An optimal constraint following control method for lower limb rehabilitation exoskeleton robot considering human-robot interaction

Abstract

The present invention discloses an optimal constraint following control method for a lower limb rehabilitation exoskeleton robot considering human-machine interaction, which relates to the field of medical rehabilitation assistive devices and includes the following steps: (4) obtaining the boundaries and functions of uncertainty parameters; (5) establishing a constraint following control scheme, which consists of a preemptive algorithm part and a manual decision part; the machine solves the control quantity P1 of the preemptive algorithm part, and solves the optimal membership function and calculates it to obtain the control quantity P2 of the manual decision algorithm part; (6) combining the preemptive algorithm with the optimal manual decision to obtain the control quantity of the robust control strategy, thereby achieving robust and optimal system performance. The present invention combines the preemptive machine algorithm with the optimal manual decision algorithm, which can fully utilize the rapid response capability of the machine and the advanced decision-making capability of humans, thereby improving the overall performance of the system and user experience, and completing more efficient, safer, more flexible and more humane human-machine interaction to achieve robust and optimal system performance.

Description

Technical Field

[0001] The present invention relates to the field of medical rehabilitation assistive devices, and in particular to an optimal constraint following control method for a lower limb rehabilitation exoskeleton robot taking human-machine interaction into consideration. Background Art

[0002] With the growing elderly population, stroke has become a common condition, often accompanied by lower limb motor dysfunction and even paralysis. This condition undoubtedly severely disrupts patients' daily lives. In the past, rehabilitation training for impaired lower limbs relied primarily on manual rehabilitation by physical therapists. However, this labor-intensive process required continuous input from rehabilitation staff, making the rehabilitation process increasingly demanding for physical therapists. To address this issue, lower limb rehabilitation exoskeleton robots have emerged in recent years. These robots have greatly reduced the burden on rehabilitation staff and significantly improved the efficiency of lower limb rehabilitation training over the past two decades.

[0003] The rehabilitation training process is divided into three stages based on the severity of the patient's impairment: primary, intermediate, and advanced. In the primary stage, given the physical limitations of stroke patients, they are typically required to strictly follow pre-defined gait trajectories, often based on clinical gait analysis results from healthy individuals. As patients gradually recover their lower limb motor abilities, training in the intermediate stage will place greater emphasis on human-machine interaction, striving to minimize interaction forces. In the advanced stage, control algorithms must be able to accurately capture the patient's movement intentions and provide necessary auxiliary control accordingly.

[0004] Of these three stages, the intermediate stage is a critical period of transition and adaptation, playing a crucial role in the rehabilitation process. However, research on human-computer interaction is still focused on breaking through key technical bottlenecks: accurate recognition of interaction intent and optimization of interaction paths. Therefore, to achieve human-computer collaboration and high-quality rehabilitation assistance, personalized and intelligent control methods will be the focus of future research.

[0005] Constrained following control is a strategy designed to ensure that a system can still achieve or approach the desired control target when subject to various constraints. It is very suitable for systems that need to comply with strict physical or operational limitations during operation.

[0006] We are committed to considering human-robot interaction in the design of constraint-following control for lower-limb rehabilitation exoskeleton robots. The machine needs to follow a set of desired constraints, which can be both holistic and non-holistic. The system contains uncertainty, which can be rapidly time-varying. The uncertainty is bounded, but the possible bounds may be unknown. Therefore, the human operator needs to contribute to the decision-making that improves the system's performance. Summary of the Invention

[0007] In view of the shortcomings of the existing technology, the present invention provides the following technical solutions: an optimal constraint following control method for a lower limb rehabilitation exoskeleton robot considering human-machine interaction, comprising the following steps:

[0008] (1) Establish a dynamic model for the lower limb rehabilitation exoskeleton robot system;

[0009] (2) Establish performance constraints based on the performance indicators that the exoskeleton robot needs to meet;

[0010] (3) Decomposition of inertia matrix and input matrix;

[0011] (4) Obtaining the bounds and functions of uncertainty parameters;

[0012] (5) Establish a constraint-following control scheme, which consists of a preemptive algorithm part and a manual decision-making part; the machine solves the control quantity P1 of the preemptive algorithm part, and solves the optimal membership function and calculates it to obtain the control quantity P2 of the manual decision-making algorithm part;

[0013] (6) Combining the preemptive algorithm with the optimal manual decision-making, a robust control strategy is obtained to control the quantity and achieve robust and optimal system performance.

