# Research on Fuzzy Control Method of Automobile Antilock Braking System

Authors: Qiang Fu
Journal: Security and Communication Networks
 (2022)

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## Abstract

The automobile Antilock Braking System (ABS) can prevent the wheel from locking by automatically adjusting the brake pressure at a high speed during emergency braking. While improving the braking effect of the car, it can also keep the steering ability of the car and ensure the safety of passengers. The automobile braking process has a strong nonlinearity and uncertainty, so exploring a simple and reliable control strategy is the focus of current research. Based on this, this paper proposes a variable-domain fuzzy proportional integral differential (PID) control strategy using a particle swarm optimization (PSO) algorithm to iteratively find the optimal theoretical domain. First, the PSO strategy is used to obtain the optimal regulation parameters. Then, the dynamic antiinterference ability of the control system is guaranteed by the variable theory domain fuzzy PID control, and the PID parameters and variable theory domain expansion factor are optimized by the PSO to increase the utilization degree of fuzzy rules initially set. Finally, compared with the traditional control strategy, the simulation and real vehicle test prove that the proposed system can significantly improve the control accuracy of abs. The proposed system has the advantages of small overshoot, short adjustment time, strong antiinterference ability, and practicability, which greatly improves the tracking performance of the ABS.


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## Body

## 1. Introduction

The data on modern automobile accidents show that nearly 20% of the accidents are caused due to the faults of the Antilock Braking System (ABS). Therefore, it is of great significance to adopt a reasonable control strategy to achieve complete control in the shortest distance.The Antilock Braking System (ABS) can avoid the phenomenon of tire locking when the wheel braking torque is large and plays a vital role in the safety of the vehicle [1]. It can not only prevent the front and rear wheels from being locked completely when braking and avoid slipping but also can shorten the braking distance and braking time, preventing excessive tire wear at the same time, and can improve the car in the braking process of handling stability and safety [2].Because the road and vehicle conditions are complex, the braking process of the ABS is characterized by nonlinear, time-varying, and uncertainty. It is required that the braking process can be fast, stable, and have strong disturbance rejection under the premise of ensuring the braking effect [3]. Moreover, ABS has high requirements for the quality of its control algorithm, and complex algorithms with slow response speed or poor antiinterference ability are not suitable for the ABS control [4]. At present, the ABS control algorithms mainly include logical threshold control [5], sliding mode variable structure control [6], PID control [7], and fuzzy control algorithm [8]. Each threshold value of the logic threshold controller needs to be determined by the repeated test, so it is difficult to debug. In literature [9], ADAMS and Simulink were used to test linear braking, and braking started at an initial speed of 100 km·h−1 on the road surface. It shows that the slip rate of ABS with a logic threshold controller fluctuates greatly during the whole braking process and cannot reach a stable state before complete braking. The sliding mode variable structure control algorithm has the advantages of simple structure and strong robustness, but it can only attenuate amplitude and cannot completely remove chattering. In literature [6], braking was carried out at an initial speed of 90 km·h−1 on wet asphalt and dry asphalt but roads. The results show that compared with the ABS system based on sliding mode, the chattering of sliding mode is reduced effectively. However, the system lacks practical vehicle tests to verify its feasibility. Literature [10] uses a PID control algorithm to start braking at an initial speed of about 100 km h−1 in MATLAB/Simulink environment and sets the target value of slip rate as 0.2. Simulation results show that with the extension of control time, the ABS system gradually reaches a stable state, but the overshoot is large and the response time is long, which does not meet the requirements of the ABS system fast and stable. Zha et al. [11] used CarSim and Simulink cosimulation to design the ABS system based on fuzzy control and started braking at an initial speed of 108 km h−1. The results show that the response speed is fast in the braking process, but the fluctuation is large and lasts until the complete braking, and the designed fuzzy controller cannot make the system reach the best stable state. It can be seen that the control algorithms for ABS systems each have their own focus and drawbacks. By combining the respective advantages of different control algorithms and complementing each other, a more suitable controller for ABS systems can be designed. For example, a fuzzy PID controller can be designed by combining the advantages of the fast response speed of fuzzy control and good stability of PID control [12]. This system has been used in literature [13] to solve the problem of inaccurate target slip rate selection. The results show that the ABS system based on fuzzy PID control has a better control effect than the PID or fuzzy control.Fuzzy PID is a control strategy developed after the birth of fuzzy cybernetics, which has been widely used in engineering [14]. Literature [15] proposed a new linear programming method for designing diagonal fuzzy PID controllers of two-input-two-output (TITO) systems and proposed a design method based on compensating characteristic trajectories. Experimental results show that linear programming improves the control efficiency of diagonal fuzzy PID. Literature [16] proposed the use of a genetic algorithm to design a fuzzy vehicle semiactive suspension magnetorheological damper PID control system; simulation results show that the control system has excellent vibration reduction.Through the analysis of the braking process, it is found that the control of antilock braking is essentially the control of slip rate, which is easily affected by many uncertain factors such as speed, wheel speed, and road conditions. The ABS can avoid the phenomenon of tire locking when the wheel braking torque is large and improve the braking performance [17]. Existing cars are equipped with a standard traditional hydraulic ABS, which is mainly based on logic rules to realize the antilock braking function of tires. However, there are problems such as complicated logic and large workload of parameter adjustment (hereinafter referred to as “parameter adjustment”) [18]. On the other hand, the electrification and intelligence of vehicles also promote the development of wire control systems [19] such as electro-hydraulic braking systems and electro-mechanical braking systems. The braking torque can be adjusted continuously, and the linear control system as the actuator can transform the control problem of the ABS into a typical system control problem. Especially, the emergence of the wire control system promotes the technological innovation of the active braking system.Although the ABS has made great progress in control strategies, different slip rate control strategies still have their ownshortcomings [20]. The PID control strategy can achieve accurate tracking of slip rate through repeated adjustment, but the PID control parameters must be changed according to different vehicle types and different working conditions. The proposed fuzzy PID can improve the robustness but also increase the complexity of the control system [21]. As an effective control method to deal with the nonlinearity and robustness of ABS, sliding mode control still relies on the mathematical model of the ABS [22]. When the dynamic characteristics of the ABS are not modeled or the disturbance quantity is too large, the tracking response speed of the slip rate and the high-frequency tremor of braking torque restrict its application. The model predictive control (MPC) requires real-time matrix inversion, which is temporarily unable to be applied in real vehicles due to the limitation of computing chip hardware [23]. Although fuzzy control and neural network control can achieve the ideal control effect through debugging and training, the time and economic cost are too high, which also restrict the application in the ABS system. More importantly, under the emergency braking condition, the vehicle will be affected by external disturbance; how to realize the suppression of external disturbance plays a crucial role in the safety of emergency braking.Traditional control strategy and optimization algorithms for complex ABS systems still have some limitations, including the immunity effect, there is no unified evaluation standard at present for the traditional control strategy, and the research lacks a large number of real vehicle tests [24]. Therefore, this paper designs an ABS control system based on the variable theory domain fuzzy PID structure method of particle swarm optimization to solve the optimal parameters. The simulation and real vehicle test show that this system has good effectiveness, robustness, and stability.The main innovations of this paper are as follows:(1)
An iterative optimization strategy using particle swarm optimization is proposed to obtain the optimal tuning parameters.(2)
The dynamic antiinterference ability of the control system is guaranteed by variable theory domain fuzzy PID control, and the PID parameters and variable theory domain expansion factor are optimized by the particle swarm optimization algorithm to increase the utilization degree of fuzzy rules initially set.This paper consists of five main parts: the first part is the introduction, the second part is state of the art, the third part is a methodology, the fourth part is the result analysis and discussion, and the fifth part is the conclusion.


