Clamping force control method for electronic mechanical braking system

Abstract

The invention discloses a clamping force control method for an electronic mechanical braking system, and belongs to the technical field of automobile brake-by-wire. The method comprises the following steps: step 1, establishing a mechanical model of the electronic mechanical braking system; step 2, based on the mechanical model, designing a high-order linear extended state observer HLESO for estimating total disturbance and uncertain items of the system; 3, the total disturbance of the system observed by the HLESO serves as a feedforward control item, and a fast terminal sliding mode controller FTSM is designed; 4, designing a fuzzy corrector to carry out smooth transition on the discontinuous characteristics of the switch control items of the FTSM; and 5, when the vehicle enters a braking state, braking is achieved through the HLESO-FTSM control strategies in the steps 3-4. According to the invention, the long-term stability of the EMB system under complex working conditions is improved, the control process is simplified, and the control bandwidth and response speed of the system are greatly improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of automobile wire-controlled braking, and in particular relates to a clamping force control method for an electronic mechanical braking system. Background Art

[0002] As a next-generation braking technology, the Electro-Mechanical Brake (EMB) system is gradually replacing traditional hydraulic brake systems with its high efficiency, environmental friendliness, and low maintenance costs, becoming a key development direction in the aerospace and automotive industries. Compared to traditional hydraulic brake systems, the EMB system eliminates the hydraulic circuit and instead utilizes an electromechanical actuator (EMA) to directly drive the brake disc, enabling precise control of braking force. This technological innovation not only improves system response speed but also effectively mitigates the risk of hydraulic oil leakage, enhancing the safety and reliability of the braking system. Furthermore, the EMB system offers excellent scalability and can integrate advanced technologies such as energy recovery and intelligent control, further promoting the development of intelligent braking systems.

[0003] However, despite its many advantages, the EMB system still faces numerous challenges in achieving high-precision clamping force control, primarily due to the highly nonlinear characteristics of the actuator, complex friction mechanisms, and external load disturbances. First, EMA typically utilizes mechanical transmission mechanisms such as ball screws or reducers. Unavoidable problems such as clearance, friction, and elastic deformation exist during the transmission process, leading to uncertainty in the clamping force output. Second, during long-term operation, the mechanical components of the EMA will wear, causing the system parameters to drift, making it difficult for control strategies based on fixed parameters to maintain long-term control accuracy. Furthermore, external environmental factors (such as temperature changes and vibration disturbances) further affect the stability of clamping force control.

[0004] To address these issues, researchers have proposed various control strategies. In early studies, proportional-integral-derivative (PID) control became the mainstream control method for EMB systems due to its simple structure and wide applicability. The typical three-loop PID control structure improves system performance through three-level regulation of current, speed, and clamping force. However, due to the coupling of the three loops, PID parameter tuning is complex and difficult to address system nonlinearities. Adaptive PID control methods have improved control effectiveness to a certain extent by dynamically adjusting parameters, but they still struggle to overcome the highly nonlinear characteristics of EMB systems.

[0005] The nonlinear characteristics of friction are a key factor affecting the accuracy of clamping force control. To improve the system's adaptability to friction variations, researchers have introduced fuzzy logic control (FLC). However, the bandwidth limitations of traditional cascaded PID control systems restrict their dynamic response capabilities, making it difficult to meet the requirements of high-speed, high-precision braking control.

[0006] To enhance the robustness of control systems, researchers have introduced more advanced control methods. For example, H∞ control can effectively suppress actuator disturbances, but its relatively conservative control strategy can lead to sluggish system response. Sliding mode control (SMC) is considered an effective approach for EMB systems due to its robustness to system uncertainties and external disturbances. However, the inherent high-frequency switching characteristics of sliding mode control can easily cause chattering, which affects actuator life and control accuracy. Summary of the Invention

[0007] To solve the above technical problems, the present invention provides a clamping force control method for an electronic mechanical brake system, constructing a fast terminal sliding mode (FTSMC) fuzzy control strategy based on a high-order extended state observer (HLESO) to simultaneously solve the problems of vibration suppression, disturbance compensation and dynamic response optimization.

