CN112477880B

CN112477880B - Longitudinal control method for unmanned automobile

Info

Publication number: CN112477880B
Application number: CN202011373882.8A
Authority: CN
Inventors: 王一晶; 曹世卓; 左志强
Original assignee: Tianjin University
Current assignee: Tianjin University
Priority date: 2020-11-30
Filing date: 2020-11-30
Publication date: 2022-05-20
Anticipated expiration: 2040-11-30
Also published as: CN112477880A

Abstract

The invention provides a longitudinal control method of an unmanned automobile, which comprises the following steps: operation S1: establishing a discrete kinematics model of a longitudinal control system of the vehicle, and taking the state expected acceleration as an input control quantity of the discrete kinematics model of the longitudinal control system; operation S2: creating a linear TD, arranging the transition process of the reference speed signal and the reference acceleration signal through the linear TD, and converting the stepped reference speed signal and the stepped reference acceleration signal into a continuous reference signal sequence; operation S3: and establishing an MPC (MPC), connecting with the linear TD, obtaining an optimal control sequence through the discrete kinematics model based on a reference sequence number sequence input by the linear TD under the condition of existence of control quantity constraint and control quantity increment constraint, completing the longitudinal control of the unmanned automobile, and improving the advantages of short adjustment time and small steady-state error due to the fact that the traditional method cannot process the limitation of physical factors.

Description

Longitudinal control method for unmanned automobile

Technical Field

The invention belongs to the field of unmanned control, and particularly relates to an unmanned vehicle longitudinal control method combining a linear tracking differentiator and a model predictive control algorithm.

Background

In recent years, electric vehicles are widely used in various scenes such as unmanned vehicles and smart cities. Compared with the traditional internal combustion engine automobile, the electric automobile has the advantages of high efficiency, energy conservation, environmental protection and the like. In an ideal situation, the electric automobile can gradually replace the traditional automobile, and is beneficial to realizing zero emission of vehicle tail gas. Environmental perception, motion planning and motion control are three major core technologies in the unmanned technology. Among them, motion control is important for the unmanned technology due to the influence of factors such as complex road conditions, various driving environments, physical constraints of actuators and the like. At present, motion control can be divided into lateral control and longitudinal control, the goal of the lateral control is to realize trajectory tracking, and the goal of the longitudinal control is to realize speed tracking. In the control process, the transverse control effect is often influenced by the longitudinal control effect, the longitudinal control is not limited by the transverse control factor, and the movement condition of a transverse vehicle system is not considered in many longitudinal control research works.

For longitudinal control, under the requirements of various indexes such as energy optimization, safety and riding comfort, economic speed control and optimal speed control are two key research contents. However, generally, it is difficult to stably secure the vertical control performance because the reference signal is discontinuous in most cases. Especially in the absence of a reasonable speed planning function, the acceleration of the reference signal needs to meet the physical characteristics of the electric vehicle, but in many studies, no consideration is given, resulting in an inappropriate reference signal. In addition, optimal speed control becomes difficult due to control input constraints imposed by actuator physical constraints as well as input delta constraints. The control input constraints are mainly directed to physical limitations and artificially set limitations imposed on the execution of links in longitudinal control. And to avoid actuator damage due to frequent and large changes in control inputs, it is common to deal with this by adding an input delta constraint. However, conventional control algorithms based on PID controllers or LQR do not address both constraints. Therefore, in the longitudinal control of the electric vehicle, it is important to consider the control amount constraint and the control amount increment constraint and study how to obtain a reasonably reliable reference signal.

