

In order to further prove the correctness, stability, and effectiveness of the cooperative control scheme proposed in this study, a multi-motor synchronous control system composed of three motors is constructed and simulated on the MATLAB/Simulink platform. The three selected motors have the same parameters, as summarized in Table 1.

Table 1.

Parameters of permanent magnet synchronous motor.

Coefficient	Value
Stator inductance (Ld)	0.000835 H
Stator inductance (Lq)	0.000835 H
Stator resistance (R)	0.18 Ω
Moment of inertia (J)	0.00062 kg·m²
Coefficient of viscosity (B)	0.0003035 N·m·s
Permanent magnet flux linkage	0.07147

Simulation of self-coupling PID and conventional PID

In order to verify the effect of the self-coupling PID control, compared with the conventional PID control, two control methods use the hysteresis controller in the current loop and the ring adjacent compensation. The controller parameters are summarized in Table 2. The expected speed value is $n^{*} = 1000 r / \min$ , when $t = 3.5 s$ , the same pulse load disturbance is added to motor 1 of each control structure, and the other motor loads remain unchanged. Figure 4 shows the response curve of self-coupling PID and PID control. Figure 5 depicts the comparison curve of synchronous error between motor 1 and motor 2. Figure 6 shows the comparison curve of synchronous error between motor 2 and motor 3. Figure 7 depicts the comparison curve of synchronous error between motor 3 and motor 1. Figure 8 illustrates each self-coupled PID controller output. Figure 9 illustrates each PID controller output. Comparative analysis demonstrate that the self-coupling PID controller has fast output response and small fluctuations; The PID control output has slow adjustment time, large fluctuations, and multiple oscillations.

Table 2.

Parameters of controller.

PID	$k_{p} = 90$	$k_{i} = 10$	$k_{d} = 1$
Self-coupling PID	$z_{c} = 3$
Current loop	$\pm 0.05$

Figure 4.

Self-coupling PID and PID control response curve.

Figure 5.

Speed synchronization difference between motor 1 and motor 2 under the separate control of sc-pid and pid.

Figure 6.

Speed synchronization difference between motor 2 and motor 3 under the separate control of sc-pid and pid.

Figure 7.

Speed synchronization difference between motor 1 and motor 3 under the separate control of sc-pid and pid.

Figure 8.

Each self-coupled PID controller output.

Figure 9.

Each PID controller output.

It can be seen from Figure 4 that the overshoot of PID control is 10% and the adjustment time is 0.7 s during the startup process, while the overshoot of self-coupling PID is 1% and the adjustment time is 0.1 s. When the load disturbance occurs, the dynamic speed drop of PID control is $20 r / \min$ , and the recovery time is 0.05 s. The dynamic speed drop of self-coupling PID control is $10 r / \min$ , and the recovery time is 0.025 s. It can be seen from Figures 5 and 6 that the motor load disturbance has a great impact on the motor synchronization performance in the system with the PID control strategy. The maximum speed synchronization deviation is $25 r / \min$ , the convergence time is 0.07–0.4 s, the maximum speed synchronization deviation of self-coupling PID control is $5 r / \min$ , and the convergence time is 0.01 s.

The simulation of master-slave control, cross-coupling control, and proposed control strategy

In order to verify the effectiveness of the adjacent ring compensation control strategy proposed in this paper, it is compared with the master-slave control and cross-coupling control. In the master-slave control strategy system, motor 1 is set as the main motor, and motor 2 and motor 3 are the slave motors. The expected speed of the three control strategies is $n^{*} = 1000 r / \min$ , when $t = 3.5 s$ , the same pulse load disturbance is added to motor 2 of each control structure, and the other motor loads are unchanged. Figure 10 shows the synchronous error comparison curve between motor 1 and motor 2. Figure 11 illustrates the synchronous error comparison curve between motor 2 and motor 3. Figure 12 depicts the synchronous error comparison curve between motor 3 and motor 1. Figure 13 illustrates the output of each self-coupled PID controller, Figure 14 illustrates the output of each controller in master-slave mode, and Figure 15 illustrates the output of each controller in cross-coupling mode. Through comparative analysis, the output response speed of the self-coupling PID controller is the fastest and the fluctuation is small; The output response speed of the master-slave control mode controller is slow, and the maximum output value is smaller than that of the self-coupling PID controller; The output response of the cross coupling control method controller is slow, but the output fluctuates greatly.

Figure 10.

Speed synchronization difference between motor 1 and motor 2 under the separate control of sc-pid, m-s and c-c.

Figure 11.

Speed synchronization difference between motor 2 and motor 3 under the separate control of sc-pid, m-s and c-c.

Figure 12.

Speed synchronization difference between motor 1 and motor 3under the separate control of sc-pid, m-s and c-c.

Figure 13.

Output of each self-coupled PID controller.

Figure 14.

Output of each controller in master-slave mode.

Figure 15.

