Intelligent driving control system based on model predictive control

Abstract

The application discloses a kind of intelligent driving control systems based on model predictive control, through control rack position and rack speed double closed-loop control to realize the angle of steering wheel execution angle follow intelligent driving system in the request of host computer, the intelligent driving control system based on model predictive control includes: rack position ring module, according to expected steering wheel angle output expected rack speed;Rack speed ring module, according to the expected rack speed to establish prediction model.The present application is aimed at the closed-loop intelligent driving control system with uncertainty disturbance, the rack speed ring uses model predictive control relative to fixed PI control, not only can realize accurate control, but also can solve the problem of present PI parameter difficult debugging, difficult to match various working conditions and poor robustness.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent auxiliary driving control, in particular to an intelligent driving control system based on model predictive control. BACKGROUND

[0002] With the increasing attention to automatic driving, intelligent driving function is widely used, which assists the driver to drive and improves the comfort and safety of driving. Vehicle lateral control is the key to realize auxiliary driving and unmanned driving technology, and the electric power steering system (EPS) is the actuator of vehicle lateral control, so the lateral control of EPS is crucial. Since the direct torque control is an open-loop control relative to the lateral control of EPS, although it responds quickly, the closed-loop control based on angle control has better stability than the direct torque control.

[0003] The lateral control based on angle control is a closed-loop control, which generally uses PI control. Although the PI control structure is simple and easy to implement, its PI parameters are strictly dependent on the tuning of engineering experience, and in actual working conditions, the current auxiliary driving allows the driver and the intelligent driving function to act at the same time, which brings uncertain disturbance to the intelligent driving control. At this time, a fixed set of PI parameters is difficult to meet the performance requirements in different working conditions and has poor robustness. SUMMARY

[0004] The present application provides an intelligent driving control method based on model predictive control. For the closed-loop intelligent driving control system with uncertain disturbance, the model predictive control is used instead of the fixed PI control, which can solve the problems of difficult parameter tuning and poor robustness.

[0005] To solve the above technical problems, the present application provides an intelligent driving control system based on model predictive control, which realizes the angle following of the steering wheel executed by the host computer requested angle in the angle following intelligent driving system through double closed-loop control of the rack position and the rack speed. The intelligent driving control system based on model predictive control comprises:

[0006] A rack position loop module outputs the expected rack speed according to the expected steering wheel angle.

[0007] A rack speed loop module establishes a prediction model according to the expected rack speed.

[0008] Preferably, it further comprises a safety verification module and a safety limiting module, and the rack speed loop module establishes a prediction model after safety verification and safety limiting.

[0009] Preferably, the rack speed loop module comprises:

[0010] An actual rack speed calculation module for calculating the actual rack speed.

[0011] The model predictive controller receives the speed information calculated by the actual rack speed calculation module, and then establishes a prediction model.

[0012] Preferably, the model predictive controller calculates the model construction step as follows:

[0013] Step 1, obtain the desired rack speed and actual rack speed, and construct the relationship between the rack speed and the mechanical speed of the motor;

[0014] Step 2, calculate the mechanical angular speed of the motor and the desired speed, establish a value function and find the limit value.

[0015] Preferably, the model predictive controller calculates the model construction further comprising the following steps:

[0016] Step 11, after obtaining the desired rack speed and actual rack speed, predict the error.

[0017] Preferably, the model predictive controller calculates the model construction further comprising the following steps:

[0018] Step 12, after the step 1 or step 11, establish a prediction model, and then establish a value function and find the limit value.

[0019] Preferably, the relationship between the rack speed and the mechanical angular speed of the motor end is:

[0020]

[0021] where ρ n is the number of pole pairs, v is the actual rack speed, ω is the motor rotor speed of the EPS, ω m is the mechanical angular speed of the motor end of the EPS, k v is the transmission ratio from the motor end to the rack position.

[0022] Preferably, the value function established in the step 2 is as follows:

[0023] f(k+1) = [ω r (k+1) - ω p (k+1)] 2 + [ΔT e (k)] 2 ;

[0024] where ω r is the desired speed, ω p is the predicted speed, ΔT e (k) is the change amount of electromagnetic torque at time k, ω p (k+1) is the predicted speed at time k+1, and ω r is the predicted speed at time k+1.(k+1) is the expected rotation speed at k+1 moment.

[0025] Preferably, the expected rotation speed ω r is different from the actual rotation speed ω m , and the formula in two continuous periods is:

[0026] BRIEF DESCRIPTION OF DRAWINGS

[0027] The present application will be further described below in conjunction with the accompanying drawings and specific embodiments:

[0028] Figure 1 The schematic diagram of the overall system architecture of a preferred embodiment of the present application.