A Model Predictive Control (MPC) algorithm is one of representative algorithms that can actively handle multiple constraints, and has been widely used in an unmanned planning and Control module. In most longitudinal control schemes in particular, MPC is often used to achieve artificially specified performance such as low fuel consumption, ride comfort, smooth trajectory, etc., in addition to addressing basic physical constraints. The MPC predicts the error state in a future period of time domain according to the mathematical model of the controlled object at the current moment, then constructs an objective function according to the specified performance requirement, finally solves the control quantity sequence which enables the objective function value to be minimum through an optimization algorithm, and applies the first component of the control sequence to the actual controlled system. Although recent research on MPC has had a great deal of experimental validation, there is no demonstration in these works of an analysis of the iterative feasibility and stability of MPC, leading to a lack of theoretical support for these results. In addition, in actual control, an unreliable reference signal may cause the MPC to lose a feasible solution at an initial time under the condition that terminal constraints are conservative, and the situation cannot be solved by setting MPC parameters. Tracking Differentiators (TDs) are often used to process unreasonable reference signals and arrange more reasonable transitions to improve Tracking. The linear TD is mainly based on a first-order inertia link to arrange a transition process without overshoot for a step reference signal, and can also obtain a reasonable differential signal of the reference signal.

Disclosure of Invention

Technical problem to be solved

In view of the above, the present invention provides a longitudinal controller for an unmanned vehicle, which combines a linear tracking differentiator and a model predictive control algorithm, in order to solve at least one of the above-mentioned technical problems.

(II) technical scheme

The present disclosure provides a longitudinal control method for an unmanned vehicle, comprising:

operation S1: establishing a discrete kinematics model of a longitudinal control system of the vehicle, and taking the state expected acceleration as an input control quantity of the discrete kinematics model of the longitudinal control system;

operation S2: creating a linear TD, arranging the transition process of the reference speed signal and the reference acceleration signal through the linear TD, and converting the stepped reference speed signal and the stepped reference acceleration signal into a continuous reference signal sequence; and

operation S3: and creating an MPC (MPC), connecting with the linear TD, and obtaining an optimal control sequence through the discrete kinematics model based on a reference sequence number sequence input by the linear TD under the condition that control quantity constraint and control quantity increment constraint exist, so as to complete the longitudinal control of the unmanned automobile.

In an embodiment of the present disclosure, the discrete kinematic model is:

wherein x (k) is a state variable, x (k) ═ v (k), a (k)]^TU (k) is a control input amount, and u (k) is a_des(k) The specific form is as follows:

wherein T is a control period.

In an embodiment of the present disclosure, the linear TD form is:

x_r(k+1)＝A_rx_r(k)+B_rv₀(k) (3)

wherein x is_r(k)＝[v_r(k) a_r(k)]^T，v₀(k) Is an original reference signal, belonging to the step signal, v_r(k) Reference velocity signal output for linear TD, a_r(k) A reference acceleration signal output for linear TD, and_rand B_rThe specific form is as follows:

wherein when the parameter s satisfies

Tracking error e of time, linear TD_td＝[v_r(k)-v₀(k) a_r(k)]^TConverge to [ 00 ]]^T。

In an embodiment of the present disclosure, the MPC is:

where m represents the prediction time domain length, U^*(k) In order to obtain an optimal control sequence by an optimization solution algorithm at the moment k, Ke (k) is a terminal linear feedback controller expression, and k (e (k)) represents the stability of a closed-loop system.

In the disclosed embodiment, in operation S3:

based on the appointed step speed signal, a reasonable reference acceleration signal and a reference speed signal are obtained through the linear TD, the MPC carries out an online solving process of an optimization problem according to the output of the linear TD to obtain the optimal control quantity at each moment, the optimal control quantity acts on an actual system to enable a longitudinal system of the vehicle to generate corresponding actions, the process is continuously repeated, and the vehicle can continuously track the appointed step speed signal.

In the embodiment of the disclosure, MPC and TD are combined, and the transition process of the unreasonable reference signal such as a step signal is arranged by the TD, so as to ensure the smooth proceeding of the MPC optimization solving process.

In the embodiment of the disclosure, the parameter s of the TD is determined through a theoretical analysis process, and the convergence of the TD can be ensured within the range that s is more than or equal to 0 and less than or equal to 1/T.

In the embodiment of the disclosure, the selection method and rule of the parameters Q, R and P of the MPC are determined through a theoretical analysis process, so that the iterative feasibility and the stability of the closed-loop system are ensured.

