A Composite Hierarchical Anti-Disturbance Control Method for Unknown Disturbances of Vehicles

Abstract

The present invention provides a composite hierarchical anti-interference control method for unknown disturbances of a vehicle. The method includes: considering the existence of unknown disturbances during the movement of the vehicle, establishing a vehicle dynamics equation in the presence of multi-source disturbances, and based on this, further considering the uncertainties existing in the vehicle system, establishing a vehicle dynamics equation based on an interval type-2 T-S fuzzy model; using a fuzzy disturbance observer to estimate the unknown disturbances of the vehicle; respectively constructing a fuzzy integral sliding mode surface in the cases of the vehicle driving straight and changing lanes in combination with the estimation results of the unknown disturbances of the vehicle, and according to the vehicle dynamics equation based on the interval type-2 T-S fuzzy model, using fuzzy integral sliding mode control to establish a corresponding vehicle closed-loop system to control the vehicle to operate stably. The method of the present invention effectively solves the influence on the vehicle stability in the presence of system uncertainties and multi-source disturbances, has strong feasibility, and is beneficial to improving economic benefits.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle stability control, and particularly relates to a composite hierarchical anti-interference control method for unknown disturbances of a vehicle. Background Art

[0002] The stability of a vehicle system is an important guarantee for its safe driving and is a hot topic in the current field of vehicle safety research. Since various control theories and methods have their own characteristics and limitations when solving non-linear vehicle stability control strategies, in-depth research on the analysis and control of vehicle systems has important theoretical significance and engineering application value. In fact, the actual operating system and the environment of the vehicle are complex and changeable, and the vehicle will inevitably be affected by various disturbances during operation. In addition, some parameters of the vehicle itself will also be uncertain during driving. The existence of external disturbances and internal parameter uncertainties reduces the control performance and control accuracy of the vehicle system, and in severe cases, phenomena such as vehicle out of control, skidding (swinging) and overturning may occur, threatening the safety of personnel. Therefore, how to more accurately describe the uncertainties existing in the system for the vehicle system, effectively offset and suppress the multi-source disturbances received by the system, and ensure its stability is a challenging problem.

[0003] Based on the above analysis, there is relatively little research at home and abroad on vehicle modeling based on interval type-2 T-S fuzzy models and the cancellation and suppression of multi-source disturbances, and the anti-disturbance control of vehicle systems based on interval type-2 T-S fuzzy models remains to be solved. Summary of the Invention

[0004] Embodiments of the present invention provide a composite hierarchical anti-interference control method for unknown disturbances of a vehicle to effectively control the stability of the vehicle.

[0005] To achieve the above object, the present invention adopts the following technical solutions.

[0006] A composite hierarchical anti-interference control method for unknown disturbances of a vehicle, comprising:

[0007] According to vehicle dynamics analysis, considering the unknown disturbances existing during the movement of the vehicle, establish a vehicle dynamics equation in the presence of multi-source disturbances;

[0008] Based on the vehicle dynamics equation in the presence of multi-source disturbances, considering the uncertainties existing in the vehicle system, establish a vehicle dynamics equation based on an interval type-2 T-S fuzzy model;

[0009] Design a fuzzy disturbance observer for the vehicle in the presence of multi-source disturbances, and use the fuzzy disturbance observer to estimate the unknown disturbances of the vehicle;

[0010] Construct a fuzzy integral sliding mode surface by combining the estimated results of the unknown disturbances of the vehicle during straight driving and lane-changing of the vehicle respectively. According to the vehicle dynamics equation based on the interval type-2 T-S fuzzy model, a corresponding vehicle closed-loop system is established by using fuzzy integral sliding mode surface control;

[0011] Based on the vehicle closed-loop system, a composite anti-disturbance control strategy is designed by using the observer and controller equations, and the composite anti-disturbance control strategy is used to control the actual yaw rate and the sideslip angle of the vehicle center of mass to approach the desired yaw rate and the sideslip angle of the vehicle center of mass.

[0012] Preferably, according to the vehicle dynamics analysis, considering the unknown disturbances existing in the vehicle during the movement process, a vehicle dynamics equation in the presence of multi-source disturbances is established, including:

[0013] Considering the unknown disturbances existing in the vehicle during the movement process, a vehicle dynamics equation in the presence of multi-source disturbances shown in Equation (1) is established:

[0014]

[0015] where m is the mass of the vehicle, I z is the moment of inertia at the center of gravity (CG), β is the sideslip angle of the vehicle center of mass, r is the yaw rate, l f and l r represent the distances from the front and rear axles to the CG respectively, v is the longitudinal velocity of the vehicle center of mass, M z is the external yaw moment, F yf and F yr are the front and rear lateral tire pressures, respectively dependent on the slip angles α f and α r , δ is the front wheel steering angle. Considering the unknown disturbances matching the input channels, assuming that the deviation angle is relatively small, the lateral tire pressures F yf and F yr are rewritten in the following form:

[0016] F yf (t) = C af α f (t)

[0017] F yr (t) = C ar α r (t)

[0018] where C af and C ar represent the cornering stiffness of the front and rear tires respectively, α f (t) represents the front wheel slip angle, α r (t) represents the rear wheel slip angle, and satisfies the following relationship:

[0019]

[0020]

[0021] Substitute the expressions of the lateral tire pressure F yf and F yr , the slip angles α f and α r of the front and rear wheels into the control equation (1) of the vehicle steering system. Let x(t) = [β(t) r(t)] T , u(t) = M z (t), w(t) = δ(t), and we get:

[0022]

[0023] where:

[0024]

[0025] A, B1, B2 are the corresponding matrices under the expression (2).

[0026] Preferably, the vehicle dynamics equation considering the existence of uncertainties in the vehicle system based on the vehicle dynamics equation under the multi-source interference is established, and the vehicle dynamics equation based on the interval type-2 T-S fuzzy model includes:

[0027] Assume that the moment of inertia I z and the mass m have a certain range of variation during vehicle driving. Select the maximum and minimum values of the reciprocals of I z and m within their respective ranges of variation, and represent them in the following form:

[0028]

[0029]

[0030] Adopt the sector nonlinear modeling method to establish the corresponding membership function, which is expressed as:

[0031]

[0032] According to the above analysis results, use a type-1 T-S fuzzy system containing four fuzzy rules to describe the vehicle model with parameter uncertainties. Considering the uncertainties in the measurement error of the moment of inertia, set I x = I z + i e I z , where i e ∈[-0.01, 0.01], and give the following form of the upper and lower membership functions:

[0033]

[0034] where: i e = 0.01

[0035]

[0036] where: i e = -0.01

[0037] The vehicle system modeling is improved to an interval type-2 T-S fuzzy system with unknown matching disturbances and norm-bounded disturbances:

[0038]

[0039] According to the properties of the interval type-2 T-S fuzzy model, it is obtained that:

[0040]

[0041]

[0042]

[0043] Considering the characteristic that the membership functions of the interval type-2 T-S fuzzy model are unknown, two known weight factors χ i (ζ(t)) and Construct a new membership function:

[0044]

[0045]

[0046] By separating the deterministic part and the uncertain part of the system, the vehicle dynamics equation based on the interval type-2 T-S fuzzy model shown in Equation (12) is obtained:

[0047]

[0048] where:

[0049]

[0050] Preferably, a fuzzy disturbance observer for the vehicle is designed for the case of multi-source disturbances, and the unknown disturbances of the vehicle are estimated by using the fuzzy disturbance observer, including:

[0051] For the unknown disturbances suffered by the vehicle, a fuzzy disturbance observer for the vehicle is designed as follows:

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059] wherein and ν1(t) are auxiliary variables generated by and respectively. The specific forms of G and H are given as follows:

[0060]

[0061] wherein, g 11 and g 12 are positive scalars, is the estimate of the unknown parameter ψ 11 ; ν 11 and are auxiliary variables, γ > 0, which is a parameter to be designed;

[0062] The unknown disturbance of the vehicle is estimated by using the fuzzy disturbance observer to obtain the estimation result of the unknown disturbance of the vehicle

[0063] Preferably, the fuzzy integral sliding mode surface is constructed by combining the estimation result of the unknown disturbance of the vehicle respectively under the conditions of the vehicle driving straight and changing lanes. According to the vehicle dynamics equation based on the interval type-2 T-S fuzzy model, the corresponding vehicle closed-loop system is established by using the fuzzy integral sliding mode surface control, including:

[0064] The design of the integral sliding mode surface under the straight driving state of the vehicle is as follows:

[0065]

[0066] In the formula, and Τ ζ is non-singular, u H (t) represents the initial control rate, is the estimation of the unknown disturbance in the system;

[0067] u H (t) represents the initial control rate:

[0068]

[0069] where Kj , where \(j = 1, 2, 3, 4\) are the gains of the controller \(u\) H (t), and \(r\) j (\(\zeta(t)\)), is the membership function of the fuzzy controller, and \(\hat{d}\) is the estimation of the unknown disturbance within the system;

[0070] According to the sliding mode equivalent control theory, set \(u\) N (t) as a discontinuous control law. Based on the necessary condition \(s(t)=0\) for reaching the fuzzy integral sliding mode surface and the following equivalent control law is obtained:

[0071]

[0072] Substitute the initial control law (21) and the equivalent control law (22) into the vehicle dynamics equation based on the interval type-2 T-S fuzzy model, and the following system dynamic trajectory on the fuzzy integral sliding mode surface is obtained:

[0073]

[0074] Design a switching control system:

[0075]

[0076] The state variables of the fuzzy system are driven to a pre-specified fuzzy integral sliding mode surface;

[0077] Consider the output error between the actual vehicle model and the desired model in the case of lane change as follows:

[0078]

[0079] The design of the integral sliding mode surface under the vehicle lane change state is as follows:

[0080]

[0081] u H (t) represents the initial control law:

[0082]

[0083] According to the sliding mode equivalent control theory, set \(u\) N (t) as a discontinuous control law to ensure that the composite system reaches the sliding mode surface. Based on the necessary condition \(s(t)=0\) for reaching the fuzzy integral sliding mode surface and the following equivalent control law is obtained:

[0084]

[0085] Substitute the initial control rate (26) and the equivalent control rate (27) into the original system, and the system dynamic trajectory on the fuzzy integral sliding surface is obtained as follows:

[0086]

[0087] For the analysis of the reachability problem, design a fuzzy integral sliding mode control rate in the following form:

[0088]

[0089] The state variables of the fuzzy system are driven to the pre-specified fuzzy integral sliding surface.

[0090] The above equations (23) and (28) respectively represent the vehicle closed-loop systems obtained by substituting the initial control rate and the equivalent control rate in the cases of vehicle straight driving and lane change.

[0091] Preferably, design a composite anti-disturbance control strategy based on the vehicle closed-loop system using the observer and controller equations, and use the composite anti-disturbance control strategy to control the actual yaw rate and sideslip angle of the vehicle to approach the desired yaw rate and sideslip angle, including:

[0092] For the vehicle closed-loop system in the vehicle straight driving state, design the following Lyapunov function:

[0093]

[0094] Considering the influence of the membership function on the system stability analysis, introduce a series of slack matrices and parameter inequalities to obtain less conservative stability conditions, so that The corresponding vehicle closed-loop system is asymptotically stable under the action of the proposed anti-disturbance control strategy;

[0095] For the vehicle closed-loop system in the vehicle lane change state, design the following Lyapunov function:

[0096]

[0097] Considering the influence of the membership function on the system stability analysis, introduce a series of slack matrices and parameter inequalities to obtain less conservative stability conditions, so that Thus, the corresponding vehicle closed-loop system can approximate the ideal lane change state, ensure the stability of the vehicle, and control the actual yaw rate and sideslip angle of the vehicle to approach the desired yaw rate and sideslip angle.

[0098] As can be seen from the technical solutions provided by the embodiments of the present invention described above, the present invention proposes a composite hierarchical anti-interference control strategy for this situation, which effectively solves the influence on vehicle stability in the presence of system uncertainties and multi-source interferences, has strong feasibility, and is beneficial to improving economic benefits.

[0099] Additional aspects and advantages of the present invention will be given in part in the following description, which will become apparent from the following description, or can be understood through the practice of the present invention. Brief Description of the Drawings

[0100] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0101] Figure 1 It is a flowchart of a composite hierarchical anti-interference control method based on unknown disturbances of a vehicle provided by an embodiment of the present invention;

[0102] Figure 2 It is a schematic diagram of the force analysis of a two-degree-of-freedom vehicle provided by an embodiment of the present invention;

[0103] Figure 3 It is a schematic diagram of the unknown disturbance estimation error curve of a vehicle in a straight-line driving state provided by an embodiment of the present invention;

[0104] Figure 4 It is a schematic diagram of the system state curve in the composite hierarchical anti-interference control method of a vehicle in a straight-line driving state provided by an embodiment of the present invention;

[0105] Figure 5 It is a schematic diagram of the unknown disturbance estimation error curve of a vehicle in a lane-changing state provided by an embodiment of the present invention;

[0106] Figure 6 It is a schematic diagram of the error state curve in the composite hierarchical anti-interference control method of a vehicle in a lane-changing state provided by an embodiment of the present invention. Detailed Embodiments

[0107] The following details the embodiments of the present invention. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below with reference to the drawings are exemplary and are only used to explain the present invention and should not be construed as limiting the present invention.

[0108] Those skilled in the art can understand that, unless specifically stated otherwise, the singular forms "a", "an", "the" and "said" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present invention means the presence of the stated features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their groups. It should be understood that when we say that an element is "connected" or "coupled" to another element, it can be directly connected or coupled to other elements, or there may also be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or coupling. The term "and / or" used herein includes any and all combinations of any one of the one or more associated listed items.

[0109] Those skilled in the art can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as the general understanding of those of ordinary skill in the art to which the present invention belongs. It should also be understood that terms such as those defined in a general dictionary should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted with an idealized or overly formal meaning unless defined as herein.