[0029] Figure 2 The simple mechanical structure diagram between the motor and the rack of the EPS of the present application.

[0030] Figure 3 The structure diagram of the model predictive controller of the rack speed loop of the present application.

[0031] Figure 4 The system control schematic diagram of the present application. DETAILED DESCRIPTION

[0032] The embodiments of the present application will be described below through specific embodiments, and those skilled in the art can fully understand other advantages and technical effects of the present application from the disclosure of the present specification. The present application can also be implemented or applied through different specific embodiments, and each detail in the present specification can be applied based on different viewpoints, and various modifications or changes can be made without departing from the general design idea of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. The exemplary embodiments of the present application can be implemented in various forms, and should not be interpreted as being limited to the specific embodiments described herein. It should be understood that these embodiments are provided to make the disclosure of the present application complete and complete, and to fully convey the technical solutions of these exemplary embodiments to those skilled in the art.

[0033] Embodiment 1

[0034] The present application provides a model predictive control-based intelligent driving control system, which realizes the angle following of the steering wheel by controlling the rack position and the rack speed double closed-loop control to achieve the angle requested by the host computer in the angle following intelligent driving system, including: a rack position loop module, which outputs the expected rack speed according to the expected steering wheel angle; a rack speed loop module, which establishes a prediction model according to the expected rack speed.

[0035] As shown in the accompanying Figure 1As shown, the preferred scheme of the present application includes a safety check module and a safety limit module, and the rack speed loop module establishes a prediction model after safety check and safety limit.

[0036] Preferably, as Figure 4 shown, the rack speed loop module includes an actual rack speed calculation module for calculating actual rack speed, and a model prediction controller for establishing a prediction model after receiving speed information calculated by the actual rack speed calculation module.

[0037] The structure diagram of the model prediction controller of the rack speed loop is as Figure 3 shown, and the model prediction controller calculation model construction steps are as follows:

[0038] Step 1, obtain the expected rack speed and actual rack speed, and construct a rack speed and motor mechanical speed relationship; Step 2, calculate the motor mechanical angular speed and expected speed, establish a value function and obtain a limit value. The model prediction controller calculation model construction further includes the following steps: Step 11, after obtaining the expected rack speed and actual rack speed, predict the error.

[0039] Preferably, it further includes Step 12, after the step 1 or step 11, establish a prediction model, and then establish a value function and obtain a limit value.

[0040] The intelligent driving control system based on model prediction control of the present application will be further described below.

[0041] The present application provides an intelligent driving function based on angle control under L2 level automatic driving working condition, which can make the steering system respond to the vehicle control request faster. In double gear type power steering machine (EPSdp) and shaft parallel type power steering machine (EPSapa), the intelligent driving function based on angle control mainly realizes the angle following intelligent driving system in which the steering wheel executes the angle requested by the upper computer through double closed loop control of rack position and rack speed.

[0042] Without considering friction and mechanical stress, according to the simple mechanical structure between the motor and the rack of the EPS, as shown in the attached Figure 2 , it can be seen that the rack speed v and the rotor speed ω of the motor end of the EPS satisfy the following relationship:

[0043]

[0044] In the above formula, v is the actual rack speed, ω is the motor rotor speed of the EPS, k v is the transmission ratio from the motor end to the rack position. According to the linear relationship between the rack speed and the motor speed, the rack speed loop in the attached Figure 1 can control the rack speed by controlling the motor speed.

[0045] At the motor end, the electromagnetic torque T generated by the motor e Part of it is used to overcome the load torque T L Another part is used to overcome rotor friction resistance and rotor inertia, and its corresponding mechanical motion equation is:

[0046]

[0047] Where: d is the value for calculating ω m The first derivative with respect to time, J is the rotor moment of inertia, β is the rotor friction coefficient, ω m Let ω be the mechanical angular velocity of the motor. Its mechanical angle is proportional to the motor rotor speed ω, and the relationship is as follows:

[0048] ω=ρ n ω m (3)

[0049] Where ρ n It is an extreme logarithm.

[0050] Therefore, according to equations (1) and (3), the speed v of the rack is related to the mechanical angular velocity ω of the EPS motor end. m Relationship:

[0051]

[0052] Define T S Given the sampling period of the velocity loop, Equation (2) is discretized using the first-order Euler method at two adjacent sampling points.

