A Cooperative Control Method for an Adaptive External Thrust Exoskeleton and Safety Bracket

Abstract

This invention relates to the field of robot motion control, and more particularly to a collaborative control method for an exoskeleton and safety brace that adapts to external thrust. It represents the pressure center by decoupling the dynamic model with external force in the sagittal plane; it sets dynamic constraints by introducing a component η containing only the motion of the human and exoskeleton system in the z-direction; and it uses multiple segmented line segments to approximate nonlinear arcs to set kinematic constraints, thereby constructing a discrete-time linear model. Based on this, a linear MPC problem is constructed to optimize and obtain the optimal center of gravity trajectory and landing point position output for the human and exoskeleton system. Compared with existing technologies, this invention improves the collaborative control accuracy of the patient-exoskeleton-safety brace system.

Description

Technical Field

[0001] This invention relates to the field of robot motion control, and more particularly to a collaborative control method for an exoskeleton and a safety brace that adapts to external thrust. Background Technology

[0002] Paraplegic patients using lower limb exoskeletons for walking typically need crutches to maintain balance. However, for patients with weaker upper limbs, maintaining balance with crutches is extremely difficult. Therefore, a movable safety brace is usually used to help patients maintain balance while walking. Furthermore, because the safety brace is quite heavy, it often requires another person to push it. During this pushing process, excessive force can cause the patient to lean forward, while insufficient force may cause them to lean backward. Therefore, achieving coordinated motion control among the patient, exoskeleton, and safety brace, without knowing the appropriate pushing force, becomes a crucial issue.

[0003] To help patients with weak muscles maintain balance during gait training, a movable safety brace is essential for assistive devices and exoskeletons. In recent years, many safety braces have been used in research related to gait training. CPWalker, a robotic platform for the rehabilitation of children with cerebral palsy, allows free movement and integrates physical and cognitive interfaces into treatment, enabling children with cerebral palsy to walk independently in rehabilitation environments. Ai-robot is designed to help paraplegic patients walk safely and effectively, but its use requires learning under the guidance of experienced healthcare professionals. A mobile robotic balance assistant is a device that combines gait assistance robotics and electric wheelchair functionality; its instability detection algorithm can be evaluated by calculating its sensitivity and specificity in recognizing normal walking and simulating falls. However, these safety braces have not yet provided a satisfactory solution to the problem of human-machine collaborative movement.

[0004] Currently, a popular approach to solving the problem of cooperative motion control in robots is Model Predictive Control (MPC). MPC is typically applied to lower limb exoskeletons and humanoid robots to generate the motion trajectory and gait of the center of mass. For example, Iyer et al. used an MPC-based cooperative control method in a stair-climbing scenario to ensure the knee joint followed a trajectory generated by optimizing impedance model parameters; Wang et al. used MPC to generate online joint trajectories based on gait parameters such as stride length, swing duration, and walking speed; Jammeli et al. proposed a model predictive control framework for assistive and rehabilitative lower limb exoskeletons. However, these methods only consider situations where the human-robot motion state is known; they cannot predict and control the cooperative motion of the human and robot when unknown external forces exist. Summary of the Invention

[0005] The purpose of this invention is to provide a collaborative control method for an exoskeleton and safety brace that adapts to external thrust, thereby improving the collaborative control accuracy of the patient-exoskeleton-safety brace system.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A collaborative control method for an adaptive external thrust exoskeleton and safety brace, applicable to human-exoskeleton-safety brace systems, includes the following steps:

[0008] Step 1: Construct a spring-damped model of the human-exoskeleton-safety support system; Based on the spring-damped model, simplify the dynamic model of the human and exoskeleton system into an inverted spring pendulum model with horizontal external force in the XOZ plane;

[0009] Step 2: Set the dynamic and kinematic constraints of the inverted spring pendulum model to complete the linearization of the kinematic model of the human and exoskeleton system;

[0010] Step 3: Based on the assumption that the derivative of acceleration remains constant over a given time interval, establish a discrete-time linear model in the sagittal plane to represent the dynamic and kinematic constraints obtained in Step 2.