[0110] For the convenience of understanding the embodiments of the present invention, the following will further explain with several specific embodiments in conjunction with the accompanying drawings, and each embodiment does not constitute a limitation on the embodiments of the present invention.

[0111] The composite hierarchical anti-interference control method based on unknown vehicle disturbances provided in this embodiment is used for vehicle stability control, and the processing flow of this method is as Figure 1 shown, including the following processing steps:

[0112] Step S1: According to vehicle dynamics analysis, considering the unknown disturbances existing during the movement of the vehicle, establish a vehicle dynamics equation in the presence of multi-source disturbances.

[0113] Step S2: Based on the vehicle dynamics equation in the presence of the above multi-source disturbances, considering the uncertainties existing in the vehicle system, establish a vehicle dynamics equation based on an interval type-2 T-S fuzzy model. Subsequent stability analysis and controller design for the vehicle system are both based on the interval type-2 T-S fuzzy model established in Step S2.

[0114] Step S3: In view of the presence of multi-source disturbances, design a fuzzy disturbance observer for the vehicle to estimate the unknown disturbances of the vehicle;

[0115] Step S4: Construct a fuzzy integral sliding mode surface by combining the above unknown disturbance estimation results of the vehicle in the cases of straight driving and lane change of the vehicle. According to the above vehicle dynamics equation based on the interval type-2 T-S fuzzy model, use the fuzzy integral sliding mode surface control to establish a corresponding vehicle closed-loop system;

[0116] Step S5: Select a suitable Lyapunov function to prove the stability of the vehicle closed-loop system. Based on the vehicle closed-loop system, design a composite anti-interference control strategy using the observer and controller equations. Use the composite anti-interference control strategy to cancel and suppress the multi-source interference existing in the vehicle system, ensure the stability and tracking performance of the system, and control the actual yaw rate and sideslip angle of the vehicle to approach the desired yaw rate and sideslip angle.

[0117] Furthermore, the above Step S1 specifically includes, combining Figure 2 the schematic diagram of the force analysis of the two-degree-of-freedom vehicle shown in

[0118]

[0119] where m is the mass of the vehicle, I z is the moment of inertia at the center of gravity (CG). β is the sideslip angle of the center of mass, r is the yaw rate, l f and l r respectively represent the distances from the front and rear axles to the CG. v is the longitudinal velocity of the center of mass, M z is the external yaw moment. In addition, F yf and F yr are the front and rear lateral tire pressures, respectively, which depend on the slip angles α f and α r , and δ is the front wheel steering angle. Considering the unknown interference matching the input channels and assuming that the deviation angle is relatively small, the lateral tire pressures F yf and F yr can be in the following forms:

[0120] F yf (t) = C af α f (t)

[0121] F yr (t) = C ar α r (t)

[0122] where C af and C ar respectively represent the cornering stiffness of the front and rear tires, α f (t) represents the front wheel slip angle, α r(t) represents the rear wheel slip angle and satisfies the following relationship:

[0123]

[0124]

[0125] Substitute the lateral tire pressure F yf and F yr , the front and rear wheel slip angles α f and α r expressions into the control equation (1) of the vehicle steering system. Let x(t) = [β(t) r(t)] T , u(t) = M z (t), w(t) = δ(t). We get:

[0126]

[0127] Where:

[0128]

[0129] A, B1, B2 are the corresponding matrices under the expression (2).

[0130] Step S2 further includes the following sub-steps:

[0131] S2.1. Describe the uncertainties existing in the vehicle driving process based on the T-S fuzzy model.

[0132] Assume that the moment of inertia I z and the mass m have a certain range of variation. Select the maximum and minimum values of the reciprocals of I z and m within their respective ranges of variation, and represent them in the following form:

[0133]

[0134]

[0135] Adopt the sector non-linear modeling method, and the corresponding membership functions can be calculated as:

[0136]

[0137] According to the above analysis results, a type-1 T-S fuzzy system containing four fuzzy rules can be used to describe the vehicle model with parameter uncertainties. Further considering the uncertainties in the measurement error of the moment of inertia, we set I x = I z + i e I z , where i e∈[-0.01, 0.01], the following forms of upper and lower membership functions are given:

[0138]

[0139] where: i e = 0.01

[0140]

[0141] where: i e = -0.01

[0142] Thus, the vehicle system modeling is improved to an interval type-2 T-S fuzzy system with unknown matching disturbances and norm-bounded disturbances:

[0143]

[0144] According to the properties of the interval type-2 T-S fuzzy model, we can obtain:

[0145]

[0146]

[0147]

[0148] S2.2. Considering the characteristic that the membership functions of the interval type-2 T-S fuzzy model are unknown, two known weight factors χ i (ζ(t)) and are introduced to construct a new membership function:

[0149]

[0150]

[0151] By separating the deterministic part and the uncertain part of the system, the processed fuzzy system is obtained as follows:

[0152]

[0153] where:

[0154]

[0155] Equation (12) represents the vehicle dynamics equation based on the interval type-2 T-S fuzzy model.