(III) advantageous effects

According to the technical scheme, the unmanned electric vehicle model prediction longitudinal controller with the tracking differentiator has at least one or part of the following beneficial effects:

(1) the scheme that the traditional method can only solve the problem by additionally increasing the saturation condition due to the fact that the traditional method can not process the control quantity increment constraint and the control quantity constraint caused by physical factor limitation and artificial specified limitation is improved;

(2) the problem of calculation of feasible solutions of the vehicle longitudinal control initial moment which are influenced by the reference signals under the condition that terminal state constraints exist in an original MPC control scheme is solved;

(3) the stability and the iteration feasibility of a basic dual-mode MPC-based system are guaranteed, the control quantity and the control quantity increment limitation are actively converted into the constraint condition of an optimization problem, and the optimal control quantity is solved by designing an objective function to carry out optimization solution;

(4) the step speed signal can be stably tracked, and compared with the control of a PID controller, the method has the advantages of short regulation time and small steady-state error.

Drawings

Fig. 1 is a schematic frame diagram of a longitudinal control system of an experimental platform for a longitudinal control method of an unmanned vehicle according to an embodiment of the present disclosure.

Fig. 2 is a schematic bottom-layer system diagram of an experimental platform for a longitudinal control method of an unmanned vehicle according to an embodiment of the present disclosure.

Fig. 3 is a schematic control structure diagram of a longitudinal control system for a longitudinal control method of an unmanned vehicle according to an embodiment of the present disclosure.

Fig. 4 is an appearance schematic diagram of an experimental platform for a longitudinal control method of an unmanned vehicle according to an embodiment of the present disclosure.

Fig. 5 is a schematic diagram of internal equipment of an experiment platform for a longitudinal control method of an unmanned vehicle according to an embodiment of the disclosure.

FIG. 6 is an action curve of the accelerator opening degree under the condition of tracking 3.5m/s for the longitudinal control method of the unmanned vehicle according to the embodiment of the disclosure.

FIG. 7 is a velocity tracking curve for a 3.5m/s tracking condition for the method for longitudinal control of an unmanned vehicle according to an embodiment of the present disclosure.

FIG. 8 is an action curve of the throttle opening under the control of an MPC for transitioning from tracking 3.5m/s to 5.5m/s for the longitudinal control method of the unmanned vehicle according to the embodiment of the disclosure.

FIG. 9 is an action curve of the throttle opening under PID control of transitioning from tracking 3.5m/s to 5.5m/s for the longitudinal control method of the unmanned vehicle according to the embodiment of the disclosure.

FIG. 10 shows the tracking result of the vehicle speed transitioning from tracking 3.5m/s to 5.5m/s for the longitudinal control method of the unmanned vehicle according to the embodiment of the disclosure.

FIG. 11 is a speed tracking comparison result after aligning time coordinates when the method for controlling the longitudinal direction of the unmanned vehicle according to the embodiment of the present disclosure tracks 5.5 m/s.

Detailed Description

The invention provides a longitudinal control method of an unmanned vehicle, which combines MPC and linear TD and considers the influence of control quantity and control quantity increment constraint. Meanwhile, the parameter range of the design process of each part of the scheme is determined based on the complete theoretical analysis process, so that the basic requirements of each part, including the convergence of linear TD, the iterative feasibility and the stability of MPC, are ensured.

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.

The longitudinal control (closed loop) system including the sensors, actuators, and controllers is shown in fig. 1, and the work flow thereof will be described in detail after the design. Aiming at such a longitudinal closed-loop system, the design of an upper-layer controller is mainly needed in the work, in order to process unreliable reference signals similar to step reference signals, control quantity constraints and control quantity increment constraints, the invention combines MPC and TD, converts physical limits and artificial setting limits borne by the control quantity and the control quantity increment into constraint conditions in MPC optimization solution, and designs a basic performance index function, thereby obtaining the optimal control quantity through optimization solution. Therefore, the invention adopts an unmanned electric vehicle model prediction longitudinal controller with a tracking differentiator, and the specific steps comprise:

first, consider the underlying controller and vehicle as an integral-the underlying system, which is modeled:

where v (t) represents the velocity of the host vehicle, a (t) represents the acceleration of the host vehicle, a_des(t) represents a desired acceleration of the vehicle as input control amounts of the system, τ and K_mRepresenting the resulting vehicle longitudinal system model parameters based on a data modeling approach.