[0011] Step 4: Based on the desired velocity, center of mass height, center of pressure, and derivative of acceleration, construct the adaptive cooperative control task objective of the MPC function of the human and exoskeleton system;

[0012] Step 5: Using the discrete-time linear model obtained in Step 3, the solution to the MPC function is transformed into a linear MPC problem model; the current centroid position of the human and exoskeleton system and the current landing point position are input into the linear MPC problem to predict and optimize the acceleration derivative of the centroid motion of the human and exoskeleton system and the landing point position at the next moment, generating the centroid motion trajectory and landing point position of the human and exoskeleton system, and realizing the coordinated control of the human-exoskeleton-safety support system.

[0013] Furthermore, the implementation method of step 1 includes the following steps:

[0014] 1.1 The spring damping model of the human-exoskeleton-safety support system is established as follows:

[0015]

[0016] Where F represents the force generated by the spring-damped system, k k k represents the stiffness coefficient of a spring-damped system. c This represents the damping coefficient of the spring-damped system, and x represents the position of the center of mass of the human and exoskeleton system in the x-direction. x represents the velocity of the person and exoskeleton system in the x-direction. d This indicates the position of the safety bracket in the x-direction. This indicates the velocity of the safety bracket in the x-direction;

[0017] 1.2. Based on the spring-damped model, the dynamic model of the human and exoskeleton system is simplified to a spring-inverted pendulum model with horizontal external force in the XOZ plane (sagittal plane):

[0018]

[0019] in, The constraint range of the pressure center is represented by x, where x represents the position of the center of mass of the person and the exoskeleton system in the x-direction, and z represents the position of the center of mass of the person and the exoskeleton system in the z-direction. Let represent the acceleration of the center of mass of the person and exoskeleton system in the z-direction, g represent the acceleration due to gravity, and m represent the mass of the person and exoskeleton system. P represents the acceleration of the center of mass of the human and exoskeleton system in the x-direction. cop It indicates the location of the pressure center of the human body and exoskeleton system.

[0020] Furthermore, the implementation method of step 2 includes the following steps:

[0021] By introducing a component η, the motion of the human and exoskeleton system in the x and z directions is decoupled, so as to achieve the constraint of the spring inverted pendulum model in the x and z directions.

[0022] The dynamic constraints of the human and exoskeleton system in the x-direction are expressed as follows:

[0023]

[0024] Where x represents the position of the center of mass of the person and exoskeleton system in the x-direction; η represents the component containing only the motion of the person and exoskeleton system in the z-direction. The constraint range of η is: Where η represents the upper bound of the movement of the human and exoskeleton system in the z-direction. P represents the lower bound of the motion of the human and exoskeleton system in the z-direction; cop Indicates the position of the heel. Indicates the position of the toes;

[0025] The dynamic constraints of the human and exoskeleton system in the z-direction are expressed as follows:

[0026]

[0027] in, This represents the acceleration of the center of mass of the human and exoskeleton system in the z-direction, and g represents the acceleration due to gravity.

[0028] Kinematic constraints are implemented using multiple segmented line segments, and dynamic constraints are represented as follows:

[0029] A c (cp)≤b

[0030] Among them, A c The matrix represents the linear kinematic constraints, b represents the vector of linear kinematic constraints, c represents the position of the center of mass of the human and exoskeleton system, and p represents the position of the contact point between the human and exoskeleton system and the ground.

[0031] Furthermore, the implementation method of step 3 includes the following steps:

[0032] Based on the invariance of the derivative of acceleration per unit time, the motion state of the human and exoskeleton system is spatially discretized to obtain a discrete-time linear model representing the dynamic and kinematic constraints of the human and exoskeleton system. The discrete-time linear model of the human and exoskeleton system in the x-direction is as follows:

[0033] Discrete-time state at time step (k+1):

[0034]

[0035]

[0036]

[0037] Where t represents the discrete time period, x k This represents the position of the human and exoskeleton system at the k-th time step in the x-direction. This represents the velocity of the human and exoskeleton system at the k-th time step in the x-direction. This represents the acceleration of the human and exoskeleton system at the k-th time step in the x-direction;

[0038] A state matrix (position, velocity, acceleration) for N time steps:

[0039]

[0040] The matrix of acceleration derivatives for N time steps:

[0041]

[0042] The result obtained by performing N iterative calculations from the discrete-time state at the (k+1)th time step is as follows:

[0043]

[0044] Where A represents the state transition matrix and B represents the control input vector. This represents the control input from time k to time (k+N-1). Indicates the current state;

[0045] Discrete-time state coefficient matrix P ps , P pu , P vs , P vu , P as and P au All of these are calculated from the matrix of acceleration derivatives for N time steps over N iterations;

[0046] The discrete-time linear model of the human and exoskeleton system in the z-direction is represented in the same form as the discrete-time linear model of the human and exoskeleton system in the x-direction.

