450 results about "Exoskeleton robot" patented technology

The invention discloses a wearable exoskeleton man-machine cooperation real-time control method based on artificial intelligence, and relates to the technical field of exoskeleton robots, and the method comprises the steps: recognizing an exoskeleton scene in power grid maintenance, analyzing the demand degree, and constructing a maintenance demand sequence; determining a collaborative analysis scene based on the maintenance demand sequence; performing quantitative analysis on the man-machine confrontation event in the collaborative analysis scene to obtain a collaborative deviation value and generate a collaborative analysis signal; constructing a data set according to signal extraction features, and predicting man-machine cooperative control behaviors by using a genetic algorithm; finally, the exoskeleton is controlled in real time according to prediction, the man-machine cooperation degree is judged, and the control mode is switched when necessary. The exoskeleton man-machine cooperation performance is improved, the cooperation problem can be found in time, the control strategy is optimized, and it is guaranteed that electric power overhaul work is conducted efficiently and safely.

The invention provides an exoskeleton robot self-adaptive control method and related equipment, and relates to the technical field of exoskeleton robots. The method comprises the following steps: acquiring motion information and position information of each movable joint in the exoskeleton robot; inputting the motion information and the position information into a pre-trained angle prediction model to obtain a target angle of each movable joint; the difference value between the target angle and the actual angle output by the exoskeleton controller serves as an angle error; constructing a virtual control quantity based on the angle error; the virtual control quantity is combined with motion parameters of the exoskeleton robot to be input into a disturbance observer, and estimated disturbance is generated; the angle error, the virtual control quantity and the estimated disturbance are input into an adaptive neural network controller, and the control torque of each movable joint is obtained; and based on the control torque of each movable joint, generating a control signal of each movable joint. The method and the device have good control precision.

The invention relates to the technical field of robot control and somatosensory interaction, and particularly discloses a knee joint exoskeleton robot control system based on somatosensory interaction. The system comprises a somatosensory signal acquisition and fusion module, a personalized dynamic model construction module, an adaptive intention decision module, a variable impedance compliance control module and a rehabilitation process evaluation and parameter self-tuning module. Through cooperative work of the modules, precise recognition of the motion intention of a wearer, online construction of an individualized biomechanical model and flexible and adaptive motion control are achieved, control parameters can be automatically adjusted according to the rehabilitation process, and therefore the flexibility and individuation level of man-machine interaction and the effectiveness of rehabilitation training are improved.

The invention provides an exoskeleton robot impedance adjustment and trajectory planning method, a medium and equipment, and relates to the technical field of exoskeleton robot control, and the method comprises the steps: obtaining a multi-dimensional original signal related to exoskeleton movement, and carrying out the preprocessing of the multi-dimensional original signal; the exoskeleton complex motion trail is decomposed into a basic motion component and a working condition adaptive component; adjusting the Fourier coefficient of the abnormal harmonic wave to suppress the abnormal harmonic wave, and obtaining an anti-interference basic motion component according to the adjusted Fourier coefficient; performing topographic feature matching and gain control on the working condition adaptive component to obtain a reconstructed working condition adaptive component; the anti-interference basic motion component and the reconstruction working condition adaptive component are recombined to obtain a final motion track; according to the error between the final motion track and the expected track of the exoskeleton, adjusting parameters of the impedance model in real time, so that the exoskeleton tracking expected track is optimized in real time. According to the method, the application scene of the exoskeleton can be widened, and the practicability of the exoskeleton in a complex environment is remarkably improved.