Operation S1: establishing a discrete kinematic model of a vehicle longitudinal control system,

wherein x (k) is a state variable, x (k) ═ v (k), a (k)]^TU (k) is an input control amount, and u (k) is a_des(k)，a_des(k) Acceleration is expected for the state. A is a system matrix, B is a control matrix, C is an output matrix, and the specific form is as follows:

wherein T is a control period.

In order to facilitate subsequent controller design and theoretical analysis of a closed-loop system, an error model is established for the system as follows:

wherein e (k) ═ v (k) — v_r(k)，a(k)-a_r(k)]^T，u_e(k)＝u(k)-u_r(k)，y_e(k)＝y(k)-y_r(k)。v_r(k) Reference velocity signal output for linear TD, a_r(k) Reference acceleration signal, y, output for linear TD_r(k) To output a reference signal u_r(k) Is a reference control signal. In addition, in consideration of the limitation of the control amount, it is converted into a constraint u on the error control amount in combination with the reference control signal_min≤u_eu_maxAnd Δ u_min≤Δu_e≤Δu_max，u_maxAnd u_minIs the upper and lower bound on the error control quantity, Deltau_maxAnd Δ u_minIs the upper and lower bound on the error control quantity increment.

Operation S3: designing linear TD to arrange reference signal transition process

From the basic form of linear TD, the TD form that is designed for practical use is as follows:

x_r(k+1)＝A_rx_r(k)+B_rv₀(k) (4)

wherein x is_r(k)＝[v_r(k) a_r(k)]^T，v₀(k) Is an original reference signal, typically a step signal, and, in addition, A_rAnd B_rThe specific form is as follows:

and determining the condition which needs to be met by the parameter s through the condition that the linear TD meets the convergence. First, according to the basic formal formula (4) of the linear TD, the expression is as follows:

x_r(k+1)＝A_rx_r(k)+B_rv₀(k)

wherein x is_r(k)＝[v_r(k) a_r(k)]^T. The following conversion relationship can be obtained:

further can obtain

Wherein I is an identity matrix. Derived from the formula (4)

Substituting the formula (5) into the above formula yields the following derivation

Tracking error e of linear TD_td(k) Is composed of

From the above formula, when s is 0. ltoreq. s.ltoreq.1/T, A_rHas a spectral radius of [0, 1 ]]Then can guarantee

Further can obtain e_td(k) Can converge to [0, 0]Corresponding to v_r(k) Converge on v₀，a_r(k) Converging to 0.

In summary, linear TD can guarantee convergence when the parameter s satisfies 0 ≦ s ≦ 1/T.

Operation S3: model Predictive Controller (MPC) creation

Before designing the concrete form of the model predictive controller, two guiding support subsequent theoretical analysis processes are given firstly.

Introduction 1: for a controllable linear system with a control quantity constraint, a feedback control law u must exist_e(k) Ke (k) makes the closed-loop system e (k +1) ═ a + BK) e (k) asymptotically stable at e (k) e Ω, where Ω represents the invariant set and also corresponds to the terminal set in the MPC control strategy.

2, introduction: the following Lyapunov equation of the closed-loop discrete system can ensure that only one positive definite symmetric matrix P exists.

P＝(A+BK)^TP(A+BK)+(C^TQC+K^TRK)

It can further be seen that there must be a neighborhood corresponding to a normal number α as follows

Ω＝{e(k)|e^T(k)Pe(k)≤α}

So that the constraint u_min≤u_e(k)≤u_maxAnd (4) meeting the requirement. Then, it can be known that a state trajectory starting within the set Ω will not exceed the set Ω in the future and will converge to the origin.

