Biped walking race robot data driving switching gain estimation technology

Abstract

The invention relates to a biped walking race robot data driving switching gain estimation technology. According to the technology, the behavior theory of a linear system is utilized, a data interpretation system of an unknown dynamic hybrid system is constructed, a global exponential stability criterion is established in combination with a data-based linear matrix inequality, and the global exponential stability of a biped robot system is ensured under the mild condition; therefore, the biped walking race robot can operate normally without depending on a model. Meanwhile, according to the hybrid estimation scheme based on data driving, a system model identification step is eliminated, and resource consumption is reduced.

Description

Technical Field

[0001] The present application relates to the field of robot control technology, and in particular to a data-driven switching gain estimation technology for a bipedal race walking robot. Background Art

[0002] With the deepening of control science, the innovation of sensor technology, and the rapid development of artificial intelligence and machine learning, robot systems have been able to effectively deal with uncertainties and external disturbances in the walking process with the help of advanced adaptive algorithms. Bipedal race walking robots, as a mechanical device that specializes in walking on two legs, are designed to integrate into human living spaces. This type of robot stands out for its unique ability to walk in unstructured and complex environments, showing strong environmental adaptability. Bipedal race walking robots have shown wide application potential in many key areas, covering disaster relief, service industry, military security, entertainment and education, and many other aspects.

[0003] However, these application scenarios are often accompanied by complex and changeable tasks and strong interference characteristics, which puts higher requirements on bipedal race walking robots, including system stability, flexibility and environmental adaptability. Therefore, ensuring that bipedal race walking robots can stably perform their tasks under various extreme conditions has become the key to promoting their technological development and practical application. Summary of the invention

[0004] Based on this, it is necessary to provide a data-driven switching gain estimation technology for a bipedal race walking robot to address the above technical problems, so that the bipedal robot can perform well when facing diverse environments and task requirements.

[0005] In a first aspect, the present application provides a data-driven switching gain estimation technology for a bipedal race walking robot. The technology includes:

[0006] A state space model of a model-based bipedal robot system is constructed using a hybrid system framework, and an equivalent system model of a data-based bipedal robot system is constructed based on the state space model; wherein both the space state model and the equivalent system model include unknown system matrices;

[0007] Constructing prior knowledge of noise in bipedal race walking robot systems;

[0008] According to the prior knowledge of the equivalent system model and noise, a set of unknown system matrices is obtained; wherein the expression of the set of unknown system matrices includes a first matrix of the unknown system matrices stripped off;

[0009] The duality lemma is used to process the set of unknown system matrices and obtain a second matrix related to the first matrix;

[0010] A gain observer is constructed based on the state space model, and an augmented system is obtained according to the gain observer and the equivalent system model;

[0011] Constructing the first model-based stability criterion of the bipedal robot system according to the augmented system;

[0012] The first stability criterion is processed by using the S lemma and substituted into the second matrix to obtain the second stability criterion based on the data;

[0013] A gain estimate is obtained based on a second stability criterion.

[0014] In one embodiment, the state space model is represented as:

[0015]

[0016] v=Cx,

[0017] Among them, x + is x + (k) = time shift of x(k+1); is the time derivative of the state x; x(t, k)∈R n represents the state of the bipedal robot system; y(t, k)∈R s is the output of the bipedal robot system; κ(t, k)∈[0, κ d ], the known constant κ d ∈R ＞0 is the periodic switching condition of the biped robot system; A, D, and C are all unknown system matrices.

[0018] In one embodiment, the equivalent system model is expressed as:

[0019]

[0020] Y ★ =CX ★ +V ★ ,

[0021] in, and Both are Hankel matrices representing the data of the biped robot system.

[0022] In one embodiment, the prior knowledge of noise is expressed as:

[0023]

[0024] in, Λ ij , i, j = 1, 2 is a known matrix, Λ ij satisfy: I is the identity matrix.