[0008] To achieve the above object, the present invention adopts the following technical solutions:

[0009] A method for controlling the clamping force of an electromechanical brake system, comprising:

[0010] Step 1: Establish a mechanical model of the electromechanical brake system;

[0011] Step 2: Based on the mechanical model, a high-order linear extended state observer (HLESO) is designed to estimate the total disturbance and uncertainty of the system.

[0012] Step 3: Using the total disturbance of the system observed by the HLESO as a feedforward control term, a fast terminal sliding film controller FTSM is designed;

[0013] Step 4: Design a fuzzy corrector to smoothly transition the discontinuous characteristics of the switch control items of the FTSM;

[0014] Step 5: When the vehicle enters the braking state, braking is achieved through the HLESO-FTSM control strategy of steps 3 and 4.

[0015] In a second aspect, the present invention provides an electronic device comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned method for controlling the clamping force of an electronic mechanical brake system.

[0016] In a third aspect, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the aforementioned method for controlling the clamping force of an electronic mechanical brake system.

[0017] The beneficial effects of the present invention are:

[0018] The present invention does not require a precise system model and does not need to consider interference factors such as motor speed, acceleration, and additional disturbances. It can effectively deal with uncertainties such as actuator aging and environmental changes, and improve the long-term stability of the EMB system under complex working conditions.

[0019] A three-mode switching control strategy for the EMB system is proposed. According to the working characteristics and control requirements of the EMB system under different working conditions, multi-mode switching control is adopted to improve the dynamic response capability of the system while avoiding the loss of kinetic energy caused by friction and the reduction of endurance, so as to optimize the overall performance.

[0020] The HLESO is introduced to accurately estimate the total disturbance and uncertainty of the system and serve as a feedforward compensation. The disturbance uncertainty only needs to be slightly larger than the estimation error, which reduces chattering without sacrificing system stability. Compared with ordinary ESO, it can better suppress low-frequency disturbances.

[0021] Combined with the FTSMC strategy, only the clamping force loop and current loop dual closed-loop control are adopted, which simplifies the control process and greatly improves the system control bandwidth and response speed. While ensuring finite time convergence, the chattering phenomenon of traditional sliding mode control is reduced through disturbance observer compensation and fuzzy control, thereby improving control smoothness without reducing system stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 Schematic diagram of the structure of the electronic mechanical brake system;

[0023] Figure 2 This is a block diagram of the overall control strategy for a clamping force control method for an electromechanical brake system;

[0024] Figure 3 is the membership function of the input and output variables of the fuzzy corrector, where (a) is the membership function of the input variable and (b) is the membership function of the output variable;

[0025] Figure 4 This is the block diagram of the HLESO-FTSM controller;

[0026] Figure 5 (a) shows the comparison between the HLESO-FTSM and PID algorithm results under 0-10KN step response;

[0027] Figure 5(b) shows the comparison between the HLESO-FTSM and PID algorithm results under continuous small step response;

[0028] Figure 6 (a) shows the control performance of HLESO-FTSM under 1 Hz frequency condition;

[0029] Figure 6 (b) shows the PID performance diagram under 1 Hz frequency conditions;

[0030] Figure 6 (c) shows the control performance of HLESO-FTSM under 2 Hz frequency conditions;

[0031] Figure 6 (d) is the PID performance diagram under 2HZ frequency conditions;

[0032] Figure 6 (e) shows the control performance of HLESO-FTSM under 4 Hz frequency conditions;

[0033] Figure 6 (f) is the PID performance diagram under 4HZ frequency conditions.

[0034] Reference numerals:

[0035] 1-brake pad, 2-transmission piston, 3-ball screw, 4-reduction mechanism, 5-permanent magnet synchronous motor, 6-brake disc. DETAILED DESCRIPTION

[0036] The present invention will be further described below with reference to the accompanying drawings and examples.