[0047] Furthermore, the implementation method of step 4 includes the following steps:

[0048] Based on the desired velocity, center of mass height, center of pressure, and derivative of acceleration, the adaptive cooperative control task objective of the MPC function for the human and exoskeleton system is constructed as follows:

[0049]

[0050]

[0051] d3=||zh ref || 2

[0052]

[0053] in, It is the desired walking speed, h ref The objective is to control the center of mass height under supported conditions. It is the derivative of the acceleration of the center of mass of the human and exoskeleton system in the x-direction. It is the acceleration derivative of the center of mass of the human and exoskeleton system in the z-direction; d1 represents the velocity control task objective, d2 represents the pressure center control task objective, and d3 represents the center of mass height control task objective.

[0054] Furthermore, the implementation method of step 5 includes the following steps:

[0055] The acceleration derivatives of the center of mass of the human and exoskeleton system in the x and z directions, as well as the position of the footing point, are selected as decision variables.

[0056] The dynamic and kinematic constraints obtained in step 2 are considered as inequality constraints in a linear MPC problem. For the objective functions d1, d2, d3, and d4 of the linear MPC problem, corresponding weights α1, α2, α3, and α4 are assigned. The control period and the prediction time step N of the MPC problem are set according to the application scenario. The resulting linear MPC framework is:

[0057]

[0058] Using the discrete-time linear model obtained in step 3, the MPC framework is transformed into the standard form of a quadratic programming solver. The transformation from the linear MPC framework to the standard quadratic programming solver form is expressed as:

[0059]

[0060] in, and All include the overall dynamic constraints and kinematic constraints; decision variables This includes the acceleration derivatives in the x and z directions at N time steps, as well as the location of the landing point; Q represents the Hessian matrix of the quadratic programming solver. The original Hessian matrix is ​​represented as:

[0061]

[0062] in, Let Q0 be the zero matrix. The components of the original Hessian matrix Q0 are represented as follows:

[0063]

[0064] R 13 =-α3(P pu -λP au ) T

[0065]

[0066] R 31 =-α3(P pu -λP au )

[0067] R 33 =α3I

[0068] in, It is the identity matrix;

[0069] In the MPC framework, the gradient vector of the original objective function is represented as:

[0070] f = [f1, f2, f3] T

[0071] The components of the gradient vector f are represented as follows:

[0072]

[0073] It is a vector consisting entirely of 1s, and

[0074] The current center of mass position and landing point position of the human and exoskeleton system are input into the standard form of the quadratic programming solver for optimization. This predicts and optimizes the acceleration derivative of the center of mass motion of the human and exoskeleton system and the landing point position at the next moment. Based on this, the trajectory of the center of mass motion and the landing point position of the human and exoskeleton system are generated, realizing the coordinated control of the human-exoskeleton-safety support system.

[0075] This invention provides a collaborative control method for an adaptive external thrust exoskeleton and safety support system. It represents the pressure center by decoupling the dynamic model with external force in the sagittal plane; it sets dynamic constraints by introducing a component η containing only the motion of the human and exoskeleton system in the z-direction; and it uses multiple segmented line segments to approximate nonlinear arcs to set kinematic constraints, thereby constructing a discrete-time linear model. Based on this, a linear MPC problem is constructed to optimize and obtain the optimal center of gravity trajectory and landing point position output for the human and exoskeleton system. Compared with existing technologies, this invention improves the collaborative control accuracy of the patient-exoskeleton-safety support system. Attached Figure Description

[0076] The invention will now be further described with reference to the accompanying drawings, in which:

[0077] Figure 1 This is a diagram of the overall framework of the algorithm proposed in this invention;

[0078] Figure 2 This is a schematic diagram of the human-exoskeleton-safety support system of the present invention;

[0079] Figure 3 This is a simplified schematic diagram of the dynamic model of the present invention;

[0080] Figure 4 This is a schematic diagram of the parameter linearization introduced in this invention;