[0025] In one embodiment, the set of unknown system matrices is represented as:

[0026]

[0027] in, Δ is the first matrix.

[0028] In one embodiment, the set of unknown system matrices processed by the duality lemma is expressed as:

[0029]

[0030] in, Θ is the second matrix.

[0031] In one embodiment, the gain observer is expressed as:

[0032]

[0033] in, is an estimate of x(t, k), L z ∈R n×s is the gain matrix, z represents the index of the gain observer;

[0034] The augmented system is expressed as:

[0035]

[0036] Among them, the error

[0037] In one embodiment, the first stability criterion is expressed as:

[0038]

[0039] P 1 ≥(1-μ 2 ) τ P 2 , P 2 ≥(1-μ 1 ) τ P 1

[0040] The first stability criterion is processed by using the S lemma to obtain the second stability criterion, which is expressed as:

[0041]

[0042] P1 ≥(1-μ 2 ) τ P 2 , P 2 ≥(1-μ 1 ) τ P 1

[0043] Among them, κ d >0, τ>0, γ, μ 1 , μ 2 ,λ≥0,δ≥0,ρ>0,θ>0 are all unknown constants, R n×n matrix and R n×p Matrix F 1 、F 2 All are unknown matrices;

[0044] Solve the second stability criterion and obtain the unknown matrix P 1 , P 2 、F 1 and F 2 ;

[0045] The gain estimate is expressed as:

[0046] L z =P z -1 F z , z∈[1,2].

[0047] In a second aspect, the present application also provides a data-driven switching gain estimation system for a bipedal race walking robot. The system comprises:

[0048] A model building module is used to build a state space model of a model-based bipedal robot system using a hybrid system framework, and to build an equivalent system model of a data-based bipedal robot system based on the state space model; wherein both the space state model and the equivalent system model include unknown system matrices;

[0049] Noise constraint module, used to build prior knowledge of noise in the bipedal race walking robot system;

[0050] A first calculation module is used to obtain a set of unknown system matrices according to an equivalent system model and prior knowledge of noise; wherein the expression of the set of unknown system matrices includes a first matrix from which the unknown system matrices are stripped;

[0051] A second calculation module is used to process a set of unknown system matrices using a duality lemma to obtain a second matrix related to the first matrix;

[0052] An observer construction module is used to construct a gain observer based on a state space model, and obtain an augmented system according to the gain observer and an equivalent system model;

[0053] A stability design module for constructing a model-based first stability criterion for a bipedal robot system according to an augmented system;

[0054] A third calculation module is used to process the first stability criterion by using the S lemma and substitute it into the second matrix to obtain a second stability criterion based on the data;

[0055] A gain estimation module is used to obtain a gain estimate according to a second stability criterion.

[0056] In a third aspect, the present application further provides a bipedal race walking robot, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above-mentioned data-driven switching gain estimation technology for a bipedal race walking robot when executing the computer program.

[0057] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps in the above-mentioned data-driven switching gain estimation technology for a bipedal race walking robot when the computer program is executed by a processor.

[0058] In a fifth aspect, the present application further provides a computer program product, including a computer program, which, when executed by a processor, implements the steps in the above-mentioned data-driven switching gain estimation technology for a bipedal race walking robot.

[0059] The above-mentioned data-driven switching gain estimation technology for a bipedal race walking robot models the bipedal robot system as an unknown hybrid system and then transforms it into a data-driven switching gain estimation method. This method does not require a clear physical model to be preset, making the data-driven method very effective when facing highly complex and nonlinear systems. At the same time, the data-driven hybrid estimation scheme eliminates the system model identification step and reduces resource consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A schematic diagram of a flow chart of a data-driven switching gain estimation technology for a bipedal race walking robot in one embodiment;

[0061] Figure 2 A schematic diagram of switching gain observation of a bipedal robot system in one embodiment;

[0062] Figure 3 A schematic diagram of data drive of a bipedal robot system in one embodiment;