## 2. State of the Art

In the process of braking, with the increase of braking torque, the tread and the ground will have a certain degree of relative sliding; that is, the wheel is in the state of rolling and sliding. If the braking torque continues to increase, the wheels will lock. The proportion of the sliding part in the wheel movement is expressed by the slip rateλ, and its formula is as follows:(1)λ=q−ωRq.In the formula,q is the vehicle speed (unit: m/s), ω is the wheel rotation angular speed (unit: rad/s), and R is the wheel rolling radius (unit: m).In this paper, the slip rateλ is used as the control object to study the ABS braking system. When the slip rate λ = 0, the adhesion coefficient μ = 0, that is, the wheel is in a pure rolling state. As the braking torque increases, the wheel changes from a rolling state to a rolling and sliding state. At this time, the slip rate increases gradually, and the adhesion coefficient increases rapidly. When the slip rate increases to λ0, the maximum adhesion can be obtained, and the corresponding adhesion coefficient is μmax. Then, the adhesion coefficient decreases gradually with the increase of the slip rate λ. When the slip rate λ = 1, it is called wheel locking. The braking principle of automobile ABS is based on this curve characteristic, by adjusting the braking torque to obtain a larger ground-braking force.The Burckhardt –tyre-road model can simulate the relationship between the slip rate and the adhesion coefficient under different road conditions. The Burckhardtμ-λ curve function is expressed as follows. And the typical values of each parameter for different road conditions are shown in Table 1.(2)μλ=c11−e−c2λ−c3λ,λz=1c2logc1c2c3μλz=c1−c3c21−logc1c2c3.Table 1
Typical values of each parameter for different road conditions.
PavementP1P2P3Dry asphalt1.391125.2240.643Wet asphalt0.96835.0340.47Cement1.308326.4020.6603Dry cobblestone1.48237.69050.7921Wet cobblestone0.511434.9420.2434Snow0.305695.3630.1876Ice0.161307.6240.124