In order to accurately track a reference speed signal and a reference acceleration signal provided by a linear TD and meet the control quantity and control quantity increment constraint, iteration feasibility and stability, the following optimal problems are constructed:

more specifically, there is the following cost function:

where i is 1, 2, 3 …, m-1, and m represents the prediction temporal length. y is_e(k + i | k) represents the output error state at time k + i predicted from the prediction model at time k, u_eThe (k + i | k) represents an error control amount to be applied at the k + i th time calculated from the optimization solution at the k th time, and e (k + m | k) represents the predicted terminal state. u. of_min、u_max、Δu_min、Δu_maxRepresenting the upper and lower limits of the error control quantity and the upper and lower limits of the error control quantity increment, respectively. Ω is the terminal state set. In addition, define

P, Q, R is a matrix of weight coefficients for the terms. And performing online optimization solution according to the MPC control strategy to obtain the optimal control quantity at each moment, wherein the final system controller form can be expressed as the following form according to a dual-mode MPC control principle:

wherein, U^*(k) The optimal control sequence is obtained by an optimization solving algorithm at the moment k.

To determine the form of the cost function, whether the optimization problem meets the iterative feasibility, and whether the closed-loop system obtained from κ (e (k)) meets the stability requirement, theoretical analysis is performed on this part of the design below.

First, a theoretical analysis was performed on the iterative feasibility of the MPC control strategy. Suppose there is an optimal solution to the optimization problem at time k, which is recorded as

Wherein i is 0, 1, …, m-1. The corresponding optimum state is recorded as e^*(k + i +1| k). Wherein the optimum control amount

And e^*(k + i +1| k) satisfies the constraint (6). Now a group of feasible candidate control sequences at the moment k +1 and corresponding prediction state sequences can be obtained from the optimal solution at the moment k

K is the control gain of the terminal linear feedback controller, and once the system state enters the terminal domain omega, the system controller is switched to the terminal linear feedback controller u_e(k) Ke (k), and the following Lyapunov equation holds

Order to

And will be

Substituting into the closed-loop system equation (3) can obtain the predicted state according to the set of control sequences

According to e (k + i +1| k) ═ Ae (k + i | k) + Bu_e(k + ilk) it is known that,

the first to fourth constraints in the system state constraint equation (6) are satisfied at the time k + 1. According to the introduction 2 and

dual mode MPC controller type (8) switch to

So that the control quantity u_e(k + m | k +1) satisfies u_min≤u_e(k+m|k+1)≤u_maxI.e. the fifth constraint in constraint (6). Since at time k the final predicted state has entered the terminal set, i.e.: e (k + m | k) ∈ Ω, then the constraint on the control quantity increment at the k + m +1 th time need not be considered in the prediction process. Prediction at time k +1, the prediction state at the last prediction time may be based on

To obtain

Is to satisfy the seventh constraint in constraint formula (6). In view of the above, it is desirable to provide,

is a set of feasible solutions derived from the optimal solution at time k + 1. By recursive iteration it can be obtained that at any time in the future constraint (6) is feasible for all optimization problems involved.

In addition to the analysis of the iterative feasibility of MPC, the stability of the system needs to be analyzed. In the discussion of feasibility, it can be seen that there exists a set of feasible control sequences at time k +1

Then the optimal control sequence obtained by solving the optimization problem at time k is recorded as

Then, corresponding to the [ k +1, k + m ] interval, the corresponding predicted state sequence and output state sequence of the system are recorded as

{e^*(k+1|k)，e^*(k+2|k)，…，e^*(k+m|k)} (10)

At this time, the optimal cost function can be expressed as

Wherein, y_e(k|k)＝y_e(k) In that respect Substituting dual-mode MPC controller equation (8) into closed-loop system equation (3) can derive the predicted state at time k +1

e(k+1)＝e^*(k+1|k)

At the moment of k +1, the optimization problem is updated, and a candidate control sequence can be obtained as

Wherein the first m-1 control quantities are the optimal solution at the moment k. Due to e^*(k + m | k) ∈ Ω, and the control input obtained by the feedback controller according to the terminal state is u_e(k+m|k+1)＝Ke^*(k + m | k), the set of control quantities is known to satisfy the system constraints (6b-6d) from the above analysis on iterative feasibility. In the prediction process of the time k + m, the time can be obtained

By lemma 2 and e^*(k + m | k) ∈ Ω, and can be obtained

(A+BK)e^*(k+m|k)∈Ω

Further bringing the control amount formula (13) into the formula (12)