[0014] As an improvement of the above technical solution, a kinetic model is established in step (1):

[0015]

[0016] in, represents the time in the lower limb rehabilitation exoskeleton robot, represents the coordinates in the lower limb rehabilitation exoskeleton robot, represents the speed in the lower limb rehabilitation exoskeleton robot, represents the acceleration in the lower limb rehabilitation exoskeleton robot, represents the uncertain parameters in the lower limb rehabilitation exoskeleton robot, represents the unknown environmental disturbance in the lower limb rehabilitation exoskeleton robot, Represents the control quantity of artificial independent decision-making in the lower limb rehabilitation exoskeleton robot, the set Σ and Υ e Denote σ and v respectively e The possible areas of the lower limb rehabilitation exoskeleton robot are all compact sets. M(q,σ,t) is the inertia matrix. represents the Coriolis force / centrifugal force, g(q,σ,t) represents the gravitational force, Represents the input matrix, matrix / vector M(q,σ,t), g(q,σ,t), All have appropriate dimensions, M(·), C(·), g(·), Be (·) are all continuous;

[0017] The performance constraints are expressed as follows:

[0018]

[0019] in, yes The i-th component of li (·) and c l (·) is the first-order form of the constraint condition. Each constraint condition can be a global constraint condition or a non-global constraint condition. The above first-order performance constraint condition is expressed in matrix form:

[0020]

[0021] Where Λ=[Λ li ] m×n ,c=[c1,c2,…,c m ] T represents the first-order performance constraint in matrix form,

[0022] Derivative the first-order performance constraint with respect to t to obtain the second-order performance constraint:

[0023]

[0024] Among them, the first-order constraint matrix Λ li (q, t) and the first-order constraint matrix c l The derivatives of (q,t) are:

[0025]

[0026] The obtained second-order performance constraints are transposed and rewritten as:

[0027]

[0028] Where l = 1,…,m, the second-order performance constraint is written in matrix form:

[0029]

[0030] The above formula can express the integration of global constraints and non-global constraints, where the matrix Represents the right-hand side matrix of the rewritten second-order performance constraint equation.

[0031] As an improvement of the above technical solution, in step (3), the inertia matrix is decomposed;

[0032] The nominal part And function Continuous, for the convenience of expression, the following definitions can be made for the combination of several inertia matrices and their nominal parts:

[0033]

[0034] E(q,σ,t):=M(q,t)M -1 (q,σ,t)-I;

[0035] From the above definitions, we can get the equation relationship between D(q,t), ΔD(q,σ,t) and E(q,σ,t):

[0036] ΔD(q,σ,t)=D(q,t)E(q,σ,t);

[0037] Then decompose the input matrix as follows:

[0038]

[0039] in, and They represent the two parts of the decomposition of the input matrix, which are specifically expressed as follows:

[0040]

[0041] Defining the constrained following error based on the first-order performance constraint

[0042]

[0043] Constraint-based tracking error Define the preemptive algorithm partial control scheme

[0044] in, It represents the control quantity obtained by the preemptive algorithm part after calculation, δ>0, which represents the control quantity coefficient of the preemptive algorithm part selected based on machine calculation;

[0045] Constraint-based tracking error Define the control scheme for the manual decision part

[0046] in, Indicates the control quantity obtained by the manual decision-making part through calculation, γ>0 indicates the control quantity coefficient based on the manual decision selection, The norm of the structure representing the uncertainty bound, The structure representing the uncertainty bound is uniquely determined by:

[0047]

[0048] in, represents the nominal part of the inertia matrix, Λ(q,t) represents the performance constraint in matrix form, δ, γ>0, and represent the scalar design parameters based on the preemptive algorithm and the manual decision algorithm, respectively;

[0049] Based on the definition of preemptive algorithm and manual decision control scheme, the robust constraint following control scheme τ(t) is established as follows:

[0050]

[0051] Among them, p1 is an established preemptive algorithm, which is directly implemented by a computer, while the selection of γ in p2 is based on manual decision-making.