[0063] Figure 4It is a structural block diagram of a data-driven switching gain estimation system for a bipedal race walking robot in one embodiment. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0065] For ease of understanding, necessary explanations are given to the technical terms that appear in the present invention. A hybrid system is a unique dynamic system that combines a continuous dynamic system with a discrete dynamic system and handles the interaction between the two. This system model has shown extensive application value in multiple academic and industrial fields, such as robotics, industrial automation, aviation flight control, power system management, traffic network optimization, and bioengineering. In the operation of a bipedal walking robot, the concept of a hybrid system is particularly important. The walking process of a robot not only involves continuous limb movement, but also includes a series of key discrete state transition events, especially at the moment when the foot contacts and detaches from the ground. These state changes have a greater impact on the dynamic behavior of the robot. The gait cycle of bipedal walking consists of multiple alternating continuous motion stages (such as single-leg support, double-leg support, leg swing, etc.), and the transitions between the stages show obvious discreteness. It is this interweaving characteristic of continuous motion and discrete events that makes the hybrid system an ideal framework for describing the dynamic behavior of a bipedal robot. It allows researchers to finely control the robot's continuous motion trajectory and accurately capture and process those key discrete state transition points in the same model, thereby achieving a comprehensive characterization of the complex motion characteristics of the bipedal race walking robot. Therefore, the hybrid system theory occupies a core position in the modeling of bipedal race walking robots and provides a strong theoretical support for achieving efficient and stable walking control.

[0066] In the precision control system of bipedal race walking robots, noise suppression and state observers are two indispensable elements, which are directly related to the stability, accuracy and robustness of robots in complex and changing environments. Noise, as the main manifestation of external interference, poses a challenge to the robot's maintenance of balance, gait planning and precise movement. To meet this challenge, the bipedal robot system relies on efficient observers to accurately estimate the internal state of the system. As an observation tool, the core value of the observer is to use noisy or erroneous sensor data, through complex dynamic models and feedback mechanisms, to indirectly infer key state variables that are difficult to measure directly (such as accurate center of mass position, joint angle, etc.). This ability is particularly critical for bipedal race walking robots, because it is neither realistic nor costly to directly and comprehensively measure all state variables in a complex execution environment. In addition, the observer also plays an important role in system fault diagnosis and response. It can quickly identify the source of the fault, quantify the degree of the fault, and provide maintenance personnel with instant and accurate fault information, greatly shortening the troubleshooting and repair time, and ensuring that the robot system quickly resumes stable operation. Therefore, by optimizing the design and application of the observer, the bipedal race walking robot can demonstrate more outstanding control performance and adaptability when facing noise interference and complex environments.

[0067] In the study of robot learning, especially bipedal race walking robots, there are two completely different strategic frameworks: model-driven and data-driven. The model-driven approach is rooted in a deep understanding of the physical mechanism of the system. It uses accurate mathematical models to describe the dynamic changes of the robot during walking. This process emphasizes the rigor of theoretical derivation and system analysis. However, in the face of complex physical phenomena in the real environment (such as variable friction, unpredictable collisions and flexible contacts, etc.), it is challenging to build a comprehensive and accurate model. In contrast, the data-driven approach shows unique advantages. It uses advanced data science technologies such as machine learning and deep learning to directly extract the patterns and laws of robot behavior from massive data without the need to preset a clear physical model. This feature makes the data-driven approach very effective when facing highly complex and nonlinear systems, especially when the model is difficult to accurately establish or there is a lot of uncertainty. Through continuous practice and feedback learning, the data-driven model can autonomously optimize the control strategy to enable the robot to adapt to diverse environments and task requirements. In view of the above advantages, the data-driven approach has become a research hotspot in the field of robot learning and is widely used in key tasks such as stability control and intelligent walking. It not only promotes the progress of bipedal race walking robot technology, but also provides strong support for the realization of more intelligent, flexible and adaptable robot systems. Therefore, data-driven methods are expected to play a more core and critical role in future robot research.