Wherein the content of the first and second substances,

further derivation can be obtained by Lyapunov equation (9)

Then at time k +1, it can be deduced from the cost function

Due to the fact that

And

the cost function can be derived

As can be seen from the iterative feasibility analysis of the MPC control scheme, the candidate control sequence formula (13) is a set of feasible solutions about the control quantity at the moment k +1, and then the solution can be obtained

When the error states of the closed-loop system formula (3) are all 0, i.e., (k) is [0, 0 ]]^TThe optimal solution is U ^*0, represents the vehicle actual control quantity u (k) and the reference control input quantity u_r(k) Equal, further indicating that the optimal value of the cost function is 0. From equation (14), it can be seen that the optimal value of the cost function of the closed-loop system is decreased under the action of the MPC control strategy, and when e (k) is [0, 0 ]]^TThen the minimum value is reached. Due to the optimal cost function J^*(k) Is continuous at time k, therefore J^*(k) Lyapunov's equation, which can be a closed-loop system equation (3), monotonically decreases when k > 0. When k → ∞, the value of the cost function tends to 0, and e (k) is [0, 0 ∞ ]]^TThen y (k) → y_r(k)。

Based on the above analysis, it can be determined when

And

when the MPC control scheme is a diagonal positive timing matrix, the iterative feasibility and the stability of the system can be ensured.

Operation S3: and establishing an MPC (MPC), connecting the MPC with the linear TD, obtaining a reasonable reference acceleration signal and a reference speed signal through the linear TD according to the specified step speed signal, carrying out an online solving process of an optimization problem by the MPC according to the output of the linear TD to obtain an optimal control quantity at each moment, acting on an actual system, enabling a longitudinal system of the vehicle to generate corresponding actions, continuously repeating the process, and realizing the continuous tracking of the specified step signal by the vehicle.

Based on the control strategy designed above, a control structure as shown in fig. 1 was constructed. The whole control scheme is implemented by firstly deploying the control strategy on an on-board computing platform TX2 in the form of an ROS node as an upper-layer controller. The slave controller of fig. 1 is also deployed in the same manner as in TX2, except for this. The slave controller is responsible for tracking the expected acceleration which is the control quantity calculated by the upper controller, and obtaining the actual control quantity throttle opening and the brake opening of the vehicle. That is, the relationship between the upper controller and the lower system including the slave controller, the embedded controller, the motor controller, the brake controller, and the vehicle can be simply shown as fig. 2, in which the upper controller inputs a desired acceleration to the lower system, and the lower system generates an actual throttle opening and a brake opening based on the desired acceleration to make the vehicle generate a corresponding desired motion. In the upper level controller, a tracking differentiator and a dual-mode MPC controller are deployed in keeping with the above control strategy scenario, as shown in fig. 3. In the actual control process, firstly, based on the GPS & IMU in the experimental platform shown in fig. 4 and 5 and the pulse encoder on the side of the vehicle tire, the vehicle speed information and the wheel rotation speed information are respectively transmitted into TX2 through RS-485 and CAN bus signals, the sensing module of TX2 fuses the two kinds of information, and finally, accurate vehicle speed information is obtained and transmitted to the upper controller (node), the upper controller calculates the upper control quantity (expected acceleration) based on the proposed control strategy and transmits the upper control quantity to the slave controller (node), the slave controller generates reasonable throttle opening quantity and brake opening quantity based on the upper control quantity and transmits the upper control quantity to the embedded controller in the form of CAN signals, the embedded controller converts the upper control quantity into corresponding PWM voltages according to the CAN signals, thereby completing the control of the brake controller and the motor controller (as shown in fig. 1), the embedded controller respectively inputs appointed PWM level signals to the motor controller and the brake controller according to the appointed throttle opening degree and the brake opening degree, the motor controller carries out motor speed regulation control according to the PWM level signals to realize acceleration and deceleration, and the brake controller controls the brake motor to rotate according to the PWM level signals, so that a brake pedal generates mechanical action and meets the braking requirement. In each control period, the upper-layer controller updates the upper-layer control quantity based on the deployed control strategy according to the real-time pose information of the vehicle and sends the updated upper-layer control quantity to the bottom-layer system, and therefore stable adjustment of the vehicle speed is achieved.