[0052] As an improvement to the above technical solution, the step (4) is to determine the cost function;

[0053] Regarding system cost, based on the robust constrained following control scheme τ(t), considering both machine calculation and human decision-making, the following equation holds:

[0054]

[0055] Where d represents the differential operator and ds represents the global minimum solution of the cost function The arc length relative to ξ, where ξ represents the biological signal generated by humans as input to decision making;

[0056] Taking the square root of both sides of the above equation yields:

[0057]

[0058] The physical meaning of ds is the robustness of the uniformly bounded region and the uniformly limited bounded region to the change of ξ. The smaller ds is, the more robust the system performance is. In other words, the uniformly bounded region and the uniformly limited bounded region are less sensitive to the change of ξ.

[0059] Based on the above considerations, the cost function can be obtained as follows:

[0060]

[0061] Considering the above cost function, the global minimum solution of the cost function is satisfy:

[0062]

[0063] Among them, W and Y are A constant that satisfies the following two boundary conditions:

[0064]

[0065] Based on the above conditions, the cost function can be rewritten as follows:

[0066]

[0067] The corresponding membership function can be uniquely determined as follows, for all ξ∈[ξ1,ξ2]:

[0068]

[0069] Among them, ξ1 and ξ2 represent the left and right boundaries of the membership function domain, respectively. represents the center point of the membership function support [ξ1,ξ2], W and Y are respectively The constants that satisfy the boundary conditions, γ denote the upper and lower bounds of γ, respectively. Represents the global minimum solution The coefficient of

[0070] The uniqueness of the membership function also indicates the uniqueness of human optimal behavior.

[0071] As an improvement to the above technical solution, based on the determination of the membership function, the optimal manual decision in step (6) is selected as follows:

[0072]

[0073] in, represents the upper bound of γ, μ(ξ) represents the membership function determined based on the cost function;

[0074] Therefore, the robust control strategy τ(t) applied to the lower limb rehabilitation exoskeleton robot system is as follows:

[0075]

[0076] Among them, τ(t) represents the control quantity of the robust control strategy, It represents the control quantity obtained by the preemptive algorithm part. It represents the control quantity obtained by the calculation of the manual decision-making part. represents the time in the lower limb rehabilitation exoskeleton robot, represents the coordinates in the lower limb rehabilitation exoskeleton robot, Represents the speed in the lower limb rehabilitation exoskeleton robot. The preemptive algorithm part p1 is directly implemented by the computer, while the selection of γ in the manual decision part p2 is based on manual decision.

[0077] The beneficial effects of the present invention are as follows: for the control method, the framework only utilizes the structural boundary information of uncertainty without knowing the exact value of the boundary, so as to ensure robust consistent boundedness and consistent final boundedness performance; secondly, for human behavior, the framework promotes the optimal decision-making result by proposing a fuzzy membership function; this function is expressed in analytical form rather than numerical form; combining the preemptive machine algorithm with the optimal human decision-making algorithm can make full use of the machine's rapid response capability and the human's advanced decision-making ability, thereby improving the overall performance of the system and user experience, and completing more efficient, safer, more flexible and more humane human-computer interaction to achieve robust and optimal system performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 It is a schematic diagram of the process of the present invention:

[0079] Figure 2 This is a schematic diagram of the overall structure of the lower limb rehabilitation exoskeleton machine system of the present invention. DETAILED DESCRIPTION

[0080] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0081] See also Figure 1-2 The present invention provides a technical solution: an optimal constraint following control method for a lower limb rehabilitation exoskeleton robot considering human-machine interaction, comprising the following steps:

[0082] (1) Establish a dynamic model for the lower limb rehabilitation exoskeleton robot system;

[0083] (2) Establish performance constraints based on the performance indicators that the exoskeleton robot needs to meet;

[0084] (3) Decomposition of inertia matrix and input matrix;

[0085] (4) Obtaining the bounds and functions of uncertainty parameters;

[0086] (5) Establish a constraint-following control scheme, which consists of a preemptive algorithm part and a manual decision-making part. The manual decision-making part is transformed into a fuzzy-based optimal problem and solved through calculus variations to obtain an analytical expression of the optimal membership function. The machine solves the control quantity P1 of the preemptive algorithm part, and the optimal membership function is solved and calculated to obtain the control quantity P2 of the manual decision-making algorithm part.

[0087] (6) Combining the preemptive algorithm with the optimal manual decision-making, a robust control strategy is obtained to control the quantity and achieve robust and optimal system performance.