[0037] Figure 1 The overall structure of an electromechanical brake (EMB) is shown. The core structure includes a permanent magnet synchronous motor 5, a reduction gear 4 (typically a planetary gear), a brake pad 1, a transmission piston 2, a ball screw 3, a force sensor, and a brake disc 6. The motor drives the reduction gear 4, transmitting rotational motion to the ball screw 3, which converts the rotational motion into linear motion. This pushes the brake disc 6 to apply a clamping force toward the wheel, achieving braking.

[0038] like Figure 2 As shown, based on Figure 1 The present invention provides a method for controlling the clamping force of an electronic mechanical brake system, comprising:

[0039] Step 1: Establish a mechanical model of the electronic mechanical brake system, including the dynamic equations of the motor, the dynamic equations of the reduction mechanism and the ball screw, and the brake clamping force equation of the brake disc;

[0040] The magnetic screw electromechanical brake system consists of a drive motor, a reduction mechanism, a ball screw, a brake disc, and other components. The models of its key components are presented here. Ignoring the changes in the electromagnetic characteristics within the PMSM, the PMSM's dynamic equations can be expressed as follows:

[0041] (1)

[0042] in, is the mechanical angle of the motor, is the mechanical speed of the motor, is the equivalent moment of inertia of the motor, is the torque of the motor, is the friction coefficient of the motor, is the load torque of the motor, and the superscript · indicates the first-order time derivative.

[0043] The reduction mechanism and ball screw convert the motor's linear motion into rotational motion. The dynamic equation is expressed as follows:

[0044] (2)

[0045] in, is the linear displacement of the ball screw, is the speed ratio of the reducer, L is the lead of the ball screw, is the brake clamping force of the brake disc.

[0046] The brake disc is clamped to generate a clamping force, which is expressed by the following mechanical equation:

[0047] (3)

[0048] in, is the clearance of the brake disc, It is the linear region of clamping force.

[0049] Furthermore, the brake disc clamping force equation can be simplified as follows:

[0050] (4)

[0051] in, is the stiffness coefficient in the EMB linear region.

[0052] Setting state variables , then its dynamic model can be expressed as:

[0053] (5)

[0054] in, Is to simplify the brake disc clamping force, is the motor speed, , is the system disturbance term.

[0055] Step 2: Design a high-order linear extended state observer (HLESO) to estimate the total disturbance and uncertainty of the system;

[0056] In order to improve the anti-interference ability of the current loop in the low frequency band, a high-order linear extended state observer is used to estimate the disturbance, and Equation (5) is written as a controllable standard form:

[0057] (6)

[0058] Where, is the differential function of the clamping force, y(t) represents the system output, and t represents time, that is, the output is a variable related to time.

[0059] To improve the system's immunity to low-frequency disturbances and achieve a more accurate estimation of the system's state variables and total disturbances, a high-order EOS is used to observe disturbances. The observer is designed as follows:

[0060] (7)

[0061] in, is the total disturbance and uncertainty of the system, is the motor torque coefficient, Indicates the clamping force error, Indicates the reference value of clamping force, is the observer gain, i=1, 2, 3, 4, is the actual total disturbance of the system, is the derivative of the actual total disturbance of the system, is the expanded state of the actual disturbance of the system.

[0062] Observer gain , i=1, 2, 3, 4 are selected as , , , , denotes the poles of the observer equation configuration.

[0063] Due to the observation error, the actual total disturbance of the system is Should be expressed as:

[0064] (8)

[0065] in, represents the observation error, represents the total disturbance of the observed system. Since the proposed observer has been proven to be stable, the estimation error is bounded.

[0066] (9)

[0067] in, represents an upper bound on the estimation error.

[0068] Step 3: Precise control of clamping force: The total disturbance of the system observed by the HLESO observer is As a feedforward control term, a fast terminal sliding film controller (FTSM) is designed:

[0069] The observer observes the total disturbance of the system Substituting into the controllable standard form we can get:

[0070] (10)

[0071] Among them, the input gain , u represents the input current of the sliding membrane controller, that is, the control law.