[0081] Figure 5 This is a simplified schematic diagram of the kinematic model of the present invention. Detailed Implementation

[0082] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0083] like Figure 1 As shown, this embodiment provides a collaborative control method for an adaptive external thrust exoskeleton and a safety brace, comprising the following steps:

[0084] Step 1: Construct a spring-damped model of the human-exoskeleton-safety support system. Based on the spring-damped model, simplify the dynamic model of the human and exoskeleton system into an inverted pendulum model with horizontal external force in the XOZ plane. The implementation method is as follows:

[0085] 1.1 Constructing a spring-damped model of the human-exoskeleton-safety support system:

[0086] like Figure 2 In the human-exoskeleton-safety frame system, medical staff push the safety frame from behind, while the patient wears the exoskeleton to complete gait training. The four wheels of the safety frame move passively, and the vertical support joints support the weight of the human and exoskeleton system. Connecting the safety frame and the exoskeleton is a spring-damped system that can extend and retract to adjust the interaction forces and relative positions. Throughout the system, on the one hand, the force generated by the spring-damped system is affected by the movement of the human, exoskeleton system, and safety frame; on the other hand, because the spring-damped system always moves horizontally, the force it generates is always along the x-axis. The spring-damped model is as follows:

[0087]

[0088] In equation (1), F is the force generated by the spring-damped system, and k k k is the stiffness coefficient of the spring-damped system. c is the damping coefficient of the spring-damped system, and x is the position of the person and the exoskeleton system in the x-direction. It is the velocity of the human and exoskeleton system in the x-direction, x d It refers to the position of the safety bracket in the x-direction. These are the speeds of the safety bracket in the x-direction.

[0089] 1.2 Based on the spring-damped model, the dynamic model of the human and exoskeleton system is simplified to a spring-inverted pendulum model with horizontal external force in the XOZ plane:

[0090] In this embodiment, the simplified structure of the dynamic model of the human and exoskeleton system is as follows: Figure 3 As shown, for the human and exoskeleton system, the dynamic equation of its center of mass can be expressed as:

[0091]

[0092] In equations (2) and (3), m is the mass of the human and exoskeleton system, and c is the position of the center of mass of the human and exoskeleton system. It is the gravitational acceleration vector. It is the ground reaction force, L is the angular momentum of the person and the exoskeleton system, and p is the position of the point of contact with the ground.

[0093] Based on equations (2) and (3), the Newton equation for the external torque divided by the z-direction is expressed as:

[0094]

[0095] The superscript z denotes the component in the z-direction, and G z It is g, which is usually taken as 9.81 m / s.

[0096] Decomposing equation (4) in the sagittal plane yields the following expression:

[0097]

[0098] In equation (5), the superscripts x and z represent the components in the x-direction and z-direction, respectively; Indicates the center of pressure.

[0099] Considering that the landing point is always on the ground (only considering motion on flat ground), therefore we have p z =0. Therefore, equation (5) can be rewritten as:

[0100]

[0101] In equation (6), P cop Indicates the location of the pressure center of the human body and exoskeleton system; It indicates the constraint range of the pressure center, used to ensure the stability of human and exoskeleton system movement.

[0102] Thus, a spring-loaded inverted pendulum model of a human and exoskeleton system with horizontal external force in the XOZ plane was obtained.

[0103] Step 2: Establish the dynamic constraint model and kinematic constraint model of the inverted spring pendulum model to complete the linearization of the kinematic model of the human and exoskeleton system;

[0104] Dynamic constraints are established by introducing the parameter η:

[0105] As shown in Equation 6, for the inverted pendulum model constructed from a human and exoskeleton system with a spring-damped system, it is necessary to ensure that its center of pressure is within the range that satisfies the constraints; and the dynamic model of the human and exoskeleton system exhibits linearity in the x-direction and nonlinearity in the z-direction. Based on this, this embodiment defines a component η to ensure linearity in the z-direction. Component η only includes the motion of the human and exoskeleton system in the z-direction. η is defined as follows:

[0106] Since the condition for the human and exoskeleton system to satisfy the stability constraint is that the center of pressure must move within the range of the foot's edge; according to equation (6), the change of η can reasonably restrict the movement of the human and exoskeleton system in the z-direction to ensure that the center of pressure satisfies the foot's edge. Therefore, the constraint range of η can be expressed as follows: Where η and The upper and lower bounds are represented respectively; their linearization constraints within the foot edge range are as follows: Figure 4 As shown.