[0088] In this implementation scheme, for the control method, the framework only utilizes the structural boundary information of uncertainty without knowing the exact value of the boundary, so as to ensure robust consistent boundedness and consistent final boundedness performance; secondly, for human behavior, the framework promotes the optimal decision-making results by proposing a fuzzy membership function; this function is expressed in analytical form rather than numerical form; combining the preemptive machine algorithm with the optimal human decision-making algorithm can make full use of the machine's rapid response capability and the human's advanced decision-making ability, thereby improving the overall performance of the system and user experience, and completing more efficient, safer, more flexible and more humane human-computer interaction to achieve robust and optimal system performance.

[0089] Specifically, in step (1), a kinetic model is established:

[0090]

[0091] in, represents the time in the lower limb rehabilitation exoskeleton robot, represents the coordinates in the lower limb rehabilitation exoskeleton robot, represents the speed in the lower limb rehabilitation exoskeleton robot, represents the acceleration in the lower limb rehabilitation exoskeleton robot, represents the uncertain parameters in the lower limb rehabilitation exoskeleton robot, represents the unknown environmental disturbance in the lower limb rehabilitation exoskeleton robot, Represents the control quantity of artificial independent decision-making in the lower limb rehabilitation exoskeleton robot, the set Σ and Υ e Denote σ and v respectively e The possible areas of the lower limb rehabilitation exoskeleton robot are all compact sets. M(q,σ,t) is the inertia matrix. represents the Coriolis force / centrifugal force, g(q,σ,t) represents the gravitational force, Represents the input matrix, matrix / vector M(q,σ,t), g(q,σ,t), All have appropriate dimensions, M(·), C(·), g(·), B e (·) are all continuous;

[0092] The performance constraints are expressed as follows:

[0093]

[0094] in, yes The i-th component of li (·) and c l(·) is the first-order form of the constraint condition. Each constraint condition can be a global constraint condition or a non-global constraint condition. The above first-order performance constraint condition is expressed in matrix form:

[0095]

[0096] Where Λ=[Λ li ] m×n ,c=[c1,c2,…,c m ] T represents the first-order performance constraint in matrix form,

[0097] Derivative the first-order performance constraint with respect to t to obtain the second-order performance constraint:

[0098]

[0099] Among them, the first-order constraint matrix Λ li (q, t) and the first-order constraint matrix c l The derivatives of (q,t) are:

[0100]

[0101] The obtained second-order performance constraints are transposed and rewritten as:

[0102]

[0103] Where l = 1,…,m, the second-order performance constraint is written in matrix form:

[0104]

[0105] The above formula can express the integration of global constraints and non-global constraints, where the matrix Represents the right-hand side matrix of the rewritten second-order performance constraint equation.

[0106] Specifically, in step (3), the inertia matrix is decomposed;

[0107] The nominal part And function Continuous, for the convenience of expression, the following definitions can be made for the combination of several inertia matrices and their nominal parts:

[0108]

[0109] E(q,σ,t):=M(q,t)M -1 (q,σ,t)-I;

[0110] From the above definitions, we can get the equation relationship between D(q,t), ΔD(q,σ,t) and E(q,σ,t):

[0111] ΔD(q,σ,t)=D(q,t)E(q,σ,t);

[0112] Then decompose the input matrix as follows:

[0113]

[0114] in, and They represent the two parts of the decomposition of the input matrix, which are specifically expressed as follows:

[0115]

[0116] Defining the constrained following error based on the first-order performance constraint

[0117]

[0118] Constraint-based tracking error Define the preemptive algorithm partial control scheme

[0119] in, It represents the control quantity obtained by the preemptive algorithm part after calculation, δ>0, which represents the control quantity coefficient of the preemptive algorithm part selected based on machine calculation;

[0120] Constraint-based tracking error Define the control scheme for the manual decision part

[0121] in, Indicates the control quantity obtained by the manual decision-making part through calculation, γ>0 indicates the control quantity coefficient based on the manual decision selection, The norm of the structure representing the uncertainty bound, The structure representing the uncertainty bound is uniquely determined by:

[0122]

[0123] in, represents the nominal part of the inertia matrix, Λ(q,t) represents the performance constraint in matrix form, δ, γ>0, and represent the scalar design parameters based on the preemptive algorithm and the manual decision algorithm, respectively;

[0124] Based on the definition of preemptive algorithm and manual decision control scheme, the robust constraint following control scheme τ(t) is established as follows:

[0125]

[0126] Among them, p1 is an established preemptive algorithm, which is directly implemented by a computer, while the selection of γ in p2 is based on manual decision-making.