## 3. Methodology

### 3.1. Vehicle Dynamics Model

The following is the vehicle 7-DOF dynamic mathematical model:(3)wq˙i−γ⋅qj=Fifl+Firrcosδ−Fjfl+Fjfrsinδ+Firl+Firrwq˙j+γ⋅qi=Fifl+Firsinδ+Fjfl+Fjfrcosδ+Fjrl+FjrrXk⋅γ˙=Fifl+Fifrsinδ+Fjfl+Fjfrcosδg+Fifr−Fiflcosδ+Fjfl−Fjfrsinδnm12+Firr−Firlnm22−Fjrl+Fjrrh,Yω˙x=−R⋅Fix−Nhx+Ndxwhere δ is the front wheel angle, rad. qi,qj are longitudinal and transverse speeds respectively, m/s. γ is the yaw velocity, rad/s. N. x = fl, fr, rl, rr corresponding to the left front wheel, the right front wheel, the left rear wheel, and the right rear wheel, respectively. ωx is the angular velocity of each tire, rad/s. w is the vehicle mass, kg. g. N = g + h is the wheelbase of the vehicle, m. nm1 is the front axle wheelbase, m. nm2 is the rear axle wheelbase, m. Xk is the vehicle around the k axis, kg·m2. ba is the distance from the center of mass to the ground, m. R is the effective radius of the tire, m. Nhx and Ndx are braking torque and driving torque of each tire, respectively, N·m.Since this paper mainly studies the slip rate control of the ABS, the linear control dynamic system is taken as the first-order inertia system, and its transfer function is as follows:(4)Nhxs=ωhs+ωh.In the formula,ωh is the zero point of the transfer function of the linear control dynamic system, which is also the bandwidth of the actuator, rad/s.


### 3.2. Tire Model

To express the friction characteristics of tires and roads more accurately, a LuGre dynamic tire model was established, which can describe the jumping and hysteresis of friction.(5)Flx=σ0lk+σ1lk˙+σ2lqrlFTxk˙lζ,n=qrl−κlR|ω|+σ0lqrlθaqrlklζ,naqrl=μc+μs−μce−qrl/qs,where L ∈ (i, j), ζ represents the coordinate axis, and αx represents the sideslip angle of each tire. Fl is the friction between the tire and the road, N. σ0l is rubber stiffness, m−1. σ1l is the rubber damping coefficient, s/m. FTx is the vertical load of the tire. qrl=Rω−qx is the relative velocity, m/s. qx is the translation speed of the tire axle, m/s. κl is the pressure distribution coefficient and its value is κl=7/6w=8.3m where w is the length of tire marks, m. θ is the road adhesion factor. μc is the coulomb friction. μs is the static friction force. qs is the Stribeck velocity, m/s, which represents the switching speed of the two friction states.The vertical load of each tire is shown as follows:(6)FTf=wah2N−wq˙iba2N−wq˙jbanm1⋅hNFTr=wah2N−wq˙iba2N+wq˙jbanm1⋅hNFTr=wag2N+wq˙iba2N−wq˙jbanm1⋅gNFTTr=wag2N+wq˙iba2N+wq˙jbanm1⋅gN.The formula for the sideslip angle of each tier is as follows:(7)αfl=δ−arctanqj+gγqi−nm1/2γαfr=δ−arctanqj+gγqi+nm1/2γαrl=−arctanqj−hγqi−nm2/2γαrr=−arctanqj−hγqi+nm2/2γ.The longitudinal speed of each wheel center in the wheel coordinate system is as follows:(8)qfl=qi−nm12γcosδ+qj+gγsinδqfr=qi+nm12γcosδ+qj+gγsinδqrl=qi−nm22γqrr=qi+nm22γ.The slip rate of each wheel is as follows:(9)λfl=ωflR−qflqflλfr=ωfrR−qfrqfrλrl=ωrlR−qrlqrlλrr=ωrrR−qrrqrr.