[0107] The dynamic constraints of the human and exoskeleton system in the x-direction can be expressed as:

[0108]

[0109] Among them, {P cop} represents the set of pressure center locations.

[0110] In equation (7), the center of pressure cannot be directly determined. However, since the motion occurs in the sagittal plane, the position P of the heel can be determined. cop and the position of the toes And the center of pressure is constrained within it. Equation (13) can thus be rewritten as:

[0111]

[0112] In addition, it can be noted that the item It is always positive. Therefore, the dynamic constraint of the human and exoskeleton system in the z-direction can be expressed as:

[0113]

[0114] Equations (8) and (9) are the dynamic constraint models of the human and exoskeleton system. In this embodiment, the dynamic constraints in the x and z directions are successfully decoupled by introducing the parameter η. At the same time, the linearization of η also ensures that the motion of the human and exoskeleton system in the z direction exhibits linear characteristics. Based on the above, the linearization of the dynamic constraints of the human and exoskeleton system is finally completed.

[0115] Set kinematic constraints:

[0116] Data collected from normal human walking shows that when one leg is in a supporting position and the other leg is in a swinging position, the motion of the center of mass of the human and exoskeleton system in the sagittal plane approximates a circular arc. The kinematic constraints of the human and exoskeleton system can be expressed as {l|cp} a}≤L0, where {l|cp a} represents the distance p from the center of mass c of the exoskeleton system to the ankle joint. a L0 is the length of the legs in a person and exoskeleton system.

[0117] To linearize the kinematic constraints of the human and exoskeleton system, multiple piecewise line segments are used to approximate the nonlinear arc. In practical applications, a higher number of piecewise line segments approximates the actual constraints, but this also increases the computational burden on subsequent MPC calculations. The linear kinematic constraints of the human and exoskeleton system can be expressed as:

[0118] A c (cp)≤b, (10)

[0119] Among them, A c b and b are the matrix and vector of the linear kinematic constraints, respectively.

[0120] To ensure that the center of mass of the human and exoskeleton system reaches its maximum height when upright, this embodiment simplifies the kinematic model of the human and exoskeleton system by confining its center of mass within a range of five segmented line segments, thereby linearizing its kinematic model. The segmented representation of the kinematic constraints in this embodiment is as follows: Figure 5 As shown, Figure 5 In the diagram, l1, l2, l3, l4, and l5 are five segmented lines. One of these segmented lines is set to the lowest position of the centroid, and the remaining segmented lines are set to an even number.

[0121] Step 3: Based on the assumption that the derivative of acceleration remains constant over an extremely short time interval, establish a discrete-time linear model in the sagittal plane to represent the dynamic and kinematic constraints:

[0122] Assuming the acceleration (derivative of acceleration) of the center of mass remains constant over a given time period, this time can be considered a discretized time period. If we use... This represents the state of the human and exoskeleton system at the k-th time step in the x-direction. The derivative of acceleration represents the discrete-time state at the (k+1)th time step. It can be represented as:

[0123]

[0124] In equation (11), t represents the discrete time period, x kThis represents the position of the human and exoskeleton system at the k-th time step in the x-direction. This represents the velocity of the human and exoskeleton system at the k-th time step in the x-direction. This represents the acceleration of the human and exoskeleton system at the k-th time step in the x-direction.

[0125] The state matrix (position, velocity, acceleration) for N time steps is represented as follows:

[0126]

[0127] The matrix representation of the acceleration derivatives over N time steps is as follows:

[0128]

[0129] The result of N-step iterative calculation using equation (11) can be expressed as:

[0130]

[0131] Discrete-time state coefficient matrix P ps , P pu , P vs , P vu , P as and P au The result is obtained by iterating N steps using equation (16). Similarly, the motion of the human and exoskeleton system in the z direction can also be represented by the same form of equations (11)-(16).

[0132] Step 4: Construct the adaptive cooperative control task objective of the MPC function for the human and exoskeleton system, and set the desired velocity, center of mass height, center of pressure, and derivative of acceleration:

[0133] In the MPC framework, the acceleration derivative of the centroid in the horizontal direction is used. and the derivative of acceleration in the vertical direction And the landing point position p is used as a decision variable. Assuming the person assisting from behind applies a variable-range force to propel the safety frame, the goal is for the person and exoskeleton system to move at a set speed, adjustable within a variable range. The task objective can be expressed as:

[0134]

[0135] in, That is the desired walking speed.