[0127] Specifically, step (4), determining the cost function;

[0128] Regarding system cost, based on the robust constrained following control scheme τ(t), considering both machine calculation and human decision-making, the following equation holds:

[0129]

[0130] Where d represents the differential operator and ds represents the global minimum solution of the cost function The arc length relative to ξ, where ξ represents the biological signal generated by humans as input to decision making;

[0131] Taking the square root of both sides of the above equation yields:

[0132]

[0133] The physical meaning of ds is the robustness of the uniformly bounded region and the uniformly limited bounded region to the change of ξ. The smaller ds is, the more robust the system performance is. In other words, the uniformly bounded region and the uniformly limited bounded region are less sensitive to the change of ξ.

[0134] Based on the above considerations, the cost function can be obtained as follows:

[0135]

[0136] Considering the above cost function, the global minimum solution of the cost function is satisfy:

[0137]

[0138] Among them, W and Y are A constant that satisfies the following two boundary conditions:

[0139]

[0140] Based on the above conditions, the cost function can be rewritten as follows:

[0141]

[0142] The corresponding membership function can be uniquely determined as follows, for all ξ∈[ξ1,ξ2]:

[0143]

[0144] Among them, ξ1 and ξ2 represent the left and right boundaries of the membership function domain, respectively. represents the center point of the membership function support [ξ1,ξ2], W and Y are respectively The constants that satisfy the boundary conditions, γ denote the upper and lower bounds of γ, respectively. Represents the global minimum solution The coefficient of

[0145] The uniqueness of the membership function also indicates the uniqueness of human optimal behavior.

[0146] Specifically, based on the determination of the membership function, the optimal manual decision in step (6) is selected as follows:

[0147]

[0148] in, represents the upper bound of γ, μ(ξ) represents the membership function determined based on the cost function;

[0149] Therefore, the robust control strategy τ(t) applied to the lower limb rehabilitation exoskeleton robot system is as follows:

[0150]

[0151] Among them, τ(t) represents the control quantity of the robust control strategy, It represents the control quantity obtained by the preemptive algorithm part. It represents the control quantity obtained by the calculation of the manual decision-making part. represents the time in the lower limb rehabilitation exoskeleton robot, represents the coordinates in the lower limb rehabilitation exoskeleton robot, Represents the speed in the lower limb rehabilitation exoskeleton robot. The preemptive algorithm part p1 is directly implemented by the computer, while the selection of γ in the manual decision part p2 is based on manual decision.

[0152] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same.

Claims

1. An optimal constraint-following control method for a lower limb rehabilitation exoskeleton robot considering human-machine interaction, characterized by: The following steps are involved: (1) Establish a dynamic model for the lower limb rehabilitation exoskeleton robot system; (2) Establish performance constraints based on the performance indicators that the exoskeleton robot needs to meet; (3) Decomposition of inertia matrix and input matrix; (4) Obtaining the bounds and functions of uncertainty parameters; (5) The machine solves the control quantity of the preemptive algorithm , change to solve the optimal membership function and calculate , and obtain the partial control quantity of the artificial decision algorithm ; (6) Combine the preemptive algorithm and manual decision-making to obtain the control quantity of the robust control strategy; In the step (3), the inertia matrix is first decomposed; Then decompose the input matrix: Defining the constrained following error based on the first-order performance constraint : Constraint-based tracking error Define the preemptive algorithm partial control scheme The partial control coefficient of the preemptive algorithm based on machine operation selection Related, and ; Constraint-based tracking error Define the manual decision-making part of the control scheme, in which the manual decision algorithm part of the control quantity and represents the control quantity obtained by the calculation of the manual decision part Related, and ; A robust constraint following control scheme is established based on the preemptive algorithm partial control scheme and the manual decision partial control scheme. as follows: 。 2. The optimal constraint following control method for a lower limb rehabilitation exoskeleton robot considering human-machine interaction according to claim 1, characterized in that: In the step (1), a kinetic model is established: ； in, represents the time in the lower limb rehabilitation exoskeleton robot, represents the coordinates in the lower limb rehabilitation exoskeleton robot, represents the speed in the lower limb rehabilitation exoskeleton robot, represents the acceleration in the lower limb rehabilitation exoskeleton robot, represents the uncertain parameters in the lower limb rehabilitation exoskeleton robot, represents the unknown environmental disturbance in the lower limb rehabilitation exoskeleton robot, Represents the control quantity of artificial independent decision-making in the lower limb rehabilitation exoskeleton robot, set and Respectively and The possible areas are all compact sets. is the inertia matrix, represents the Coriolis force or centrifugal force, Represents gravity, Represents the input matrix, matrix vector , , , have appropriate dimensions, , , , All are continuous; The performance constraints are expressed as follows: ； in, yes No. A quantity, and is the first-order form of the constraint condition. Each constraint condition can be a global constraint condition or a non-global constraint condition. The above first-order performance constraint condition is expressed in matrix form: ； in, , represents the first-order performance constraint in matrix form, The first-order performance constraints Taking the derivative, we get the second-order performance constraint: ； Among them, the first-order formal constraint matrix and the first-order formal constraint matrix The derivatives are: ； ； The obtained second-order performance constraints are transposed and rewritten as: ； in, , write the second-order performance constraint in matrix form: ； The above formula can express the integration of global constraints and non-global constraints, where the matrix Represents the right-hand side matrix of the rewritten second-order performance constraint equation.