[0156] In step S3, for the unknown disturbances acting on the vehicle, a fuzzy disturbance observer for the vehicle is designed as follows:

[0157]

[0158]

[0159]

[0160]

[0161]

[0162]

[0163]

[0164] where and ν1(t) are auxiliary variables generated by and respectively. The specific forms of G and H are given as follows:

[0165]

[0166] where, g 11 and g 12 are positive scalars. Where is the estimate of the unknown parameter ψ 11 . Similarly,

[0167] ν 11 and are auxiliary variables. γ > 0 is a parameter to be designed.

[0168] By designing a new fuzzy disturbance observer, the estimated value of the unknown disturbance is obtained for subsequent design.

[0169] Step S4 further includes the following sub-steps:

[0170] S4.1. Design the integral sliding mode surface under the vehicle straight-line driving state as follows:

[0171]

[0172] This formula is based on the vehicle modeling model and the estimate of the disturbance observer. In the formula and Τ ζ is non-singular, u H (t) represents the initial control rate, is the estimate of the unknown disturbance in the system.

[0173] u H (t) represents the initial control rate:

[0174]

[0175] Among which K j , j = 1, 2, 3, 4 are the gains of the controller u H (t). r j (ζ(t)), is the membership function of the fuzzy controller. is the estimation of the unknown disturbance within the system.

[0176] According to the sliding mode equivalent control theory, set u N (t) as a discontinuous control law to ensure that the composite system can reach the sliding mode surface. According to the necessary condition s(t) = 0 for reaching the fuzzy integral sliding mode surface and the following equivalent control law is obtained:

[0177]

[0178] Substitute the initial control law (21) and the equivalent control law (22) into the original system, and the system dynamic trajectory on the fuzzy integral sliding mode surface is as follows:

[0179]

[0180] In the reaching phase of the sliding mode control, the system state is vulnerable to model inaccuracies, parameter perturbations, and uncertainties. To improve the robustness of the control system, a switching control system also needs to be designed:

[0181]

[0182] Despite uncertainties and multi-source disturbances, the state variables of the fuzzy system can still be driven to the pre-specified fuzzy integral sliding mode surface.

[0183] S4.2. Consider the output error between the actual vehicle model and the desired model in the case of lane change as follows:

[0184]

[0185] The design of the integral sliding mode surface under the vehicle lane change state is as follows:

[0186]

[0187] u H (t) represents the initial control law:

[0188]

[0189] According to the sliding mode equivalent control theory, set u N (t) as a discontinuous control law to ensure that the composite system can reach the sliding mode surface. According to the necessary condition s(t) = 0 for reaching the fuzzy integral sliding mode surface and The following equivalent control rate is obtained:

[0190]

[0191] Substitute the initial control rate (26) and the equivalent control rate (27) into the original system, and the following system dynamic trajectories on the fuzzy integral sliding surface are obtained:

[0192]

[0193] For the analysis of the reachability problem, design a fuzzy integral sliding mode control rate in the following form:

[0194]

[0195] Despite the existence of uncertainties and multi-source interferences, the state variables of the fuzzy system can still be driven to the pre-specified fuzzy integral sliding surface.

[0196] Equations (23) and (28) represent the vehicle closed-loop systems obtained by substituting the initial control rate and the equivalent control rate respectively in the cases of vehicle straight driving and lane change.

[0197] In step S5, appropriate Lyapunov functions are constructed for the closed-loop systems in the cases of straight driving and lane change respectively.

[0198] S5.1. Design the following Lyapunov function for the closed-loop system (23) obtained in the state of vehicle straight driving:

[0199]

[0200] Considering the influence of the membership function on the system stability analysis, introduce a series of slack matrices and parameter inequalities to obtain less conservative stability conditions, so that Thus, the corresponding closed-loop system is asymptotically stable under the action of the proposed anti-interference control strategy.

[0201] S5.2. Design the following Lyapunov function for the closed-loop system (28) obtained in the state of vehicle lane change:

[0202]

[0203] Considering the influence of the membership function on the system stability analysis, introduce a series of slack matrices and parameter inequalities to obtain less conservative stability conditions, so that Thus, the corresponding vehicle closed-loop system can approximate the ideal lane change state and ensure the stability of the vehicle.