The results of the drive test of the control scheme on the unmanned mobile platform of the university of the inventor are given below, as shown in fig. 4 and 5. The unmanned mobile platform is provided with vehicle-mounted sensors such as a laser radar, a camera and a receiver, and an accelerator and a brake in the platform are modified, so that the unmanned mobile platform has a basic drive-by-wire function. The mobile platform takes TX2 as a controller calculation unit, and has the advantages of light weight, low power, strong calculation force and the like. In fig. 5, the vehicle-mounted actuator corresponds to an accelerator pedal and a brake pedal. After the upper-layer controller and the slave controller are successfully deployed, the proposed control scheme is actually tested, wherein fig. 6 shows the change of the accelerator opening of the vehicle at the step speed of 3.5m/s, and fig. 7 shows the vehicle speed tracking result when the vehicle tracks the step speed of 3.5 m/s.

Further, the adjusting time of the adjusting process is 15.8s, the steady-state error is 0.0456m/s, and the standard deviation is 0.0606m/s measured from the aspect of data indexes, so that the feasibility of the control scheme of the dual-mode MPC with the TD is verified. The second working condition is that the vehicle firstly tracks the step signal of 3.5m/s, and the vehicle reference speed is increased to 5m/s at the end of the track to continue tracking. Fig. 8 and 9 are control quantity inputs of the dual-mode MPC control scheme with TD and the PID control scheme, respectively, under the condition, and fig. 10 is a comparison of specific tracking results. It can be seen from the figure that the dual mode MPC control scheme with TD tracks better than the PID control scheme. FIG. 11 is a graph of the tracking effect of two controllers on a 5.5m/s velocity signal after aligning the time coordinates. In addition, the tracking results of the two controllers in the tracking stage of 3.5m/s are shown in table 1, and the tracking results of the two controllers in the tracking stage of 5.5m/s are shown in table 2.

TABLE 1 tracking results in case of tracking at 3.5m/s

TABLE 2 tracking results in case of tracking 5.5m/s

It should be understood that the above-mentioned are only typical of the present invention, and should not be considered as limiting the present invention, and any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

So far, the embodiments of the present invention have been described in detail with reference to the accompanying drawings. It is to be noted that, in the attached drawings or in the description, the implementation modes not shown or described are all the modes known by the ordinary skilled person in the field of technology, and are not described in detail. Further, the above definitions of the various elements and methods are not limited to the various specific structures, shapes or arrangements of parts mentioned in the examples, which may be easily modified or substituted by those of ordinary skill in the art.

From the above description, those skilled in the art should clearly recognize that the present invention relates to a method for controlling a longitudinal direction of an unmanned vehicle.

In summary, the invention provides a longitudinal control method for an unmanned vehicle, and compared with the prior art, the traditional method cannot process the control quantity increment constraint and the control quantity constraint caused by physical factor limitation and artificial specified limitation, and can only solve the problem by additionally adding a saturation condition. In addition, the unreasonable reference signal influences the calculation of a feasible solution at the initial moment of the longitudinal control of the vehicle under the condition that the terminal state constraint exists, and the problem cannot be solved by the original MPC control scheme. In addition, in recent years some research work on unmanned MPC control schemes, the theoretical analysis of MPCs has not been complete. The algorithm provided by the invention ensures the stability and the iteration feasibility of the system based on the basic dual-mode MPC, actively converts the control quantity and the control quantity increment limitation into the constraint condition of the optimization problem, and carries out optimization solution through designing the objective function to obtain the optimal control quantity. In the design process, each part considers the feasibility of theoretical problem solving and the convergence of the system and provides theoretical analysis and proof.

Finally, the scheme is verified on the modified unmanned park electric sightseeing vehicle, and the verification result shows that the control scheme can stably track the step speed signal and obviously improve the control effect in the aspects of adjusting time and steady-state error compared with a PID (proportion integration differentiation) controller.

