Intelligent driving control system based on model predictive control

Abstract

The invention discloses an intelligent driving control system based on model predictive control, which realizes that an angle executed by a steering wheel follows an angle requested by an upper computer in an intelligent driving system by controlling double closed-loop control of a rack position and a rack speed, and comprises a rack position loop module, a model predictive control module and a model predictive control module, outputting a desired rack speed according to the desired steering wheel angle; a rack speed loop module establishes a predictive model based on the desired rack speed. For a closed-loop intelligent driving control system with uncertain disturbance, model prediction control is adopted for a rack speed ring of the closed-loop intelligent driving control system, compared with fixed PI control, accurate control can be achieved, and the problems that in the prior art, PI parameters are difficult to debug, various working conditions are difficult to match, and robustness is poor can be solved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent assisted driving control, and in particular to an intelligent driving control system based on model predictive control. Background Art

[0002] As people pay more and more attention to autonomous driving, intelligent driving functions are widely used to assist drivers and improve driving comfort and safety. Vehicle lateral control is the key to realizing assisted 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 direct torque control is an open-loop control compared to EPS lateral control, although it responds quickly, the closed-loop control based on angle control has better stability than direct torque control.

[0003] Lateral control based on angle control is a closed-loop control, which generally adopts PI control. Although the PI control structure is simple and easy to implement, its PI parameters must strictly rely on the adjustment of engineering experience. Moreover, in actual working conditions, the current assisted driving allows the driver and intelligent driving functions to work at the same time, which will bring uncertain disturbances to the intelligent driving control. At this time, a set of fixed PI parameters will be difficult to meet the performance requirements under different working conditions, and the robustness is poor. Summary of the invention

[0004] The present invention provides an intelligent driving control method based on model predictive control. For a closed-loop intelligent driving control system with uncertain disturbances, the model predictive control is used to solve the problem of difficult parameter debugging and poor robustness compared to fixed PI control.

[0005] In order to solve the above technical problems, the present invention provides an intelligent driving control system based on model predictive control, which realizes the angle of the steering wheel following the angle requested by the host computer in the intelligent driving system by controlling the rack position and rack speed dual closed-loop control. The intelligent driving control system based on model predictive control includes:

[0006] The rack position loop module outputs the desired rack speed based on the desired steering wheel angle;

[0007] The rack speed loop module establishes a prediction model according to the desired rack speed.

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

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

[0010] An actual rack speed calculation module is used to calculate the actual rack speed;

[0011] The model prediction controller establishes a prediction model after receiving the speed information calculated by the actual rack speed calculation module.

[0012] Preferably, the model prediction controller calculation model construction steps are as follows:

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

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

[0015] Preferably, the model predictive controller calculation model construction also includes the following steps:

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

[0017] Preferably, the model predictive controller calculation model construction also includes the following steps:

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

[0019] Preferably, the relationship between the rack speed and the mechanical angular velocity of the EPS 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 EPS, ω m is the mechanical angular velocity of the EPS motor, k v is the transmission ratio from the motor end to the rack position.

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

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

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

[0025] Preferably, the expected speed ω r With the actual speed ω m There is an error between the two consecutive cycles. The formula is:

[0026] BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments:

[0028] Figure 1 It is a schematic diagram of the overall system architecture of a preferred embodiment of the present invention.

[0029] Figure 2 It is a simplified mechanical structure diagram between the motor and the rack of the EPS of the present invention.

[0030] Figure 3 It is a structural diagram of the model predictive controller of the rack speed loop of the present invention.

[0031] Figure 4 Schematic diagram of system control of the present invention. DETAILED DESCRIPTION

[0032] The following describes the implementation methods of the present invention through specific specific embodiments, and those skilled in the art can fully understand other advantages and technical effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through different specific implementation methods, and the details in this specification can also be applied based on different viewpoints, and various modifications or changes can be made without deviating from the overall design concept of the invention. It should be noted that the following embodiments and the features in the embodiments can be combined with each other in the absence of conflict. The following exemplary embodiments of the present invention can be implemented in a variety of different 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 invention thorough and complete, and to fully convey the technical solutions of these exemplary specific embodiments to those skilled in the art.

[0033] Example 1

[0034] The present invention provides an intelligent driving control system based on model predictive control, which realizes that the angle of steering wheel execution follows the angle requested by the upper computer in the intelligent driving system by controlling the rack position and rack speed dual closed-loop control, including: a rack position loop module, which outputs the expected rack speed according to the expected steering wheel angle; and a rack speed loop module, which establishes a prediction model according to the expected rack speed.

[0035] As attached Figure 1As shown, the preferred solution of the present invention includes a safety verification module and a safety limitation module, and the rack speed loop module establishes a prediction model after safety verification and safety limitation.

[0036] Preferably, if Figure 4 As shown, the rack speed loop module includes: an actual rack speed calculation module, which is used to calculate the actual rack speed; and a model prediction controller, which establishes a prediction model after receiving the speed information calculated by the actual rack speed calculation module.

[0037] Then the structure diagram of the model predictive controller of the rack speed loop is as follows Figure 3 As shown, the steps for constructing the calculation model of the model predictive controller are as follows:

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

[0039] Preferably, the method further comprises step 12, wherein after step 1 or step 11, a prediction model is established, a value function is established, and a limit value is obtained.