[0068] The present application embodiment provides a data-driven switching gain estimation technology for a bipedal race walking robot, such as Figure 1 As shown, the following steps are included:

[0069] Step 101, constructing a state space model of a model-based bipedal robot system using a hybrid system framework, and constructing an equivalent system model of a data-based bipedal robot system based on the state space model; wherein both the space state model and the equivalent system model include unknown system matrices.

[0070] The data-driven state space model of the bipedal race walking robot system is constructed to describe an unknown hybrid system of the bipedal robot. The specific modeling is as follows:

[0071]

[0072] y=Cx,

[0073] Among them, x + is x + (k) = time shift of x(k+1), k is a positive integer; is the time t derivative of state x, specifically expressed as: x(t, k)∈R n represents the state of the bipedal robot system; y(t, k)∈R s is the output of the bipedal robot system; κ(t, k)∈[0, κ d ], the known constant κ d ∈R ＞0 is the periodic switching condition of the bipedal robot system; A∈R n×n , D∈R n×n and C∈R s×n is the unknown system matrix, A is the state matrix of continuous-time dynamics, D is the state matrix of discrete-time dynamics, and C is the output matrix of the system.

[0074] Based on the state space model, a data-driven equivalent system model is constructed in the bipedal race walking robot system, which is in the following form:

[0075]

[0076] Y ★ =CX ★ +V★,

[0077] in, Where w and v are process noise and measurement noise respectively;

[0078]

[0079] and is the Hankel matrix of the biped robot system data representation in the continuous time domain or in the discrete time domain, where u represents the system output.

[0080] Step 102, constructing prior knowledge of noise in the bipedal race walking robot system.

[0081]

[0082] in, Λ ij , i, j = 1, 2 is a known matrix, Λ ij satisfy: The prior knowledge of noise constrains the maximum value of the noise.

[0083] Step 103, obtaining a set of unknown system matrices according to the equivalent system model and prior knowledge of noise; wherein the expression of the set of unknown system matrices includes stripping off the first matrix of the unknown system matrices.

[0084] In this embodiment, a set of system matrices based on data drive in a biped robot system is designed, and its form is as follows:

[0085]

[0086] in, Δ is the first matrix.

[0087] Step 104: Use the duality lemma to process the set of unknown system matrices to obtain a second matrix related to the first matrix.

[0088] In the duality lemma, if Δ is reversible, then the following sets hold:

[0089]

[0090] in, Θ is the second matrix.

[0091] Step 105: construct a gain observer based on the state space model, and obtain an augmented system according to the gain observer and the equivalent system model.

[0092] The data-driven gain observer in the bipedal race walking robot system is as follows:

[0093]

[0094] in, is an estimate of x(t, k), L z ∈R n×sis the gain matrix. If the error It converges to 0 for all (t, k)∈K. Figure 2 The hybrid switching gain observation framework is shown. Assume that the observer gain designed in region 1 is L 1 , the observer gain designed in region 2 is L 2 , where y s is the switching point between region 1 and region 2, and ψ is the minimum residence time.

[0095] According to the gain observer, a data-driven augmented system for the bipedal race walking robot system is established in the following form:

[0096]

[0097] Step 106: construct a first stability criterion based on the model of the biped robot system according to the augmented system.

[0098] The first stability criterion is the global exponential stability criterion.

[0099] Each solution of the hybrid system in step 101 is defined in the hybrid time domain as:

[0100] K: ={(t, k), t∈[t k , t k+1 ],k∈N},

[0101] Among them, t k : = kκ d and t k+1 : =(k+1)κ d represents the transition period of action switching in the biped robot. As is known to all, the system solution in step 101 is a local absolutely continuous function mapping (t, k) ∈ K. Let ζ(t, k, x 0 ) is the solution of the system of equations in step 101 for (t, k)∈K, where x 0 is the initial condition.