### 3.3. Fuzzy Control Strategy

The dynamic antiinterference ability of the control system is guaranteed by the variable theory domain fuzzy PID control. The PID parameters and the variable theory domain expansion factor are optimized by the PSO algorithm to increase the utilization degree of fuzzy rules initially set. The motor parameters are shown in Table2. In short, it is a method that uses an iterative particle swarm optimization strategy to obtain optimal tuning parameters.Table 2
Motor parameters.
ParameterValueMaximum no-load speedt0 (r·min−1)125Peak blocking currentIt (A)3.5Peak blocking torqueTt (N·m)20Armature resistanceR (Ω)1.75Torque coefficientZn (Nm·A−1)2Maximum supply voltageU (V)25Continuous blocking torqueTg (N·m)8Continuous blocking voltageUg (V)8Continuous blocking currentIg (A)10Armature inductanceL (mH)2.5Rotational inertiaJw (kg·m2)0.005Counter potential coefficient0.2The PSO algorithm is an optimization algorithm that is used to solve the best solution of the problem [25], which can obtain the optimal solution of the problem to be solved within a certain range. In the field of control, the PSO algorithm can be combined with the PID controller to realize the dynamic adjustment of parameters [26]. Individuals can obtain group motion so that the solution of the problem is orderly and the best solution is obtained [27]. Figure 1 shows the flow chart of the particle swarm optimization algorithm applied in this paper. Firstly, the initial setting of optimization parameters is carried out. After initializing the particles, the positions and fitness of individuals and groups are calculated to determine whether the termination conditions are met. If so, the results are output; if not, the particles are updated to form iteration.Figure 1
Particle swarm optimization process.To judge the approximate degree of each particle value and theoretical solution, a reasonable evaluation index should be determined and adapted to the model characteristics of the torque motor. In this paper, the integral of the time-weighted absolute error (ITAE) is selected as a function to evaluate the degree of approximation between particle value and theoretical solution (particle swarm fitness). The expression of the ITAE index is as follows:(10)ITAE:s=∫0∞en|dn,where e(n) is the deviation between the actual value and the set value and n is the optimization time.It can be seen from formula (12) that the ITAE index can be used as the characterization parameters of variables such as overshoot, adjustment time, and rise time. In this paper, the ITAE index is taken as a comprehensive evaluation factor to measure the control performance of the control process; that is, the particle swarm adaptation value reflects the situation of the comprehensive parameters such as overshoot, adjustment time, and rise time.The fuzzy controller is a PID parameter self-tuning controller. At the same time, the expansion factorsα(e) and α(EC) of input variables and LK (m) (m = P, I, D) of output variables are dynamically adjusted by using the change rate of E and EC, to realize the automatic tuning of domain factors (see Figure 2).Figure 2
Variable domain structure of fuzzy PID.According to the min-max reasoning criterion proposed by Xu et al. [20], the output of the weighted average solution after fuzzy processing is(11)ΔZw=Cx∑xμC1ΔZxw∑xμC1ΔZxw.PID parameter setting formula is as follows:(12)Zu=Zu0+ΔZu,Zx=Zx0+ΔZx,Zd=Zd0+ΔZd,where Zu0,Zx0,andZd0 are the initial set values of PID parameters. ΔZu,ΔZx,andΔZd are dynamic setting values.The actual output is as follows:(13)pz=Zuez+Zx∑x=1zex+Zdecz.To maximize the utilization of fuzzy rules and better optimize the control index, the input and output of fuzzy PID controller can be dynamically adjusted by scaling factor and scaling factor. For the quantization factor of the input variable, considering the maximum use of fuzzy rules, the principle of positive correlation between quantization factor and input variable is adopted, and the form widely used in the motor control field is selected from a variety of description methods:α=1−λ1e−zi2. For the proportional factor of output variables, combined with the influence of PID parameters on the control performance, the proportional factor of Zu and Zd is monotonically consistent with the error, and Zx scale factor and error have a monotone inverse principle, to get the scale factor empirical formula. The empirical formula of expansion factor and scale factor summarized in this paper is shown in the following formula:(14)αe=1−λ1e−z1i2,αec=1−λ2e−z2i2,βpu=λ3|e|,βpx=1e+λ4,βpd=λ5e.Each parameter of formula (14) was obtained by the PSO algorithm. Using the PSO, the inertia factor is set as 0.6 and the acceleration constant is set as 2. In this paper, there are seven parameters λ1, λ2, λ3, λ4, λ5, z1, and z2 in the expansion factor formula, plus three initial parameters Zu0,Zx0,andZd0 of PID. The dimension is set as 10, the particle swarm size is set as 30, the maximum iteration number is set as 30, and the minimum adaptation value is set as 0. The maximum number of iterations and the minimum adaptive value can be used as the termination conditions of the algorithm. The offspring of particle swarm initialization is added to the ITAE index for solving, and the historical positions and fitness of individuals and groups are saved for termination judgment. If the termination conditions are not met, the parameters are reset for the next round of solving, to form iteration until the optimization termination conditions are reached.