[0204] Most actual engineering systems are affected by multi-source interference. A single anti-interference controller has a large conservatism and it is difficult to achieve high-precision control. Moreover, the system stability and anti-interference performance guaranteed by a single controller cannot be satisfied either. Based on this, a composite hierarchical anti-interference control theory for multi-source interference is proposed. Its essence lies in making full use of the characteristics and structures of interference, respectively modeling multi-source heterogeneous interference, designing an interference observer, and then combining corresponding anti-interference control strategies to suppress and cancel interference. In this patent, the composite hierarchical anti-interference control strategy is to combine an interference observer with sliding mode control to cancel and suppress multi-source interference existing in the system.

[0205] A fuzzy model of the vehicle is established to obtain an interval type-2 T-S fuzzy model, which includes unknown matching interference and norm-bounded interference. By designing an interference observer to estimate the unknown interference existing in the vehicle system, an estimation of the unknown interference can be obtained. Based on this, a composite control strategy is designed by combining the fuzzy sliding mode control method to cancel and suppress multi-source interference existing in the vehicle system, and the stability and tracking performance of the system are guaranteed.

[0206] Next, in order to verify the effectiveness of the composite hierarchical anti-interference control method based on unknown disturbances of the vehicle provided in this embodiment, MATLAB is used for simulation experiments and detailed descriptions are made.

[0207] The two-degree-of-freedom vehicle model provided in this embodiment comprehensively considers the influence of unknown interference and parameter uncertainty on vehicle stability. The composite anti-interference control strategy using a fuzzy integral sliding mode surface can achieve the cancellation and suppression of multi-source interference, making the closed-loop system asymptotically stable in both cases.

[0208] Case 1:

[0209] In the simulation experiment, we use the vehicle dynamics model to verify the effectiveness of the proposed composite fuzzy integral sliding mode controller. The parameters used in the vehicle model are: m = 1430 kg, I z = 2430 kg·m 2 , l f = 1.1 m, and l r = 1.4 m. The design of the composite controller adopts the method proposed in the present invention, in which a 20% change in vehicle mass and moment of inertia is considered. In addition, when the vehicle moves in a straight line, we set the speed v = 60 km / h. Thus, we can obtain the form of the membership function and the specific values of each matrix.

[0210] First, we assume that the unknown harmonic parameter is d(t) = 5sin(1t), and the parameters G and H of its disturbance observer are defined as follows:

[0211]

[0212] Secondly, to verify the effectiveness of the method designed based on the separation principle, assume that γ = 200, ι i1 = σ i1 = 1, ι i2 = 1, σ i2 = 0.1, λ i1 = λ i2 = 0.1, i = 1, 2, 3, 4, and By using the MATLAB LMI toolbox, we can obtain:

[0213] K1 = 10 3 ×[0.3215 - 1.7221], K2 = 10 3 ×[0.3215 - 1.7223]

[0214] K3 = 10 3 ×[0.3696 - 1.9766], K4 = 10 3 ×[0.3699 - 1.9783]

[0215] Case 2:

[0216] Lane changing refers to the movement from one lane to another on a road with two or more lanes in the same direction. A composite fuzzy integral sliding mode controller is constructed to ensure the performance of the vehicle system. Using the vehicle model parameters and vehicle speed in Case 1, then, we assume that the unknown harmonic parameter is d(t) = 5sin(1t). By setting γ = 1000, g 11 = 10 and g 12 = 120, λ i1 = λ 12 = λ 22 = 0.01, λ 32 = λ 42 = 0.1, i = 1, 2, 3, 4. We analyze the interference estimation effect and system stability in this case.

[0217] K1 = 10 4 ×[6.5904 - 7.1210], K2 = 10 4 ×[6.2802 - 6.7919]

[0218] K3 = 10 4 ×[8.7202 - 9.3323], K4 = 10 4 ×[8.7322 - 9.3456]

[0219] Based on the above parameters, the composite control strategy proposed by the present invention is verified by simulation through Case 1 and Case 2 respectively.

[0220] The schematic diagram of the unknown interference estimation error curve under the straight-line driving state of the vehicle provided by the embodiment of the present invention is as Figure 3 shown, and the schematic diagram of the system state curve in the composite hierarchical anti-interference control method under the straight-line driving state of the vehicle is as Figure 4 shown. The schematic diagram of the unknown interference estimation error curve under the lane-changing state of the vehicle is as Figure 5 shown, and the schematic diagram of the error state curve in the composite hierarchical anti-interference control method under the lane-changing state of the vehicle is as Figure 6 shown. Figure 3 And Figure 4 show the schematic diagram of the unknown interference estimation error curve under the straight-line driving state of the vehicle and the schematic diagram of the system state curve in the composite hierarchical anti-interference control method; Figure 5 And Figure 6 show the schematic diagram of the unknown interference estimation error curve under the lane-changing state of the vehicle and the schematic diagram of the error state curve in the composite hierarchical anti-interference control method. It can be seen from the simulation schematic diagram that the proposed interference observer can estimate the unknown interference in the presence of multi-source interference, and the proposed composite hierarchical anti-interference control method based on the unknown disturbance of the vehicle can achieve the cancellation and suppression of interference, ensuring the stability of the closed-loop system in both cases.