[0072] Assumed clamping force reference value is continuous and has a second-order derivative. The error variable is defined as And the sliding mode s is designed as:

[0073] (11)

[0074] Where, . are the first and second derivatives of the error variable; 、 、 、 is the adjustment coefficient of the sliding surface s; when the system reaches the ideal sliding mode ( ), if the following conditions are met, the error variable It converges to 0 in a finite time:

[0075] (12)

[0076] The FTSM control law is:

[0077] (13)

[0078] in, is an equivalent control item, For switch control items.

[0079] The equivalent control term aims to keep the system state trajectory on the preset sliding surface after reaching the sliding surface. The derivation process of the equivalent control term is as follows. The derivative of s is:

[0080] (14)

[0081] make , we can get the equivalent control term for:

[0082] (15)

[0083] If the uncertainty function of the compensated system Described as:

[0084] (16)

[0085] in, is the uncertainty of the control gain.

[0086] The uncertainty of the system satisfies the following conditions:

[0087] (17)

[0088] in, Indicates the upper limit of the system uncertainty. Switch control term The following arrival conditions must be met:

[0089] (18)

[0090] Switch Control for:

[0091] (19)

[0092] in, represents the symbolic function, represents the switching gain, is the adjustment coefficient.

[0093] Step 4: Fuzzy corrector design: Due to the existence of the switch control item in the FTSM controller, the system will cause chattering. The fuzzy corrector is designed to smoothly transition the discontinuous characteristics of the switch control item, effectively weaken the chattering phenomenon, and improve the stability and control accuracy of the system.

[0094] The switching term in sliding film control is an important factor to ensure the robustness of the system. Even if the state of the system deviates from the preset sliding surface, the switching term can still force the system to return to the sliding surface. However, the discontinuity of the switching control term will inevitably cause chattering when the state trajectory reaches the sliding surface. Therefore, this paper studies the switching control term. Add fuzzy control to suppress chattering problems.

[0095] The input of the blur corrector is and , the output is the corrected switch control term The physical domain of the input variable is quantized into the fuzzy set {-1, 0, 1}, and the physical domain of the output variable is quantized into {-4, -3, -2, -1, 0, 1, 2, 3, 4}. According to its range of variation, the fuzzy value of the input variable is divided into negative (N), zero (ZE), and positive (P), and a triangular membership function that is easy to design, has high resolution, and is sensitive to control is used, such as Figure 3 As shown in Figure 1, (a) is the membership function of the input variable, and (b) is the membership function of the output variable. The output fuzzy values ​​include negative high (NH), negative large (NB), negative medium (NM), negative small (NS), zero (ZE), positive small (PS), positive medium (PM), positive large (PB), and positive high (PH), ensuring that the output changes smoothly and avoids violent fluctuations, thereby ensuring the stable operation of the system. The membership function also uses trigonometric functions. The fuzzy rules are designed according to inequality (18), as shown in Table 1.

[0096] Table 1

[0097]

[0098] Figure 4 The detailed structure of the EMB system brake controller is shown. The controller mainly consists of a high-order linear extended state observer (HLESO) to estimate the total system disturbance and a fast terminal sliding mode (FTSM) control module to control the EMB brake clamping force.

[0099] In each sampling period, the motor Shaft current and the clamping force of the EMB system The clamping force is measured by a sensor and at the same time, the reference value is generated by the upper controller. Then, the total disturbance of the system is observed according to the system state through the high-order linear extended state observer (HLESO) of formula (7). , which includes the friction variation of the ball screw-nut mechanism, contact nonlinearity, and external uncertainties, and performs disturbance compensation in the feedforward control. However, since the discontinuity of the switching control term may cause chattering, the original switching control term is smoothed by a fuzzy controller to reduce the chattering effect. Finally, the control law input is calculated as , and is transmitted to the permanent magnet synchronous motor (PMSM), driving the EMB system to perform braking action. The above control process is repeated in each sampling cycle to ensure high-precision clamping force control and excellent dynamic response characteristics of the EMB system.

[0100] Step 5: Determine the state of the electronic mechanical brake system based on the vehicle operating state, target clamping force, and actual clamping force feedback signals, and switch to different control strategies according to different states;

[0101] In this control strategy, the EMB state can be divided into three stages: braking, coasting, and driving. State switching is based on the vehicle's operating conditions, target clamping force, and feedback signals from actual clamping force.