3. The optimal constraint following control method for a lower limb rehabilitation exoskeleton robot considering human-machine interaction according to claim 1, characterized in that: The inertia matrix is decomposed to obtain the nominal part , And function Continuous, for the convenience of expression, the following definitions can be made for the combination of several inertia matrices and their nominal parts: ； ； ； From the above definition, we can get , and The equation relationship between: ； The decomposition of the input matrix is as follows: ； in, and They represent the two parts of the decomposition of the input matrix, which are specifically expressed as follows: ； The constraint following error is expressed as follows: ； The acquisition of some control quantities of the preemptive algorithm depends on the following formula: ； The acquisition of some control variables of the artificial decision-making algorithm depends on the following formula: ； in, , The norm of the structure representing the uncertainty bound, The structure representing the uncertainty bound is uniquely determined by: ； in, represents the nominal part of the inertia matrix, represents the performance constraints in matrix form, , represent the scalar design parameters based on preemptive algorithm and manual decision algorithm, respectively.

4. The optimal constraint following control method for a lower limb rehabilitation exoskeleton robot considering human-machine interaction according to claim 3, characterized in that: The step (4) is to determine the cost function; Regarding system cost, based on the robust constraint following control scheme, taking into account both machine calculation and human decision-making, the following equation holds true: ； in, represents the differential operator, represents the global minimum solution of the cost function Relative to The arc length, Represents biological signals generated by humans as inputs to decision making; Taking the square root of both sides of the above equation yields: ； in, The physical meaning of the uniform bounded region and the uniform limit bounded region is Robustness to change, The smaller the value, the more robust the system performance. In other words, the uniform boundedness region and the uniform limit boundedness region are Less sensitive to changes in Based on the above considerations, the cost function can be obtained as follows: ； Considering the above cost function, the global minimum solution of the cost function is satisfy: ； in, 、 They are A constant that satisfies the following two boundary conditions: ； ； Based on the above conditions, the cost function can be rewritten as follows: ； The corresponding membership function can be uniquely determined as follows: : ； in, Represent the left and right boundaries of the membership function domain, Represents the membership function support The center point, 、 They are The constants that satisfy the boundary conditions, , , Respectively The upper and lower bounds of Represents the global minimum solution The coefficient of .

5. The optimal constraint following control method for a lower limb rehabilitation exoskeleton robot considering human-machine interaction according to claim 4, characterized in that: Based on the determination of the membership function, the optimal manual decision in step (6) is selected as follows: ； in, express The upper bound of represents the membership function determined based on the cost function; Therefore, the control quantity of the robust control strategy applied to the lower limb rehabilitation exoskeleton robot system is obtained as follows: ； in, represents the control quantity of the robust control strategy, Represents the partial control amount of the preemptive algorithm, represents the partial control quantity of the artificial decision algorithm, represents the time in the lower limb rehabilitation exoskeleton robot, represents the coordinates in the lower limb rehabilitation exoskeleton robot, Represents the speed of the lower limb rehabilitation exoskeleton robot, part of the established preemptive algorithm Directly implemented by computers, while the manual decision-making part middle The selection is based on manual decision making.

Images