[0221] Through the above analysis, the effectiveness of the composite hierarchical anti-interference control strategy based on the unknown disturbance of the vehicle provided by this embodiment is proved.

[0222] In summary, the method of the embodiment of the present invention can effectively describe and characterize the parameter uncertainty existing in the vehicle system and the multi-source interference received; effectively attenuate and suppress the influence of multi-source interference on the stability of the vehicle system; enable the sideslip angle and yaw angular velocity of the vehicle system to have good tracking performance. The present invention solves the influence of system uncertainty and unknown disturbance on the vehicle driving process.

[0223] Those of ordinary skill in the art can understand that the drawings are only schematic diagrams of one embodiment, and the modules or processes in the drawings are not necessarily essential for implementing the present invention.

[0224] From the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention, in essence, or the part that makes a contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0225] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the differences between each embodiment and other embodiments are emphasized. In particular, for the apparatus or system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and reference can be made to the relevant parts of the method embodiments for the relevant content. The apparatus and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative work.

[0226] As mentioned above, the above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A composite hierarchical anti-interference control method for unknown disturbances of vehicles, characterized in that, Including: According to vehicle dynamics analysis, considering the unknown disturbances existing during vehicle motion, establish a vehicle dynamics equation in the presence of multi-source disturbances; Based on the vehicle dynamics equation in the presence of multi-source disturbances, considering the uncertainties existing in the vehicle system, establish a vehicle dynamics equation based on the interval type-2 T-S fuzzy model; Design a fuzzy disturbance observer for the vehicle in the presence of multi-source disturbances, and use the fuzzy disturbance observer to estimate the unknown disturbances of the vehicle; Construct a fuzzy integral sliding mode surface by combining the estimated results of the unknown disturbances of the vehicle respectively under the conditions of vehicle straight driving and lane change. According to the vehicle dynamics equation based on the interval type-2 T-S fuzzy model, use the fuzzy integral sliding mode surface control to establish a corresponding vehicle closed-loop system; Based on the vehicle closed-loop system, design a composite anti-disturbance control strategy using the observer and controller equations, and use the composite anti-disturbance control strategy to control the actual yaw rate and sideslip angle of the vehicle to approach the desired yaw rate and sideslip angle; The above-mentioned according to vehicle dynamics analysis, considering the unknown disturbances existing during vehicle motion, establish a vehicle dynamics equation in the presence of multi-source disturbances, including: Considering the unknown disturbances existing during vehicle motion, establish the vehicle dynamics equation in the presence of multi-source disturbances shown in Equation (1): where m is the mass of the vehicle, I z is the moment of inertia at the center of gravity (CG), β is the sideslip angle of the center of mass, r is the yaw rate, l f and l r respectively represent the distances from the front and rear axles to the CG, v is the longitudinal velocity of the center of mass, M z is the external yaw moment, F yf and F yr are the front and rear lateral tire pressures, respectively dependent on the slip angles α f and α r , δ is the front wheel steering angle. Considering the existence of unknown disturbances matching the input channel and assuming that the deviation angle is relatively small, the lateral tire pressures F yf and F yr are rewritten in the following form: F yf f(t) = C af α f α(t) F yr f(t) = C ar α r α(t) Among them, C af and C ar represent the cornering stiffness of the front and rear tires respectively, α f (t) represents the front wheel slip angle, α r (t) represents the rear wheel slip angle, and they satisfy the following relationship: Substitute the expressions of the lateral tire pressure F yf and F yr , the slip angles α f and α r of the front and rear wheels into the control equation (1) of the vehicle steering system. Let x(t) = [β(t) r(t)] T , u(t) = M z (t), w(t) = δ(t), and we get: Where: A, B1, and B2 are the corresponding matrices under this expression (2).

2. The method according to claim 1, wherein The above-mentioned based on the vehicle dynamics equation in the presence of multi-source disturbances, considering the uncertainties existing in the vehicle system, establish a vehicle dynamics equation based on the interval type-2 T-S fuzzy model, including: Suppose the moment of inertia \(I\) during vehicle driving z and the mass \(m\) have a certain range of variation. Select the maximum and minimum values of the reciprocals of \(I\) and \(m\) within their respective ranges of variation, and express them in the following form: z ​ Adopt the sector nonlinear modeling method to establish the corresponding membership function, expressed as: Based on the above analysis results, a type-1 T-S fuzzy system with four fuzzy rules is used to describe the vehicle model with uncertain parameters. Considering the uncertainty in the measurement error of the moment of inertia, $I$ is set as x $I = I$ z $+ i$ e $I$ z , where $i$ e $\in[-0.01, 0.01]$, and the following forms of the membership functions are given: where: i e = 0.01 where: i e = -0.01 The vehicle system modeling is improved to an interval type-2 T-S fuzzy system with unknown matching disturbances and norm-bounded disturbances; According to the properties of the interval type-2 T-S fuzzy model, obtain: Considering the characteristics of the unknown membership functions of the interval type-2 T-S fuzzy model, two known weight factors χ i (ζ(t)) and Construct a new membership function: By separating the deterministic part and the uncertain part of the system, obtain the vehicle dynamics equation based on the interval type-2 T-S fuzzy model shown in Equation (12): Where:

3. The method according to claim 2, characterized in that, The above-mentioned design a fuzzy disturbance observer for the vehicle in the presence of multi-source disturbances, and use the fuzzy disturbance observer to estimate the unknown disturbances of the vehicle, including: For the unknown disturbances suffered by the vehicle, design the following fuzzy disturbance observer for the vehicle: where and ν1(t) are auxiliary variables generated by and respectively. The specific forms of G and H are given as follows: where, g 11 and g 12 are positive scalars, is an estimate of the unknown parameter ψ 11 , ν 11 and are auxiliary variables, γ > 0, and are parameters to be designed; The unknown disturbance of the vehicle is estimated by using the fuzzy disturbance observer to obtain the estimation result of the unknown disturbance of the vehicle 4. The method according to claim 3, characterized in that The above-mentioned construct a fuzzy integral sliding mode surface by combining the estimated results of the unknown disturbances of the vehicle respectively under the conditions of vehicle straight driving and lane change. According to the vehicle dynamics equation based on the interval type-2 T-S fuzzy model, use the fuzzy integral sliding mode surface control to establish a corresponding vehicle closed-loop system, including: The design of the integral sliding mode surface under the vehicle straight driving state is as follows: wherein, and Τ ζ is non-singular, u H (t) represents the initial control rate, is the estimate of the unknown disturbance within the system; u H (t) represents the initial control rate: where K j , j = 1, 2, 3, 4 are the gains of the controller u H (t), is the membership function of the fuzzy controller, is the estimate of the unknown disturbance within the system; According to the sliding mode equivalent control theory, set u N (t) as the discontinuous control rate. According to the necessary condition s(t)=0 for reaching the fuzzy integral sliding mode surface and the following equivalent control rate is obtained: Substitute the initial control rate (21) and the equivalent control rate (22) into the vehicle dynamics equation based on the interval type-2 T-S fuzzy model, and obtain the system dynamic trajectory on the fuzzy integral sliding mode surface as follows: Design a switching control system: The state variables of the fuzzy system are driven to the pre-specified fuzzy integral sliding mode surface; Consider the output error between the actual vehicle model and the desired model under the condition of lane change as follows: The design of the integral sliding mode surface under the vehicle lane change state is as follows: u H (t) represents the initial control rate: According to the sliding mode equivalent control theory, set u N (t) as the discontinuous control law to ensure that the composite system reaches the sliding mode surface. According to the necessary condition s(t) = 0 for reaching the fuzzy integral sliding mode surface and the following equivalent control law is obtained: Substitute the initial control rate (26) and the equivalent control rate (27) into the original system, and the system dynamic trajectory on the fuzzy integral sliding surface is obtained as follows: For the analysis of the reachability problem, design a fuzzy integral sliding mode control rate in the following form: The state variables of the fuzzy system are driven to a pre-specified fuzzy integral sliding surface; The above equations (23) and (28) respectively represent the vehicle closed-loop systems obtained after substituting the initial control rate and the equivalent control rate in the cases of vehicle straight driving and lane change.

5. The method according to claim 4, characterized in that, The composite anti-interference control strategy is designed based on the vehicle closed-loop system using the observer and controller equations, and the composite anti-interference control strategy is used to control the actual yaw rate and the sideslip angle of the vehicle's center of mass to approach the desired yaw rate and sideslip angle of the vehicle's center of mass, including: For the vehicle closed-loop system in the vehicle straight driving state, design the following Lyapunov function: Considering the influence of the membership function on the system stability analysis, a series of slack matrices and parameter inequalities are introduced to obtain stability conditions with less conservatism, such that the corresponding vehicle closed-loop system is asymptotically stable under the proposed anti-disturbance control strategy; For the vehicle closed-loop system in the vehicle lane change state, design the following Lyapunov function: Considering the influence of the membership function on the system stability analysis, a series of slack matrices and parameter inequalities are introduced to obtain stability conditions with less conservatism, so that the corresponding vehicle closed-loop system can approximate the ideal lane-changing state, and control the actual yaw rate and sideslip angle of the vehicle's center of mass to approach the desired yaw rate and sideslip angle of the center of mass.

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