[0102] When the vehicle enters the braking state, the system receives the target clamping force signal and uses the HLESO-FTSM control strategy to improve anti-interference ability and dynamic response characteristics, accurately control the clamping force, and realize the braking process.

[0103] When the vehicle enters coasting mode, the powertrain no longer outputs drive torque. Instead, the brake clearance is adjusted through a dual closed-loop position-current control system to minimize response delay during the next braking operation. Because brake pad wear reduces thickness, dynamic brake clearance adjustment is required. Therefore, the EMB system performs a clamping action upon each power-up initialization. The clamping force sensor measures the contact position between the brake pad and the brake disc to calculate a compensation value and optimize the clearance setting. This strategy not only helps shorten brake response time but also prevents insufficient braking force due to excessive wear.

[0104] When the vehicle enters the driving state, the vehicle powertrain begins providing driving force. To reduce drag losses and ensure normal system operation, the braking torque must be released to return the brake pads to their initial position. During this phase, the system employs a position control strategy to restore the brake pads to their set initial position. This not only effectively reduces energy loss caused by incomplete brake pad release, but also ensures optimal system operation during the next braking event.

[0105] The present invention is proved to be stable:

[0106] The Lyapunov function is defined as:

[0107] (20)

[0108] Taking the derivative of the above function we get:

[0109] (twenty one)

[0110] Bringing it into the controller gives:

[0111] (twenty two)

[0112] Therefore the system is stable.

[0113] To fully simulate the braking conditions of the EMB system in actual driving scenarios, both step response and sinusoidal response tests were used, corresponding to emergency braking and ABS system modulation during braking, respectively. A traditional cascade PID controller was also used for comparison.

[0114] The experiment tested the scenario of entering braking from the coasting state, and the braking clearance was 1mm at this time.

[0115] (1) Step experiment

[0116] In the step response test, a 0-10 kN step response is first presented as a reference signal. Figures 5(a) and 5(b) show the comparative experimental results. It can be seen that the HLESO-FTSM eliminates the backlash and reaches the target clamping force in approximately 110 ms, surpassing the response speed of traditional PID control. This translates to a shorter braking distance. Furthermore, the proposed HLESO-FTSM controller achieves faster response while also reducing overshoot, resulting in more precise braking control.

[0117] (2) Sine experiment

[0118] Here, a sinusoidal signal with a 5 kN amplitude modulated by 50% at frequencies of 1 Hz, 2 Hz, and 4 Hz is used as the target clamping force. The test results are shown in Figures 6(a)-6(f). Figures 6(a)-6(f) compare the control performance under 1 Hz, 2 Hz, and 4 Hz sinusoidal signals, respectively. As can be seen from the comparison of Figures 6(a)-6(b), the control performance of the two algorithms is nearly identical at 1 Hz. The clamping force error bounds of the proposed HLESO-FTSM algorithm are slightly smaller than those of the PID algorithm. However, the clamping force curve exhibits some chattering due to the influence of the switching control term when approaching the target clamping force. As the reference clamping force frequency increases, the lag phase of the actual clamping force curve increases. However, the proposed HLESO-FTSM algorithm still tracks the clamping force command well, maintaining low error bounds while ensuring the amplitude reaches approximately 98% of the command. The error bounds at 4 Hz are similar to those of the PID algorithm at 2 Hz, demonstrating good system responsiveness. As the command frequency increases, the PID control amplitude decreases, and the error bounds are higher than those of the HLESO-FTSM algorithm, as shown in Figures 6(c)-6(f). The proposed HLESO-FTSM algorithm also significantly outperforms PID control in terms of phase lag. This is because the current command is directly generated from the clamping force loop, simplifying the control process and improving the control response speed.

[0119] In a second aspect, the present invention provides an electronic device comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned method for controlling the clamping force of an electronic mechanical brake system.

[0120] In a third aspect, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the aforementioned method for controlling the clamping force of an electronic mechanical brake system.