The invention relates to the technical field of data processing, and discloses a gait rehabilitation evaluation method and system based on spatial-temporal characteristics of multi-modal data, a terminal and a storage medium, and the method comprises the steps: synchronously collecting myoelectricity, electroencephalogram and exoskeleton robot IMU signals of a dyskinesia crowd in a certain motion normal form in a specific period interval; a motion function scoring result of a doctor on a patient according to a scale is synchronously recorded, data preprocessing, channel selection and feature extraction are performed on myoelectricity, electroencephalogram and exoskeleton robot IMU signals, a model is established based on a neural network, motion evaluation is performed according to a fused feature array, and a motion function quantitative score is obtained; and the motion function state of the evaluated person is graded according to the score. According to the method, the multi-modal synchronous data rehabilitation evaluation model of the movement function of the dyskinesia crowd is constructed according to multiple information mining, and the movement function state of the evaluated person is rapidly evaluated, predicted and graded.

The invention provides an exoskeleton mountaineering intention recognition method and system and a storage medium, and belongs to the technical field of exoskeleton robot control, and the method comprises the steps of signal preprocessing and compensation, hierarchical feature extraction, multi-modal fusion recognition, control output, safety protection and the like. The method comprises the following steps: performing 50Hz power frequency filtering and 20-450Hz band-pass filtering on an electromyographic signal, and compensating signal distortion caused by muscle deformation by combining IMU (Inertial Measurement Unit) acceleration data; a dynamic four-dimensional channel is adopted to preferably extract myoelectricity features, and an improved attention mechanism model is utilized to extract multi-scale features; fusing multi-modal information such as myoelectricity and terrains based on a Bayesian fusion framework to realize intention recognition; meanwhile, triple safety protection strategies of joint angle hard limiting, falling detection and protection and signal loss fault-tolerant processing are set; the accuracy and control speed of motion intention recognition are improved, good safety performance is achieved, and the auxiliary effect and reliability of the exoskeleton in the mountaineering motion are effectively improved.

The invention relates to a robot track synchronization control method. According to the technical scheme, efficient collection and over-distance transmission of neural signals are achieved through the quantum entanglement technology, the brain movement intention is analyzed in combination with a high-sensitivity sensor and an intelligent algorithm, an instruction is transmitted to a robot power driving system through a low-delay communication architecture, and real-time synchronization of man-machine tracks is achieved through a two-way feedback system. Meanwhile, the movement flexibility of the robot is improved by adopting a lightweight design, the problems of high delay, low precision, poor interaction experience and the like in traditional man-machine synchronous control are solved, and the method can be widely applied to the fields of exoskeleton robots, industrial collaborative robots, rehabilitation medical robots and the like. The method has the advantages of being low in delay, high in precision, capable of improving interaction experience and wide in application range.

The invention relates to the field of exoskeleton robot technology and robot control, in particular to an intelligent self-adaptive exoskeleton robot multi-view sensing obstacle avoidance method and system. A binocular camera is combined with a laser radar to monitor the external environment, and when it is detected that an obstacle exists around a wearer, the obstacle avoidance is performed; and path searching is carried out by using an improved fast expansion random tree star algorithm, a control instruction is generated based on exoskeleton kinematics modeling, and the exoskeleton robot is driven to realize an obstacle avoidance function. The obstacle avoidance method can flexibly deal with various complex conditions in a dynamically changing environment, is high in real-time performance, can help a wearer to complete obstacle avoidance in some emergency conditions, fully reflects the intelligent self-adaptive capability of the system, and enhances the safety and reliability.

The invention discloses an exoskeleton robot fixed time control method based on output constraint and disturbance observation, relates to the field of robot control, constructs a fixed time neural controller (FTNC), and ensures that a system is stable in fixed time by combining a universal obstacle Lyapunov function (UBLF) and a preset performance function (PPF), so as to improve the system stability. The convergence time is irrelevant to the initial state; meanwhile, a radial basis function neural network (RBFNN) and a nonlinear disturbance observer (NDO) are adopted to jointly compensate the uncertainty of the model and the man-machine interaction disturbance; by dynamically adjusting UBLF boundary conditions, joint angle errors are limited, and training safety is guaranteed. According to the method, a fixed time control theory is adopted, fixed time is combined with a preset performance function (PPF) and a universal barrier Lyapunov function (UBLF), and the output of the exoskeleton robot is strictly limited, so that the rapid convergence and transient performance requirements of an exoskeleton system are met at the same time.