[0136] By utilizing dynamic constraints, the position of the pressure center is ensured to remain within the edge of the supporting foot. During the movement of the human-exoskeleton-safety frame, the position of the pressure center of the human and exoskeleton system is as close as possible to the middle of the supporting foot edge to maintain stability. This task objective can be expressed as:

[0137]

[0138] Considering that the exoskeleton is connected to the human body, the task objective of making its walking movements more human-like can be represented as:

[0139] d3=||zh ref || 2 (19)

[0140] Among them, h ref It refers to the height of the center of mass in the supported state; in order to make the exoskeleton in a human upright walking state rather than a robot kneeling walking state, the height of the center of mass movement should be as high as possible.

[0141] Since the acceleration derivatives of the center of mass of the human and exoskeleton system in the x and z directions are decision variables of the MPC function, minimizing these derivatives is required to ensure smooth motion and prevent abrupt changes. This task objective can be expressed as:

[0142]

[0143] in, It is the derivative of the acceleration of the center of mass of the human and exoskeleton system in the x-direction. It is the acceleration derivative of the center of mass of the human and exoskeleton system in the z-direction.

[0144] The above d1, d2, d3 and d4 are the task objectives of the adaptive cooperative control of the MPC function.

[0145] Step 5: Establish a linear MPC problem, predict and optimize the derivative of the center of mass acceleration and the landing point position, and generate the center of mass trajectory and landing point position of the human and exoskeleton system:

[0146] To ensure coordinated movement between the human, the exoskeleton system, and the safety brace, adaptive coordinated control target weights need to be set. Based on this, using the linearized dynamic formulas (8) and (9) and the kinematic constraint formula (10), the original Model Predictive Control (MPC) problem can be transformed into a convex problem (a convex problem has a unique optimal solution). Specifically:

[0147] The acceleration derivatives of the center of mass of the human and exoskeleton system in the x and z directions, as well as the position of the landing point, are selected as decision variables. In this embodiment, formulas (8) and (9) representing the dynamic constraint model and formula (10) representing the kinematic constraint model are regarded as inequality constraints in the linear MPC problem. At the same time, for the objective functions d1, d2, d3, and d4 of the linear MPC problem, corresponding weights α1, α2, α3, and α4 are assigned. The control period is set to 0.01s, and the prediction time step N of the MPC problem is 15. The specific linear MPC framework can be expressed as:

[0148]

[0149] The linear MPC framework in equation (21) is transformed into the standard form of a quadratic programming solver by using the discrete-time state-space equations in equations (11)-(15).

[0150]

[0151] in, and It includes the entire dynamic constraints and kinematic constraints; decision variables It includes the acceleration derivatives in the x and z directions for N time steps, as well as the location of the landing point.

[0152] The Hessian matrix Q input to the quadratic programming solver is represented as follows: The original Hessian matrix Represented as:

[0153]

[0154] in, Let Q0 be the zero matrix. The components of the original Hessian matrix Q0 are represented as follows:

[0155]

[0156] R 13 =-α3(P pu -λP au ) T (25)

[0157]

[0158] R 31 =-α3(P pu -λP au ), (27)

[0159] R 33 =α3I, (28)

[0160] in, It is an identity matrix.

[0161] In the MPC framework, the gradient vector of the original objective function is represented as:

[0162] f = [f1, f2, f3] T (29)

[0163] The components of the gradient vector f are represented as follows:

[0164]

[0165]

[0166]

[0167] in, It is a vector consisting entirely of 1s, and

[0168] The matrix Q is processed to ensure that the Hessian matrix in the quadratic programming problem is positive semi-definite. The first step of obtaining each prediction result is achieved using the optimal decision variables. The first step of each prediction result can be obtained by Equation (11) to calculate the state at the next time step in MPC.

[0169] The moment the human and exoskeleton system reaches its highest point is selected as the gait transition moment, and the moment it reaches its lowest point is selected as the foot contact moment, using a standard quadratic programming solver. Furthermore, PVT (Position-Velocity-Time) curve interpolation is employed to plan the foot's trajectory during each swing phase. The joint motion trajectories of the human and exoskeleton system during movement are obtained using pseudo-inverse matrices and inverse kinematics. Joint control is achieved by controlling the joint position through PID parameters.