Output of each controller in cross-coupling mode.

It can be seen from Figures 10 –12 that the synchronization errors of the three control strategies in the startup process finally tend to 0, and the synchronization errors of the adjacent ring compensation control and the cross-coupling control are 0, while the synchronization error of the master-slave control strategy is large, and the maximum speed synchronization error can reach $578 r / \min$ , which needs about 0.015 s to converge to 0. The synchronization performance and cross-coupling control strategy in the startup stage is basically the same, which are significantly better than the master-slave control strategy.

The load disturbance of the motor has a significant influence on the synchronous performance of the motor in the system using the master-slave control strategy, which needs 0.06–0.09 s to gradually converge to 0, and the cross-coupling control needs 0.035–0.04 s to gradually converge to 0; with the use of adjacent ring compensation control, the synchronization error between the disturbance motor and the adjacent two motors is less than that of the master-slave control and cross-coupling control strategy, and the synchronization error converges to 0 in 0.01–0.037 s and runs stably. Among them, the relative maximum synchronous speed deviation is $40 r / \min$ when the master-slave control strategy is adopted; the relative maximum dynamic speed drop of the cross-coupling control strategy and the adjacent ring compensation control strategy is about $10 r / \min$ . Thus, the adjacent ring compensation control can not only effectively suppress the synchronization error caused by a motor disturbance in the system, but also accelerate its convergence speed.

According to the simulation results of motor starting, when the system adopts the adjacent ring compensation control strategy, the motor synchronization error always approaches 0 and converges rapidly in the motor starting process, consequently, it has a good starting synchronization performance. The synchronous error caused by motor load disturbance is relatively small and converges rapidly, and its disturbance rejection is significantly better than that of the master-slave control strategy and cross-compensation control strategy. Based on the above analysis, it can be seen that the adjacent ring compensation control strategy significantly improves the dynamic and steady-state characteristics of the system, which shortens the disturbance recovery time, weakens the oscillation phenomenon, and offers good synchronization. In addition, the proposed control strategy has fewer controller parameters, simple parameter setting and small amount of system computation.

Conclusions

In order to solve the performance of multi-motor cooperative control, a self-coupling PID control structure with ring adjacent compensation is proposed to improve the synchronization, dynamic and static performance of the multi-motor system. The following conclusions are obtained through experiments:

1) In order to improve the synchronization of the multi-motor system, a new ring-adjacent compensation structure is proposed based on the coupling compensation principle. Where the complexity of the control structure can’t be affected by the number of motors;

2) In order to improve the system tracking and disturbance rejection, a self-coupled PID control strategy is used to analyze the system performance by pulse excitation response. The tuning rules of controller parameters are summarized by applying the relationship between roots and coefficients;

3) In order to achieve adjacent compensation, by constructing a Luenberger observer, the load torque can be observed only by the electromagnetic torque of the motor and the actual speed of the motor.

The convergence of the control algorithm is demonstrated by the use of the Lyapunov function. The proposed control strategy is applied to the synchronous control system of three motors and compared to the system using master-slave control and cross-coupling control strategy. The simulation results show that the proposed control strategy makes the system have good starting performance, dynamic performance, and disturbance rejection, and ensures the synchronization accuracy of the system, which is suitable for the synchronous control of multiple motors.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Science and Technology Research Project of the Education Department of Jilin Province [Grant JJKH20210042KJ], Jilin Provincial Development and Reform Commission Project [Grant 2019C058-1], the Jilin Province Vocational education research project [Grant 2021XHY248], and Graduate Innovation Program Project of Beihua University (Grant 2022006).

ORCID iDs

Dejun Liu

Guangda Chen

References

Zhang

Ding

Guo

, et al. Eco-driving cruise control for 4WIMD-EVs based on receding Horizon reinforcement learning. Electronics 2023; 12(6): 1350.

Kakouche

Rekioua

Mezani

, et al. Model predictive direct torque control and fuzzy logic energy management for multi power source electric vehicles. Sensors 2022; 22(15): 5669.

Shu

Wei

Tan

, et al. Investigation of dynamic and synchronization properties of a multi-motor driving system: theoretical analysis and experiment. Mech Syst Signal Process 2021; 153(6): 107496.

Sun

Zhu

, et al. MPTC for PMSMs of EVs with multi-motor driven system considering optimal energy allocation. IEEE Trans Magn 2019; 55: 1–6.

Kakouche

Rekioua

Mezani

, et al. Model predictive direct torque control and fuzzy logic energy management for multi power source electric vehicles. Sensors 2022; 22: 5669.

Zhao

Ren

Gao

. Synchronization and tracking control for multi-motor driving servo systems with backlash and friction. Int J Robust Nonlinear Control 2016; 26(13): 2745–2766.

Yuhao

Fei

Yunkai

. Overview of multi-motor synchronous motion control technology. Trans China Electrotech Soc 2021; 36(14): 2922–2935.