[0102] For the state space model in step 101, the definition of stability is described as:

[0103] Closed Set is globally exponentially stable if there exist constants a, b>0, and c such that

[0104] ||ζ(t 0 , k 0 , x 0 )|| H <a,

[0105] ||ζ(t,k,x 0 )|| H ≤b||ζ(t 0, k 0 ,x 0 )|| H e -c(t+k) ,

[0106] For any initial condition x 0 , initial time t 0 =0, k 0 =0 and (t, k)∈K holds, then the bipedal robot system is stable.

[0107] Therefore, the first model-based stability criterion is expressed as follows using linear matrix inequalities:

[0108] If there is a constant κ d >0,τ>0,γ,μ 1 , μ 2 , R n×n matrix R n×p Matrix F 1 and F 2 , so that:

[0109]

[0110] P 1 ≥(1-μ 2 ) τ P 2 , P 2 ≥(1-μ 1 ) r P 1 (6)

[0111] For all times (t, k)∈K, the bipedal robot system is stable.

[0112] In another embodiment, since the uncertainty term in conditions (1) and (2) in the first stability criterion is only the unknown system matrix A, a data set containing only A is required. From step 102 to step 104, it can be seen that the unknown system matrix A is contained in the noise data W ★ It is not difficult to conclude that the noise data W ★ The following conditions are met:

[0113]

[0114] in, Γ ij , i, j = 1, 2 are known matrices, Γ ij satisfy: and Using a technique similar to step 103, the noise data W ★ The unknown system matrix A in is peeled off to obtain:

[0115]

[0116] in, In this embodiment, Ω is the first matrix. According to the duality lemma, this condition is equivalent to:

[0117]

[0118] in, In this embodiment, Y is the second matrix.

[0119] Step 107: Use the S-lemma to process the first stability criterion and substitute it into the second matrix to obtain a second stability criterion based on the data.

[0120] The S lemma is: Assume R (s+h)×(s+h) matrix and is symmetric. If there exists R s×h The matrix N is such that the Slater condition is:

[0121]

[0122] For all N∈R s×h , S 22 ≤0, Q 22 ≤0 and If and only if there exist the following constants σ≥0 and ρ>0,

[0123]

[0124] At the same time, the observable assumption is:

[0125] If the data (X, Y ★ ) is observable if and only if Make

[0126]

[0127] Using the S lemma to process the first stability criterion, we can obtain the second stability criterion. Under the above assumptions, if there are constants λ≥0, δ≥0, ρ>0, θ>0, κ d >0, τ>0, γ, μ 1 , μ 2 and R n×n matrix and R n×p Matrix F 1 、F 2 , so that

[0128]

[0129] P 1 ≥(1-μ 2 ) τ P 2 , P 2 ≥(1-μ 1 ) τ P 1 (6)

[0130] If it holds, the bipedal robot system is stable.

[0131] In this embodiment, conditions (1) and (2) in step 106 with the unknown system matrix A are converted into conditions (1) and (2) in step 107 with only the second matrix Y, and conditions (3) and (4) in step 106 with the unknown system matrices C and D are converted into conditions (1) and (2) in step 107 with only the second matrix Θ, thereby realizing the conversion of the first stability criterion based on the model to the second stability criterion based on the data of the biped robot system.

[0132] Step 108, obtaining a gain estimate according to a second stability criterion.

[0133] By solving the second stability criterion, the unknown matrix P is calculated. 1 , P 2 、F 1 and F 2 , then the switching gain based on the data is estimated as:

[0134] L z =P z -1 F z , z∈[1,2].

[0135] To verify the stability process of the data-driven switching gain estimation in the bipedal race walking robot system, assuming that the data-driven switching gain estimation in the bipedal race walking robot system given in step 107 is feasible, the verification steps are as follows:

[0136] S1. Using the S lemma, conditions (1) and (2) in step 107 are given as:

[0137]

[0138] S2. By multiplying the left and right sides of the S1 formula by (I, A) and its transpose, we get:

[0139]

[0140] then,

[0141]

[0142] This means that conditions (1) and (2) in step 107 can ensure that conditions (1) and (2) in step 106 are met.