### 3.4. ABS Control Mode

To make full use of the advantages of motor antilock braking, the control mode of the electric vehicle antilock braking was designed under the conditions of motor maximum braking strengthKmax, maximum regenerative braking strength Kreg, and state of charge SOC.At the start of braking, the braking strength demand at the brake pedalK is compared with the maximum breaking strength of the motor Kmax. If K < Kmax, compare with the maximum regenerative braking strength Kreg; otherwise, use hydraulic ABS braking. K < Kreg, indicating that there is a charging current, enters the state of charge judgment, otherwise directly starts the motor ABS braking. If SOC < SOCmax, motor ABS braking is conducted, otherwise hydraulic ABS braking is adopted.


## 3.1. Vehicle Dynamics Model

The following is the vehicle 7-DOF dynamic mathematical model:(3)wq˙i−γ⋅qj=Fifl+Firrcosδ−Fjfl+Fjfrsinδ+Firl+Firrwq˙j+γ⋅qi=Fifl+Firsinδ+Fjfl+Fjfrcosδ+Fjrl+FjrrXk⋅γ˙=Fifl+Fifrsinδ+Fjfl+Fjfrcosδg+Fifr−Fiflcosδ+Fjfl−Fjfrsinδnm12+Firr−Firlnm22−Fjrl+Fjrrh,Yω˙x=−R⋅Fix−Nhx+Ndxwhere δ is the front wheel angle, rad. qi,qj are longitudinal and transverse speeds respectively, m/s. γ is the yaw velocity, rad/s. N. x = fl, fr, rl, rr corresponding to the left front wheel, the right front wheel, the left rear wheel, and the right rear wheel, respectively. ωx is the angular velocity of each tire, rad/s. w is the vehicle mass, kg. g. N = g + h is the wheelbase of the vehicle, m. nm1 is the front axle wheelbase, m. nm2 is the rear axle wheelbase, m. Xk is the vehicle around the k axis, kg·m2. ba is the distance from the center of mass to the ground, m. R is the effective radius of the tire, m. Nhx and Ndx are braking torque and driving torque of each tire, respectively, N·m.Since this paper mainly studies the slip rate control of the ABS, the linear control dynamic system is taken as the first-order inertia system, and its transfer function is as follows:(4)Nhxs=ωhs+ωh.In the formula,ωh is the zero point of the transfer function of the linear control dynamic system, which is also the bandwidth of the actuator, rad/s.


## 3.2. Tire Model

To express the friction characteristics of tires and roads more accurately, a LuGre dynamic tire model was established, which can describe the jumping and hysteresis of friction.(5)Flx=σ0lk+σ1lk˙+σ2lqrlFTxk˙lζ,n=qrl−κlR|ω|+σ0lqrlθaqrlklζ,naqrl=μc+μs−μce−qrl/qs,where L ∈ (i, j), ζ represents the coordinate axis, and αx represents the sideslip angle of each tire. Fl is the friction between the tire and the road, N. σ0l is rubber stiffness, m−1. σ1l is the rubber damping coefficient, s/m. FTx is the vertical load of the tire. qrl=Rω−qx is the relative velocity, m/s. qx is the translation speed of the tire axle, m/s. κl is the pressure distribution coefficient and its value is κl=7/6w=8.3m where w is the length of tire marks, m. θ is the road adhesion factor. μc is the coulomb friction. μs is the static friction force. qs is the Stribeck velocity, m/s, which represents the switching speed of the two friction states.The vertical load of each tire is shown as follows:(6)FTf=wah2N−wq˙iba2N−wq˙jbanm1⋅hNFTr=wah2N−wq˙iba2N+wq˙jbanm1⋅hNFTr=wag2N+wq˙iba2N−wq˙jbanm1⋅gNFTTr=wag2N+wq˙iba2N+wq˙jbanm1⋅gN.The formula for the sideslip angle of each tier is as follows:(7)αfl=δ−arctanqj+gγqi−nm1/2γαfr=δ−arctanqj+gγqi+nm1/2γαrl=−arctanqj−hγqi−nm2/2γαrr=−arctanqj−hγqi+nm2/2γ.The longitudinal speed of each wheel center in the wheel coordinate system is as follows:(8)qfl=qi−nm12γcosδ+qj+gγsinδqfr=qi+nm12γcosδ+qj+gγsinδqrl=qi−nm22γqrr=qi+nm22γ.The slip rate of each wheel is as follows:(9)λfl=ωflR−qflqflλfr=ωfrR−qfrqfrλrl=ωrlR−qrlqrlλrr=ωrrR−qrrqrr.