[0170] At this point, the coordinated movement trajectory and landing point of the human and exoskeleton system can be generated. Therefore, this invention can generate coordinated movement of the human-exoskeleton-safety support under the action of external human force.

Claims

1. A method for coordinated control of an exoskeleton and safety brace that adapts to external thrust, applicable to a human-exoskeleton-safety brace system, characterized in that: The method includes the following steps: Step 1: Construct a spring-damped model of the human-exoskeleton-safety support system; Based on the spring-damped model, simplify the dynamic model of the human and exoskeleton system into an inverted spring pendulum model with horizontal external force in the XOZ plane; Step 2: Set the dynamic and kinematic constraints of the inverted spring pendulum model to complete the linearization of the kinematic model of the human and exoskeleton system; Step 3: Based on the assumption that the derivative of acceleration remains constant over a given time interval, establish a discrete-time linear model in the sagittal plane to represent the dynamic and kinematic constraints obtained in Step 2. Step 4: Based on the desired velocity, center of mass height, center of pressure, and derivative of acceleration, construct the adaptive cooperative control task objective of the MPC function of the human and exoskeleton system; Step 5: Using the discrete-time linear model obtained in Step 3, the solution to the MPC function is transformed into a linear MPC problem model; the current centroid position of the human and exoskeleton system and the current landing point position are input into the linear MPC problem to predict and optimize the acceleration derivative of the centroid motion of the human and exoskeleton system and the landing point position at the next moment, generating the centroid motion trajectory and landing point position of the human and exoskeleton system, and realizing the coordinated control of the human-exoskeleton-safety support system.

2. The method for coordinated control of an adaptive external thrust exoskeleton and a safety brace according to claim 1, characterized in that: The implementation method of step 1 includes the following steps: 1.1 The spring damping model of the human-exoskeleton-safety support system is established as follows: Where F represents the force generated by the spring-damped system, k k k represents the stiffness coefficient of a spring-damped system. c This represents the damping coefficient of the spring-damped system, and x represents the position of the person and the exoskeleton system in the x-direction. x represents the velocity of the person and exoskeleton system in the x-direction. d This indicates the position of the safety bracket in the x-direction. This indicates the velocity of the safety bracket in the x-direction; 1.2 Based on the spring-damped model, the dynamic model of the human and exoskeleton system is simplified to a spring-inverted pendulum model with a horizontal external force in the sagittal plane: in, The constraint range of the pressure center is represented by x, where x represents the position of the center of mass of the person and the exoskeleton system in the x-direction, and z represents the position of the center of mass of the person and the exoskeleton system in the z-direction. Let represent the acceleration of the center of mass of the person and exoskeleton system in the z-direction, g represent the acceleration due to gravity, and m represent the mass of the person and exoskeleton system. P represents the acceleration of the center of mass of the human and exoskeleton system in the x-direction. cop It indicates the location of the pressure center of the human body and exoskeleton system.

3. A method for coordinated control of an adaptive external thrust exoskeleton and a safety brace according to claim 2, characterized in that: The implementation method of step 2 includes the following steps: By introducing a component η, the motion of the human and exoskeleton system in the x and z directions is decoupled, so as to achieve the constraint of the spring inverted pendulum model in the x and z directions. The dynamic constraints of the human and exoskeleton system in the x-direction are expressed as follows: Where x represents the position of the center of mass of the person and exoskeleton system in the x-direction; η represents the component containing only the motion of the person and exoskeleton system in the z-direction. The constraint range of η is: Where η represents the upper bound of the movement of the human and exoskeleton system in the z-direction. This represents the lower bound of the motion of the human and exoskeleton system in the z-direction; P cop Indicates the position of the heel. Indicates the position of the toes; The dynamic constraints of the human and exoskeleton system in the z-direction are expressed as follows: in, This represents the acceleration of the center of mass of the human and exoskeleton system in the z-direction, and g represents the acceleration due to gravity. Kinematic constraints are implemented using multiple segmented line segments, and dynamic constraints are represented as follows: A c (c-p)≤b Among them, A c The matrix represents the linear kinematic constraints, b represents the vector of linear kinematic constraints, c represents the position of the center of mass of the human and exoskeleton system, and p represents the position of the contact point between the human and exoskeleton system and the ground.