[0143] S3. Similarly, applying the S lemma to conditions (3) and (4) in 107 yields:

[0144]

[0145] The data-based switching gain estimate L in step 108 is z Substituting into the above formula, we get:

[0146]

[0147] S4. Definition The expression is:

[0148]

[0149] Through Multiply the left and right sides of by (I, D, C) and its transpose, and we get

[0150]

[0151] It is not difficult to conclude that conditions (3) and (4) in step 106 are met.

[0152] like Figure 3 The schematic diagram of the data drive of the bipedal robot system in this application is shown. In the figure, the sensor collects the input state data, output state data, etc. of the bipedal robot system and transmits them to the unknown hybrid system. The sensor also collects noise data. The data gain observer uses the switching gain estimation technology proposed in the present invention to estimate the switching gain based on the data collected by the sensor. The data gain observer outputs the switching gain estimation and transmits it to the actuator of the bipedal robot system for execution, and feeds the execution result back to the observer. The state changes of the bipedal robot caused by the execution of the actuator will also be reflected in the unknown hybrid system and noise data.

[0153] The present invention proposes a data-driven estimation technology for a bipedal race walking robot with an unknown hybrid system with periodic jumps. By designing a state observer, a global exponential stability criterion is established using linear matrix inequality, so that the bipedal race walking robot system is in a relatively stable state, ensuring that the bipedal race walking robot system can perform tasks normally without relying on the model.

[0154] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0155] Based on the same inventive concept, the embodiment of the present application also provides a bipedal race walking robot data-driven switching gain estimation system for implementing the above-mentioned bipedal race walking robot data-driven switching gain estimation technology. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above-mentioned technology, so the specific limitations in one or more bipedal race walking robot data-driven switching gain estimation system embodiments provided below can refer to the limitations of a bipedal race walking robot data-driven switching gain estimation technology above, and will not be repeated here.

[0156] In one embodiment, Figure 4 As shown, a data-driven switching gain estimation system for a bipedal race walking robot is provided, comprising:

[0157] A model building module 401 is used to build a state space model of a model-based bipedal robot system using a hybrid system framework, and to build an equivalent system model of a data-based bipedal robot system according to the state space model; wherein both the space state model and the equivalent system model include unknown system matrices;

[0158] A noise constraint module 402 is used to construct prior knowledge of noise in the bipedal race walking robot system;

[0159] A first calculation module 403 is used to obtain a set of unknown system matrices according to an equivalent system model and prior knowledge of noise; wherein the expression of the set of unknown system matrices includes a first matrix from which the unknown system matrices are stripped;

[0160] A second calculation module 404 is used to process a set of unknown system matrices using the duality lemma to obtain a second matrix related to the first matrix;

[0161] An observer construction module 405 is used to construct a gain observer based on the state space model, and obtain an augmented system according to the gain observer and the equivalent system model;

[0162] A stability design module 406, for constructing a model-based first stability criterion of the biped robot system according to the augmented system;

[0163] A third calculation module 407 is used to process the first stability criterion using the S lemma and substitute it into the second matrix to obtain a second stability criterion based on the data;

[0164] The gain estimation module 408 is configured to obtain a gain estimate according to a second stability criterion.

[0165] Each module in the above-mentioned bipedal race walking robot data driven switching gain estimation system can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the bipedal race walking robot in the form of hardware, or can be stored in the memory of the bipedal race walking robot in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0166] In one embodiment, a bipedal race walking robot is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in all the above technical embodiments when executing the computer program.

[0167] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in all the above technical embodiments are implemented.

[0168] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in all the above technical embodiments when executed by a processor.