## 3.3. Fuzzy Control Strategy

The dynamic antiinterference ability of the control system is guaranteed by the variable theory domain fuzzy PID control. The PID parameters and the variable theory domain expansion factor are optimized by the PSO algorithm to increase the utilization degree of fuzzy rules initially set. The motor parameters are shown in Table2. In short, it is a method that uses an iterative particle swarm optimization strategy to obtain optimal tuning parameters.Table 2
Motor parameters.
ParameterValueMaximum no-load speedt0 (r·min−1)125Peak blocking currentIt (A)3.5Peak blocking torqueTt (N·m)20Armature resistanceR (Ω)1.75Torque coefficientZn (Nm·A−1)2Maximum supply voltageU (V)25Continuous blocking torqueTg (N·m)8Continuous blocking voltageUg (V)8Continuous blocking currentIg (A)10Armature inductanceL (mH)2.5Rotational inertiaJw (kg·m2)0.005Counter potential coefficient0.2The PSO algorithm is an optimization algorithm that is used to solve the best solution of the problem [25], which can obtain the optimal solution of the problem to be solved within a certain range. In the field of control, the PSO algorithm can be combined with the PID controller to realize the dynamic adjustment of parameters [26]. Individuals can obtain group motion so that the solution of the problem is orderly and the best solution is obtained [27]. Figure 1 shows the flow chart of the particle swarm optimization algorithm applied in this paper. Firstly, the initial setting of optimization parameters is carried out. After initializing the particles, the positions and fitness of individuals and groups are calculated to determine whether the termination conditions are met. If so, the results are output; if not, the particles are updated to form iteration.Figure 1
Particle swarm optimization process.To judge the approximate degree of each particle value and theoretical solution, a reasonable evaluation index should be determined and adapted to the model characteristics of the torque motor. In this paper, the integral of the time-weighted absolute error (ITAE) is selected as a function to evaluate the degree of approximation between particle value and theoretical solution (particle swarm fitness). The expression of the ITAE index is as follows:(10)ITAE:s=∫0∞en|dn,where e(n) is the deviation between the actual value and the set value and n is the optimization time.It can be seen from formula (12) that the ITAE index can be used as the characterization parameters of variables such as overshoot, adjustment time, and rise time. In this paper, the ITAE index is taken as a comprehensive evaluation factor to measure the control performance of the control process; that is, the particle swarm adaptation value reflects the situation of the comprehensive parameters such as overshoot, adjustment time, and rise time.The fuzzy controller is a PID parameter self-tuning controller. At the same time, the expansion factorsα(e) and α(EC) of input variables and LK (m) (m = P, I, D) of output variables are dynamically adjusted by using the change rate of E and EC, to realize the automatic tuning of domain factors (see Figure 2).Figure 2
Variable domain structure of fuzzy PID.According to the min-max reasoning criterion proposed by Xu et al. [20], the output of the weighted average solution after fuzzy processing is(11)ΔZw=Cx∑xμC1ΔZxw∑xμC1ΔZxw.PID parameter setting formula is as follows:(12)Zu=Zu0+ΔZu,Zx=Zx0+ΔZx,Zd=Zd0+ΔZd,where Zu0,Zx0,andZd0 are the initial set values of PID parameters. ΔZu,ΔZx,andΔZd are dynamic setting values.The actual output is as follows:(13)pz=Zuez+Zx∑x=1zex+Zdecz.To maximize the utilization of fuzzy rules and better optimize the control index, the input and output of fuzzy PID controller can be dynamically adjusted by scaling factor and scaling factor. For the quantization factor of the input variable, considering the maximum use of fuzzy rules, the principle of positive correlation between quantization factor and input variable is adopted, and the form widely used in the motor control field is selected from a variety of description methods:α=1−λ1e−zi2. For the proportional factor of output variables, combined with the influence of PID parameters on the control performance, the proportional factor of Zu and Zd is monotonically consistent with the error, and Zx scale factor and error have a monotone inverse principle, to get the scale factor empirical formula. The empirical formula of expansion factor and scale factor summarized in this paper is shown in the following formula:(14)αe=1−λ1e−z1i2,αec=1−λ2e−z2i2,βpu=λ3|e|,βpx=1e+λ4,βpd=λ5e.Each parameter of formula (14) was obtained by the PSO algorithm. Using the PSO, the inertia factor is set as 0.6 and the acceleration constant is set as 2. In this paper, there are seven parameters λ1, λ2, λ3, λ4, λ5, z1, and z2 in the expansion factor formula, plus three initial parameters Zu0,Zx0,andZd0 of PID. The dimension is set as 10, the particle swarm size is set as 30, the maximum iteration number is set as 30, and the minimum adaptation value is set as 0. The maximum number of iterations and the minimum adaptive value can be used as the termination conditions of the algorithm. The offspring of particle swarm initialization is added to the ITAE index for solving, and the historical positions and fitness of individuals and groups are saved for termination judgment. If the termination conditions are not met, the parameters are reset for the next round of solving, to form iteration until the optimization termination conditions are reached.