[0053]

[0054] Where, ω m (k) represents the rotational speed at time k; ω m (k+1) represents the rotational speed at time k+1; in actual control, the sampling period of the speed module is very short, 5ms, so we can assume the load torque T between two adjacent sampling periods. L Similarly, according to equations (6) and (5), its mechanical angle ω within one sampling period m The change is:

[0055]

[0056] Where ΔT e (k) represents the change in electromagnetic torque at time k. Then, the predicted rotational speed ω at time k+1 is... p (k+1):

[0057] make Then the equation (8) will be simplified as:

[0058] ω p (k+1) = (1 + A)ω m (k) - Aω m (k-1) + BΔT e (k) (9)

[0059] The predicted speed ω p (k+2) is:

[0060] ω p (k+2) = (1 + A)ω m (k+1) - Aω m (k) + BΔT e (k+1) (10)

[0061] Then the predicted speed ω (k+N) is:

[0062] ω p (k+N) = (1 + A)ω m (k+N-1) - Aω m (k+N-2) + BΔT e (k+N-1) (11)

[0063] In the actual system, the expected speed ω r and the actual speed ω m have an error, which is expressed in two consecutive periods as:

[0064]

[0065] Where e represents the error between the expected speed ω r and the actual speed ω m .

[0066] In the actual system, when the system reaches steady state, the predicted speed is equal to the expected speed, and then the value function is defined as:

[0067] f(k+1) = [ω r (k+1) - ω p (k+1)] 2 + [ΔT e (k)] 2 (13)

[0068] In order to make the value function minimum, there is the extreme condition

[0069]

[0070] Then the increment of torque:

[0071]

[0072] Similarly:

[0073]

[0074] According to the above reasoning, the torque increment of N prediction periods is:

[0075]

[0076] Then the torque change amount after N periods is Then:

[0077]

[0078] Then bring the above formula (4) into formula (18), and then the torque change amount based on the rack speed v is:

[0079]

[0080] Where e v (i) = v r (i)-v(i), v r (i) is the expected rack speed at time i, and v(i) is the actual rack speed at time i.

[0081] Therefore, the torque amount after N periods is T e = ΔT e + T e (0), so

[0082]

[0083] The beneficial effects of the present application are that for the closed-loop intelligent driving control system with uncertain disturbances, the model predictive control is used in the rack speed loop instead of fixed PI control, which can not only achieve precise control, but also solve the problems of difficult PI parameter adjustment, difficult matching of various working conditions, and poor robustness.

[0084] The present application has been described in detail through specific embodiments and examples, but these do not constitute a limitation on the present application. Those skilled in the art can make many modifications and improvements without departing from the principles of the present application, and these should also be considered as within the scope of protection of the present application.

Claims

1. A model predictive control-based intelligent driving control system, characterized in that, The intelligent driving control system, based on model predictive control, achieves steering wheel angle tracking by controlling both rack position and rack speed through dual closed-loop control. The rack position ring module outputs the desired rack speed based on the desired steering wheel angle; The rack speed loop module establishes a prediction model based on the desired rack speed; The rack speed ring module includes: The actual rack speed calculation module is used to calculate the actual rack speed. The model prediction controller receives the speed information calculated by the actual rack speed calculation module and then builds a prediction model. The model prediction controller computation model construction steps are as follows: Step 1: Obtain the desired rack speed and the actual rack speed, and construct the relationship between rack speed and motor mechanical speed; Step 2: Calculate the mechanical angular velocity and desired speed of the motor, establish the value function, and obtain the limit value; The value function established in step 2 is as follows: ； in, For the desired speed, To predict rotational speed, The change in electromagnetic torque at time k For the predicted rotational speed at time k+1, Let be the expected rotational speed at time k+1.

2. The intelligent driving control system based on model predictive control according to claim 1, characterized in that, It also includes a safety verification module and a safety limitation module. The rack speed ring module then establishes a prediction model through safety verification and safety limitation.

3. The intelligent driving control system based on model predictive control according to claim 1, characterized in that, The model prediction controller computation model construction also includes the following steps: Step 11: After obtaining the desired rack speed and the actual rack speed, predict the error.

4. The intelligent driving control system based on model predictive control according to claim 1, characterized in that, The model prediction controller computation model construction also includes the following steps: Step 12: After step 1 or step 11, establish a prediction model, then establish a value function and obtain the limit value.

5. The intelligent driving control system based on model predictive control according to claim 1, characterized in that, The relationship between the rack speed and the rotational speed of the EPS motor end is expressed as follows: ； in For extreme logarithms, This represents the actual rack speed. This refers to the rotor speed of the EPS motor. The rotational speed is the mechanical angular velocity at the EPS motor end. This is the transmission ratio from the motor end to the rack position.

6. The intelligent driving control system based on model predictive control according to claim 3, characterized in that, Desired rotational speed Compared with actual speed There is an error between them, and the formula for two consecutive periods is: ， Where e(k) represents the expected rotational speed at time k. Compared with actual speed The error between k and k, where e(k+1) represents the expected rotational speed at time k+1. Compared with actual speed Errors between them.

Images