[0121] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for controlling the clamping force of an electromechanical brake system, characterized in that: include: Step 1: Establish a mechanical model of the electromechanical brake system; Step 2: Based on the mechanical model, a high-order linear extended state observer (HLESO) is designed to estimate the total disturbance and uncertainty of the system. Step 3: Using the total disturbance of the system observed by the HLESO as a feedforward control term, a fast terminal sliding film controller FTSM is designed; Step 4: Design a fuzzy corrector to smoothly transition the discontinuous characteristics of the switch control items of the FTSM; Step 5: When the vehicle enters the braking state, braking is achieved through the HLESO-FTSM control strategy of steps 3 and 4.

2. The method for controlling the clamping force of an electronic mechanical brake system according to claim 1, wherein: In step 1, the mechanical model of the electronic mechanical brake system includes the motor dynamics equation, the speed reduction mechanism and the ball screw dynamics equation, and the brake disc clamping force equation.

3. The method for controlling the clamping force of an electronic mechanical brake system according to claim 2, wherein: The brake disc clamping force equation is: （4） in, represents the simplified brake disc clamping force, is the stiffness coefficient in the EMB linear region, is the clearance of the brake disc, is the linear displacement of the ball screw; Setting state variables , then the dynamic model of the brake disc clamping force is expressed as: （5） in, Is to simplify the brake disc clamping force, is the mechanical speed of the motor, , is the system disturbance term, is the speed ratio of the reducer, L is the lead of the ball screw, is the equivalent moment of inertia of the motor, is the torque of the motor, is the friction coefficient of the motor.

4. The method for controlling the clamping force of an electronic mechanical brake system according to claim 3, wherein: The high-order linear extended state observer HLESO designed in step 2 is: （7） in, Indicates the clamping force error, represents the clamping force reference value, y(t) represents the system output, t represents time, is the total disturbance and uncertainty of the system, is the motor torque coefficient, is the observer gain, i=1, 2, 3, 4, is the actual total disturbance of the system, is the derivative of the actual total disturbance of the system, is the expanded state of the actual disturbance of the system, is the differential function of the clamping force, and the superscript · indicates the first-order derivative; Actual total disturbance of the system Expressed as: （8） in, represents the observation error, represents the total disturbance of the system under observation.

5. The method for controlling the clamping force of an electronic mechanical brake system according to claim 4, wherein: Designing the fast terminal sliding membrane controller FTSM in step 3 includes: The observer observes the total disturbance of the system Substituting into the controllable standard form: （10） Among them, the input gain , u represents the input current of the sliding membrane controller FTSM, that is, the control law: （13） in, is an equivalent control item, For switch control items.

6. The method for controlling the clamping force of an electronic mechanical brake system according to claim 5, characterized in that: The switch control item for: （19） in, represents the symbolic function, represents the switching gain, is the sliding surface adjustment coefficient.

7. The method for controlling the clamping force of an electromechanical brake system according to claim 1, wherein: The step 4 includes inputting the sliding mode and the first-order derivative of the sliding film controller into the fuzzy corrector, processing the input based on the triangular membership function, and outputting a corrected switch control item.

8. The method for controlling the clamping force of an electromechanical brake system according to claim 1, wherein: Step 5 includes determining the state of the electronic mechanical brake system according to the vehicle operating state, the target clamping force, and the actual clamping force feedback signal, and switching different control strategies according to different states: When the vehicle enters the braking state, the system receives the target clamping force signal and implements the braking process through the HLESO-FTSM control strategy; When the vehicle enters the coasting state, the power system no longer outputs driving torque, and the braking clearance is adjusted through position-current dual closed-loop control; When the vehicle enters the driving state, the braking torque is released and the system adopts the position control strategy to return to the set initial position.

9. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs; When one or more programs are executed by the one or more processors, the one or more processors implement the clamping force control method of an electronic mechanical brake system according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that Executable instructions are stored thereon, and when the instructions are executed by a processor, the processor can implement a clamping force control method for an electronic mechanical brake system as described in any one of claims 1-8.

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