Kong

Wang

Kong

, et al. Motor shifting and torque distribution control of a multi-motor driving system in electric construction vehicles. Adv Mech Eng 2021; 13(6): 589–598.

Annisa

Mat Darus

Tokhi

, et al. Controlling the non-parametric modeling of double link flexible robotic manipulator using hybrid PID tuned by P-type ILA. Int J Intergr Eng 2018; 10(7): 219–232.

10.

Yibo

Kuan

. Brief introduction of multimotor synchronous control technology. Small Spec Electr Mach 2019; 47(8): 69–73.

11.

Yingying

Zhang

Zhenjian

, et al. Study on adaptive coupling synchronous control of cutterhead driving motors of tunnel boring machine. Tunn Constr 2020; 40(12): 1800–1807.

12.

Ting

Hong

Wei

, et al. Research on dual drive synchronization performance based on virtual shaft control strategy. In: IEEE 2017 2nd international conference on robotics and automation engineering, Shanghai, China, 29–31 December2017, pp.254–258. New York, NY: IEEE.

13.

Fan

Youtong

Xiaoyan

, et al. Application of synchronization control on traction motors of high-speed trains. Micromotors 2016; 49(8): 27–30.

14.

Renyin

Yangkuan

Lianqing

, et al. Review of multi-motor synchronization control. Electr Mach Control Appl 2017; 44(6): 8–12.

15.

Changliang

Xin

, et al. Speed synchronization control of dual-PMSM system. Trans China Electrotech Soc 2017; 32(23): 1–8.

16.

Qiang

Shaowei

Zhanqing

, et al. Multi-motor speed synchronous control based on improved relative coupling structure. Trans China Electrotech Soc 2019; 34(3): 474–482.

17.

Wang

Dong

Ren

, et al. SDRE based optimal finite-time tracking control of a multi-motor driving system. Int J Control 2021; 94(9): 2551–2563.

18.

Wang

Ding

Zhao

, et al. Neural network-based output synchronization control for multi-actuator system. Int J Adapt Control Signal Process 2022; 36(5): 1155–1171.

19.

Liu

Yang

. Sliding-mode control based adaptive PID control with compensation controller for motion synchronization of dual servo system. In: 2017 36th Chinese control conference, Dalian, China, 26–28 July2017, pp.4929–4934. New York, NY: IEEE.

20.

Hongyan

Ximei

. Dual linear motors servo system synchronization control based on sugeno type fuzzy neural network and complementary sliding mode controller. Trans China Electrotech Soc 2019; 34(13): 2726–2733.

21.

Wang

Zhang

. GESO-based position synchronization control of networked multiaxis motion system. IEEE Trans Ind Inform 2020; 16(1): 248–257.

22.

Verrelli

Pirozzi

Tomei

, et al. Synchronization control of DC motors through adaptive disturbance cancellation techniques. In: IEEE 2016 international symposium on power electronics, electrical drives, automation and motion, Capri, Italy, 22–24 June2016, pp.488–493. New York, NY: IEEE.

23.

Limei

. Cross couple iterative learning control of direct drive XY table based on an empirical mode decomposition algorithm. Proc CSEE 2016; 36(17): 4745–4752.

24.

Xiaoping

Zhenguang

Zhu

. A synchronic control method for double switched reluctance motors based on fuzzy control. Control Eng China 2017; 24(11): 170–175.

25.

Mengta

Ping

. Research on variable universe fuzzy PID control of electronic shaft driving gravure printing system. Electr Drive 2019; 49(12): 76–81.

26.

Ren

Zheng

. Integral predictor based prescribed performance control for multi-motor driving servo systems. J Franklin Inst 2022; 359(16): 8910–8932.

27.

Zhezhao

Zeyu

. On control theory of PID and auto-coupling PID. IET Control Theory Appl 2020; 37(12): 2654–2662.

28.

Jie

Zhezhao

. Auto-coupling PID control method for nonlinear time varying systems. IET Control Theory Appl 2022; 39(2): 299–306.

29.

Zhezhao

Wenjue

. Self-coupling PID controllers. Zidonghua Xuebao 2021; 47(2): 404–422.

30.

Yilin

Zhezhao

Wei

. An autocoupling PID control method for nonlinear underactuated unstable systems. Space Control Technol Appl 2023; 49(1): 82–89.

Research on self-coupling PID for multi-driven synchronization control with ring adjacent compensation

Abstract

Keywords

Introduction

Ring adjacent compensation control structure

Design of multi-motor speed synchronization control

Design of self-coupling PID controller

Control system performance analysis

Parameter tuning analysis

Adjacent ring compensation control strategy

Design of load torque observer

Simulation and analysis

Simulation of self-coupling PID and conventional PID

The simulation of master-slave control, cross-coupling control, and proposed control strategy

Conclusions

Footnotes

Declaration of conflicting interests

Funding

ORCID iDs

References