## 3.4. ABS Control Mode

To make full use of the advantages of motor antilock braking, the control mode of the electric vehicle antilock braking was designed under the conditions of motor maximum braking strengthKmax, maximum regenerative braking strength Kreg, and state of charge SOC.At the start of braking, the braking strength demand at the brake pedalK is compared with the maximum breaking strength of the motor Kmax. If K < Kmax, compare with the maximum regenerative braking strength Kreg; otherwise, use hydraulic ABS braking. K < Kreg, indicating that there is a charging current, enters the state of charge judgment, otherwise directly starts the motor ABS braking. If SOC < SOCmax, motor ABS braking is conducted, otherwise hydraulic ABS braking is adopted.


## 4. Result in Analysis and Discussion

### 4.1. Simulation Experiment

The ABS performance is simulated and verified under different braking conditions using the proposed slip rate control method. Simulation parameters are shown in Table3. White noise with a peak value of 1% of the output signal is added to the simulation process.Table 3
Simulation parameters.
ParameterValuew (kg)2500Xk (kg·m2)2865σ0l (m−1)195.5nm2 (m)1.85bg (m)0.8σ1l (m−1)0.95nm1 (m)1.75Y (kg·m2)1qs (m·s−1)7.5μc0.9h (m)1.469g (m)1.365A (m·s−2)10σ2l (s·m−1)0.002Xm (kg·m2)2.5N (m)2.955The bandwidth of the linear control system is set at 1s while linear brakingωh is set at 20. Make the steering angle input a step signal of 0.2 rad, and simulate the high adhesion road with the adhesion coefficient of 0.8 and the low adhesion road with the adhesion coefficient of 0.4, respectively.The results are shown in Figures3 and 4. The front wheel braking torque on a low adhesion road is stable around 600 N·m, and the rear wheel braking torque is stable around 500 N·m. On a high adhesion road surface, the front wheel braking torque is stable around 1 250 N·m, and the rear wheel braking torque is stable around 750 N·m. Since there is a step input of 0.2 rad at 1 s, the braking torque of the right wheel begins to be greater than that of the left wheel after 1 s, which conforms to the vehicle dynamics characteristics and verifies the correctness of the model.Figure 3
Braking torque curve during steering on low adhesion road.Figure 4
Braking torque curve during steering on high adhesion road.


### 4.2. Actual Vehicle Test and Analysis of ABS Braking Effect

To verify the ABS active disturbance rejection control effect, the ABS PID brake controller and the braking controller in this paper are designed, and the active disturbance rejection controller is installed in the test vehicle. The vehicle parameters are shown in Table4.Table 4
Vehicle parameters.
Length, width, height (mm)2850, 1320, 1880Wheelbase (mm)2050Wheel radiusR (mm)0.5Vehicle massW (kg)1500Rated powerP (kW)5Maximum speedvmax (km·h−1)50Driving formRear wheel drivePower formACThe smooth road test scheme is as follows: the test vehicle starts braking at the initial speed of 25 m·s−1 on the cement road with no load.The docking road test scheme was as follows: the test vehicle was unloaded, started braking at the initial speed of 25 m·s−1 on the ice surface, and drove 20 m to the cement road surface.The speed, wheel speed, slip rate, and braking torque of the smooth road test are shown in Figures5–7.Figure 5
Braking performance based on the proposed controller.Figure 6
Braking performance based on PID controller.Figure 7
Comparison of braking torque between the proposed controller and PID controller.The final braking distance and final braking time of the real vehicle test are extracted from Figures5–7, as shown in Table 5.Table 5
Simulation and experimental results of final braking distance and final braking time.
ParameterSimulation dataExperimental dataProposedPIDProposedPIDFinal braking time (s)2.7312.8862.8283.022Final braking distance (m)34.5538.3436.1140.38It can be seen from Figures5–7 that the measured braking test results of cement pavement are very close to the simulation results of typical cement pavement with the optimal slip rate of 0.2. The test verifies that fuzzy PID control based on the variable theory domain has shorter final braking distance, less final braking time, and better braking performance than ABS based on PID control. It can be seen from Table 5 that the test data are slightly larger than the simulation data, mainly because the optimal slip rate of cement pavement used in real vehicle tests is slightly greater than 0.2. Figures 5–7 and Table 5 verify the correctness of the simulation models and simulation results.