[0040] The intelligent driving control system based on model predictive control of the present invention is further explained below.

[0041] The present invention provides an intelligent driving function based on angle control under L2 level automatic driving conditions, which can enable the steering system to respond to the control request of the whole vehicle more quickly. In a dual-pinion power steering machine (EPSdp) and an axle-parallel power steering machine (EPSapa), the intelligent driving function based on angle control mainly realizes the angle of the steering wheel following the angle requested by the host computer in the intelligent driving system by controlling the rack position and rack speed dual closed-loop control.

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

[0043]

[0044] In the above formula, v is the actual rack speed, ω is the motor rotor speed of EPS, and 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 following Figure 1 The rack speed loop in the control can achieve the purpose of controlling 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 , and another part is used to overcome the rotor friction resistance and rotor inertia, and the corresponding mechanical motion equation is:

[0046]

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

[0048] ω=ρ n ω m (3)

[0049] where ρ n is the pole pair number.

[0050] Then according to equations (1) and (3), the rack speed v and the mechanical angular velocity ω of the EPS motor end are: m The relationship is:

[0051]

[0052] Define T S is the sampling period of the velocity loop, and the first-order Euler method is used to discretize equation (2) at two adjacent sampling points.

[0053]

[0054] Among them, ω m (k) is the speed at time k; ω m (k+1) is the speed at time k+1; in actual control, the sampling period of the speed module is very short, 5ms, so it can be assumed that the load torque T between two adjacent sampling periods is L The same, then according to equations (6) and (5), the mechanical angle ω in one sampling period is m The change in is:

[0055]

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

[0057] make Then the above formula (8) can be simplified to:

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

[0059] Predicted speed ω at time k+2 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 at time 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 With the actual speed ω m There is an error between the two consecutive cycles, which can be expressed as:

[0064]

[0065] Where e represents the desired speed ω r With the actual speed ω m The error between.

[0066] In the actual system, when the system reaches a steady state, the predicted speed is equal to the expected speed, so its 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 minimize the value function, there is an extreme value condition have to:

[0069]

[0070] Then the torque increment is:

[0071]

[0072] Similarly, we can get:

[0073]

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

[0075]

[0076] Then the torque change after N cycles but:

[0077]

[0078] Then, by substituting the above formula (4) into formula (18), the torque change 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] So the torque after N cycles is T e =ΔT e +T e (0), then

[0082]

[0083] The beneficial effects of the present invention are as follows: for a closed-loop intelligent driving control system with uncertain disturbances, its rack speed loop adopts model predictive control instead of fixed PI control, which can not only achieve precise control, but also solve the current problems of difficult debugging of PI parameters, difficulty in matching various working conditions, and poor robustness.

[0084] The present invention has been described in detail above through specific implementation modes and embodiments, but these do not constitute limitations of the present invention. Without departing from the principles of the present invention, those skilled in the art may also make many variations and improvements, which should also be regarded as the protection scope of the present invention.

Claims

1. An intelligent driving control system based on model predictive control, characterized in that: The angle of the steering wheel follows the angle requested by the host computer in the intelligent driving system by controlling the rack position and rack speed in a dual closed-loop control. The intelligent driving control system based on model predictive control includes: The rack position loop module outputs the desired rack speed based on the desired steering wheel angle; The rack speed loop module establishes a prediction model according to the desired rack speed.

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 loop module 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 rack speed loop module comprises: An actual rack speed calculation module is used to calculate the actual rack speed; The model prediction controller establishes a prediction model after receiving the speed information calculated by the actual rack speed calculation module.

4. The intelligent driving control system based on model predictive control according to claim 3 is characterized in that: The steps for constructing the model prediction controller calculation model are as follows: Step 1, obtain the expected rack speed and the actual rack speed, and construct a relationship between the rack speed and the motor mechanical speed; Step 2, calculate the mechanical angular velocity and expected speed of the motor, establish the value function and find the limit value.

5. The intelligent driving control system based on model predictive control according to claim 4 is characterized in that: The model prediction controller calculation model construction also includes the following steps: Step 11, after obtaining the desired rack speed and the actual rack speed, predict the error.

6. The intelligent driving control system based on model predictive control according to claim 4, characterized in that: The model prediction controller calculation 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 a limit value.

7. The intelligent driving control system based on model predictive control according to claim 4, characterized in that: The relationship between the rack speed and the mechanical angular velocity of the EPS motor end is: where ρ n is the number of pole pairs, v is the actual rack speed, ω is the motor rotor speed of EPS, ω m is the mechanical angular velocity of the EPS motor, k v is the transmission ratio from the motor end to the rack position.

8. The intelligent driving control system based on model predictive control according to claim 4, characterized in that: The value function established in step 2 is as follows: f(k+1)=[ω r (k+1)-ω p (k+1)] 2 +[ΔT e (k)] 2 ; Among them, ω r is the desired speed, ω p is the predicted speed, ΔT e (k) is the change of electromagnetic torque at time k, ω p (k+1) is the predicted speed at time k+1, ω r (k+1) is the expected rotation speed at time k+1.

9. The intelligent driving control system based on model predictive control according to claim 5, characterized in that: Expected speed ω r With the actual speed ω m There is an error between the two consecutive cycles. The formula is:

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