It should also be noted that directional terms, such as "upper", "lower", "front", "rear", "left", "right", etc., used in the embodiments are only directions referring to the drawings, and are not intended to limit the scope of the present invention. Throughout the drawings, like elements are represented by like or similar reference numerals. Conventional structures or constructions will be omitted when they may obscure the understanding of the present invention.

And the shapes and sizes of the respective components in the drawings do not reflect actual sizes and proportions, but merely illustrate contents of the embodiments of the present invention. Furthermore, in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim.

Unless otherwise indicated, the numerical parameters set forth in the specification and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by the present invention. In particular, all numbers expressing quantities of ingredients, reaction conditions, and so forth used in the specification and claims are to be understood as being modified in all instances by the term "about". Generally, the expression is meant to encompass variations of ± 10% in some embodiments, 5% in some embodiments, 1% in some embodiments, 0.5% in some embodiments by the specified amount.

Furthermore, the word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements.

The use of ordinal numbers such as "first," "second," "third," etc., in the specification and claims to modify a corresponding element does not by itself connote any ordinal number of the element or any ordering of one element from another or the order of manufacture, and the use of the ordinal numbers is only used to distinguish one element having a certain name from another element having a same name.

In addition, unless steps are specifically described or must occur in sequence, the order of the steps is not limited to that listed above and may be changed or rearranged as desired by the desired design. The embodiments described above may be mixed and matched with each other or with other embodiments based on design and reliability considerations, i.e., technical features in different embodiments may be freely combined to form further embodiments.

Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features of the invention in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so invented, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature of the invention in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. Also in the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware.

Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the method of the invention should not be construed to reflect the intent: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing inventive embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.

The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for longitudinal control of an unmanned vehicle comprises the following steps:

operation S3: establishing a dual-mode MPC, connecting with the linear TD, obtaining an optimal control sequence through the discrete kinematics model based on a reference sequence number sequence input by the linear TD under the condition of existence of control quantity constraint and control quantity increment constraint, and completing the longitudinal control of the unmanned automobile;

in operation S3: based on the appointed step speed signal, obtaining a reasonable reference acceleration signal and a reference speed signal through the linear TD, carrying out an online solving process of an optimization problem by the dual-mode MPC according to the output of the linear TD to obtain an optimal control quantity at each moment, acting on an actual system, enabling a longitudinal system of the vehicle to generate corresponding actions, and continuously repeating the process to enable the vehicle to continuously track the appointed step speed signal.

2. The method of claim 1, wherein the discrete kinematic model is:

wherein x (k) is a state variable, x (k) ═ v (k), a (k)]^TWhere v (k) is the vehicle speed at time k, a (k) is the vehicle acceleration at time k, u (k) is the control input, and u (k) is a_des(k)，a_des(k) In order to expect the acceleration, A is a system matrix, B is a control matrix, C is an output matrix, and the specific form is as follows:

C＝[1 0]

wherein T is a control period, tau and K_mRespectively representing the time constant and the proportional gain obtained based on the data modeling method.

3. The method of claim 1, wherein the linear TD form is:

x_r(k+1)＝A_rx_r(k)+B_rv₀(k) (3)

wherein when the parameter s satisfies

Time T is control period, tracking error e of linear TD_td＝[v_r(k)-v₀(k) a_r(k)]^TConverge to [ 00 ]]^T。

4. The method for longitudinal control of an unmanned vehicle as claimed in claim 1, wherein the dual mode MPC is:

5. The longitudinal control method of the unmanned vehicle as claimed in claim 1, wherein the dual-mode MPC and the linear TD are combined, and the transition process of the unreasonable reference signal is arranged through the linear TD, so as to ensure the smooth proceeding of the optimization solution process of the dual-mode MPC.

6. The longitudinal control method of an unmanned vehicle as claimed in claim 1, wherein the parameter s of the linear TD is determined through a theoretical analysis process, and the convergence of the linear TD can be ensured within a range satisfying 0 ≦ s ≦ 1/T, where T is a control period.

7. The method for controlling the longitudinal direction of the unmanned vehicle as claimed in claim 1, wherein the selection method and rules of the parameters Q, R and P of the dual-mode MPC are determined through a theoretical analysis process, so as to ensure the iterative feasibility and stability of the closed-loop system.