[0169] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0170] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0171] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0172] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A data-driven switching gain estimation technology for a bipedal race walking robot, characterized in that: The techniques include: A state space model of a model-based bipedal robot system is constructed using a hybrid system framework, and an equivalent system model of the bipedal robot system based on data is constructed according to the state space model; wherein both the state space model and the equivalent system model include unknown system matrices; Constructing prior knowledge of noise in the bipedal race walking robot system; According to the equivalent system model and the prior knowledge of the noise, a set of unknown system matrices is obtained; wherein the expression of the set of unknown system matrices includes a first matrix of the unknown system matrix stripped off; Processing the set of unknown system matrices using the duality lemma to obtain a second matrix related to the first matrix; Constructing a gain observer based on the state space model, and obtaining an augmented system according to the gain observer and the equivalent system model; constructing a model-based first stability criterion of the biped robot system according to the augmented system; Processing the first stability criterion using the S lemma and substituting it into the second matrix to obtain a second stability criterion based on data; A gain estimate is obtained according to the second stability criterion.

2. The technology according to claim 1, characterized in that The state space model is expressed as: Among them, x + is x + (k) = time shift of x(k+1); is the time derivative of the state x; x(t,k)∈R n represents the state of the bipedal robot system; y(t,k)∈R s is the output of the bipedal robot system; κ(t,k)∈[0,κ d ], the known constant κ d ∈R ＞0 is the periodic switching condition of the bipedal robot system; A, D, and C are all the unknown system matrices.

3. The technology according to claim 2, characterized in that The equivalent system model is expressed as: Y ★ =CX ★ +V ★ , in, and All are Hankel matrices of the biped robot system data representation.

4. The technology according to claim 3, characterized in that The prior knowledge of the noise is expressed as: in, Λ ij ,i,j=1,2 is a known matrix, Λ ij satisfy:

5. The technology according to claim 4, characterized in that The set of unknown system matrices is expressed as: in, Δ is the first matrix.

6. The technology according to claim 5, characterized in that The set of unknown system matrices processed by the dual lemma is expressed as: in, Θ is the second matrix.

7. The technology according to claim 6, characterized in that The gain observer is expressed as: in, is an estimate of x(t,k), L z ∈R n×s is the gain matrix, z represents the index of the gain observer; The augmented system is expressed as: Among them, the error 8. The technology according to claim 7, characterized in that The first stability criterion is expressed as: The first stability criterion is processed by using the S lemma to obtain the second stability criterion, which is expressed as: Among them, κ d >0, τ>0, γ, μ1, μ2, λ≥0, δ≥0, ρ>0, θ>0 are all unknown constants, R n×n matrix and R n×p Matrices F1 and F2 are both unknown matrices; Solving the second stability criterion to obtain the unknown matrices P1, P2, F1 and F2; The gain estimate is expressed as:

9. A data-driven switching gain estimation system for a bipedal race walking robot, characterized in that: The system comprises: A model building module, used to build a state space model of a model-based bipedal robot system using a hybrid system framework, and to build an equivalent system model of the bipedal robot system based on data according to the state space model; wherein both the space state model and the equivalent system model include unknown system matrices; A noise constraint module, used to construct a priori knowledge of noise in the bipedal race walking robot system; A first calculation module is used to obtain a set of unknown system matrices according to the equivalent system model and the prior knowledge of the noise; wherein the expression of the set of unknown system matrices includes a first matrix stripped of the unknown system matrix; A second calculation module is used to process the set of unknown system matrices by using the duality lemma to obtain a second matrix related to the first matrix; An observer construction module, used to construct a gain observer based on the state space model, and obtain an augmented system according to the gain observer and the equivalent system model; A stability design module, configured to construct a model-based first stability criterion of the biped robot system according to the augmented system; A third calculation module, configured to process the first stability criterion by using the S lemma and substitute the result into the second matrix to obtain a second stability criterion based on data; A gain estimation module is used to obtain a gain estimate according to the second stability criterion.

10. A bipedal race walking robot, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the data-driven switching gain estimation technology for a bipedal race walking robot according to any one of claims 1 to 8 are implemented.

Images