## 4.1. Simulation Experiment

The ABS performance is simulated and verified under different braking conditions using the proposed slip rate control method. Simulation parameters are shown in Table3. White noise with a peak value of 1% of the output signal is added to the simulation process.Table 3
Simulation parameters.
ParameterValuew (kg)2500Xk (kg·m2)2865σ0l (m−1)195.5nm2 (m)1.85bg (m)0.8σ1l (m−1)0.95nm1 (m)1.75Y (kg·m2)1qs (m·s−1)7.5μc0.9h (m)1.469g (m)1.365A (m·s−2)10σ2l (s·m−1)0.002Xm (kg·m2)2.5N (m)2.955The bandwidth of the linear control system is set at 1s while linear brakingωh is set at 20. Make the steering angle input a step signal of 0.2 rad, and simulate the high adhesion road with the adhesion coefficient of 0.8 and the low adhesion road with the adhesion coefficient of 0.4, respectively.The results are shown in Figures3 and 4. The front wheel braking torque on a low adhesion road is stable around 600 N·m, and the rear wheel braking torque is stable around 500 N·m. On a high adhesion road surface, the front wheel braking torque is stable around 1 250 N·m, and the rear wheel braking torque is stable around 750 N·m. Since there is a step input of 0.2 rad at 1 s, the braking torque of the right wheel begins to be greater than that of the left wheel after 1 s, which conforms to the vehicle dynamics characteristics and verifies the correctness of the model.Figure 3
Braking torque curve during steering on low adhesion road.Figure 4
Braking torque curve during steering on high adhesion road.


## 4.2. Actual Vehicle Test and Analysis of ABS Braking Effect

To verify the ABS active disturbance rejection control effect, the ABS PID brake controller and the braking controller in this paper are designed, and the active disturbance rejection controller is installed in the test vehicle. The vehicle parameters are shown in Table4.Table 4
Vehicle parameters.
Length, width, height (mm)2850, 1320, 1880Wheelbase (mm)2050Wheel radiusR (mm)0.5Vehicle massW (kg)1500Rated powerP (kW)5Maximum speedvmax (km·h−1)50Driving formRear wheel drivePower formACThe smooth road test scheme is as follows: the test vehicle starts braking at the initial speed of 25 m·s−1 on the cement road with no load.The docking road test scheme was as follows: the test vehicle was unloaded, started braking at the initial speed of 25 m·s−1 on the ice surface, and drove 20 m to the cement road surface.The speed, wheel speed, slip rate, and braking torque of the smooth road test are shown in Figures5–7.Figure 5
Braking performance based on the proposed controller.Figure 6
Braking performance based on PID controller.Figure 7
Comparison of braking torque between the proposed controller and PID controller.The final braking distance and final braking time of the real vehicle test are extracted from Figures5–7, as shown in Table 5.Table 5
Simulation and experimental results of final braking distance and final braking time.
ParameterSimulation dataExperimental dataProposedPIDProposedPIDFinal braking time (s)2.7312.8862.8283.022Final braking distance (m)34.5538.3436.1140.38It can be seen from Figures5–7 that the measured braking test results of cement pavement are very close to the simulation results of typical cement pavement with the optimal slip rate of 0.2. The test verifies that fuzzy PID control based on the variable theory domain has shorter final braking distance, less final braking time, and better braking performance than ABS based on PID control. It can be seen from Table 5 that the test data are slightly larger than the simulation data, mainly because the optimal slip rate of cement pavement used in real vehicle tests is slightly greater than 0.2. Figures 5–7 and Table 5 verify the correctness of the simulation models and simulation results.


## 5. Conclusion

The Antilock Braking System can improve the braking performance of vehicles in extreme conditions, but the braking system has the characteristics of uncertainty, time variability, and nonlinear. To ensure the handling stability and safety of automobile braking and further realize the optimal control of antilock braking systems under various working conditions, this paper designed a set of automobile antilock braking control systems based on variable theory domain fuzzy PID. The simulation experiment and real vehicle test of the system, respectively, show that the designed control system can effectively reduce chattering and has good effectiveness, robustness, and stability. Although the designed system has strong robustness and is suitable for vehicle driving, some problems need to be improved: (1) the ABS simulation system is not perfect enough. This paper adopts a relatively simple model to build the ABS simulation model, and it is more ideal. (2) The analysis of test road conditions is not comprehensive enough. Special braking conditions such as ramp parking and bend braking have not been involved. How to recognize the complex road conditions and design the road recognition module to act on the tyre model will become an important problem to further improve the model. The next research focus will be to analyze the braking performance of the vehicle comprehensively and ensure the safety and handling stability of the vehicle through braking simulation and tests of various scenarios.



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*Source: 5001185-2022-07-08.xml*