4. A method for coordinated control of an adaptive external thrust exoskeleton and a safety brace according to claim 3, characterized in that: The implementation method of step 3 includes the following steps: Based on the invariance of the derivative of acceleration per unit time, the motion state of the human and exoskeleton system is spatially discretized to obtain a discrete-time linear model representing the dynamic and kinematic constraints of the human and exoskeleton system. The discrete-time linear model of the human and exoskeleton system in the x-direction is as follows: Discrete-time state at time step (k+1): Where t represents the discrete time period, x k This represents the position of the human and exoskeleton system at the k-th time step in the x-direction. This represents the velocity of the human and exoskeleton system at the k-th time step in the x-direction. This represents the acceleration of the human and exoskeleton system at the k-th time step in the x-direction; A state matrix (position, velocity, acceleration) for N time steps: The matrix of acceleration derivatives for N time steps: The result obtained by performing N iterative calculations from the discrete-time state at the (k+1)th time step is as follows: Where A represents the state transition matrix and B represents the control input vector. This represents the control input from time k to time (k+N-1). Indicates the current state; Discrete-time state coefficient matrix P ps , P pu , P vs , P vu , P as and P au All of these are calculated from the matrix of acceleration derivatives for N time steps over N iterations; The discrete-time linear model of the human and exoskeleton system in the z-direction is represented in the same form as the discrete-time linear model of the human and exoskeleton system in the x-direction.

5. A method for coordinated control of an adaptive external thrust exoskeleton and a safety brace according to claim 4, characterized in that: The implementation method of step 4 includes the following steps: Based on the desired velocity, center of mass height, center of pressure, and derivative of acceleration, the adaptive cooperative control task objective of the MPC function for the human and exoskeleton system is constructed as follows: d3=||zh ref || 2 in, It is the desired walking speed, h ref The objective is to control the center of mass height under supported conditions. It is the derivative of the acceleration of the center of mass of the human and exoskeleton system in the x-direction. It is the acceleration derivative of the center of mass of the human and exoskeleton system in the z-direction; d1 represents the velocity control task objective, d2 represents the pressure center control task objective, and d3 represents the center of mass height control task objective.

6. A method for coordinated control of an adaptive external thrust exoskeleton and a safety brace according to claim 5, characterized in that: The implementation method of step 5 includes the following steps: The acceleration derivatives of the center of mass of the human and exoskeleton system in the x and z directions, as well as the position of the footing point, are selected as decision variables. The dynamic and kinematic constraints obtained in step 2 are treated as inequality constraints in a linear MPC problem; for the objective functions d1, d2, d3, and d4 of the linear MPC problem, corresponding weights α1, α2, α3, and α4 are assigned; the control period and the prediction time step N of the MPC problem are set according to the application scenario. The resulting linear MPC framework is as follows: Using the discrete-time linear model obtained in step 3, the MPC framework is transformed into the standard form of a quadratic programming solver. The transformation from the linear MPC framework to the standard quadratic programming solver form is expressed as: s.t.A qp u k ≤b qp in, and All include the overall dynamic constraints and kinematic constraints; decision variables This includes the acceleration derivatives in the x and z directions at N time steps, as well as the location of the landing point; Q represents the Hessian matrix of the quadratic programming solver. The original Hessian matrix is ​​represented as: in, Let Q0 be the zero matrix. The components of the original Hessian matrix Q0 are represented as follows: R 13 =-α3(P pu -λP au ) T R 31 =-α3(P pu -λP au ) R 33 =α3I in, It is the identity matrix; In the MPC framework, the gradient vector of the original objective function is represented as: f=[f1,f2,f3] T The components of the gradient vector f are represented as follows: It is a vector consisting entirely of 1s, and The current center of mass position and landing point position of the human and exoskeleton system are input into the standard form of the quadratic programming solver for optimization. This predicts and optimizes the acceleration derivative of the center of mass motion of the human and exoskeleton system and the landing point position at the next moment. Based on this, the trajectory of the center of mass motion and the landing point position of the human and exoskeleton system are generated, realizing the coordinated control of the human-exoskeleton-safety support system.

Images