The utility model discloses an exoskeleton robot testing frame, which belongs to the technical field of exoskeleton robot testing, aims at solving the problems of poor stability and inconvenience in height adjustment of exoskeleton robot testing, and comprises a bottom frame and a support frame, the support frame is fixed on the side wall of the top end of the bottom frame, and a plurality of mounting plates are fixed on the side wall of the bottom end of the bottom frame. Universal wheels are fixed on the side wall of the bottom end of the mounting plate; the top frame is fixed to the side wall of one side of the supporting frame, a lifting frame is arranged under the top frame, and a lifting assembly is arranged on one side of the supporting frame; through the arrangement of the lifting assembly, the height position of the exoskeleton can be adjusted according to field application requirements, so that a worker can conveniently mount and dismount the exoskeleton, the working efficiency of the work at the earlier stage of exoskeleton testing is greatly improved, the motion flexibility of the exoskeleton and the use performance of the testing frame are improved, and the testing frame is suitable for popularization and application. And the device is suitable for exoskeletons with different heights, and the adaptability is high.

The invention discloses a real-time detection method and system for the lower limb binding state of an exoskeleton robot. The method comprises the following steps: acquiring a human body gait in a reversing motion state at present, performing feature extraction on the human body gait, and judging a current gait phase through the feature extraction; and then the lower limb binding state of the corresponding gait phase is detected in real time. The system comprises a state observer module, a gait phase recognition module, a binding detection module, an active disturbance rejection controller module and a CMSIS-DSP library module. Through a split-phase dynamic detection strategy, the real-time performance and reliability of binding and falling identification are remarkably improved.

The invention relates to the technical field of exoskeleton robots, in particular to an exoskeleton driving system with multi-mode torque coupling and intelligent-brain cooperative control, which comprises a flexible torque coupling mechanism, a holographic intelligent-brain cooperative control strategy, an energy safety integrated shield module and an ultra-light integrated magic body structure, wherein the flexible torque coupling mechanism is used for adapting to power and flexibility of different scenes and carrying out torque output and rigidity continuous adjustment; a holographic intelligent brain cooperative control strategy is fused with perception information, data are analyzed through quantum federal learning and a bionic algorithm, torque is optimized, and cooperative control is carried out; the energy and safety integrated shield module is used for constructing a double-safety system, and recycling is achieved through magnetorheological fluid kinetic energy recovery and shape memory alloy potential energy storage. The ultra-light integrated magic body structure is matched with the human body size to adjust the exoskeleton, and the man-machine cooperation response is improved through the composite carbon fiber material and the highly integrated design. Therefore, the problems of high energy consumption, complex mechanical structure, insufficient stability and the like in the prior art are solved.

The invention belongs to the technical field of robot control, and discloses an exoskeleton robot control method and system based on a brain-computer interface, and the method comprises the steps: obtaining brain-computer interface data, carrying out the preprocessing of the brain-computer interface data, obtaining the preprocessed brain-computer interface data, extracting motion features, and obtaining a motion feature vector; performing motion intention recognition on the motion feature vector by using an integrated learning model, generating a motion control instruction, and performing motion control according to the motion control instruction; and based on the obtained motion state feedback data, motion state adjustment is carried out by using a dynamic adjustment algorithm, a motion control instruction is dynamically adjusted according to a motion state adjustment result, and motion control optimization is carried out through the adjusted motion control instruction. The problems that in the prior art, the signal processing precision is low, the intention recognition accuracy is insufficient, and the control strategy is rigid are solved, and efficient cooperation of the exoskeleton robot and the motion intention of the user is achieved.

The invention discloses a multi-stage control method for a lower limb exoskeleton, is applied to the field of exoskeleton robots, and aims to solve the problem that the design control precision is reduced due to the uncertainty of a model in the prior art. According to the controller designed by the invention, in a high-level control layer, a human body training mode is determined by a motion intention of an operator so as to generate a reference gait track; in the middle control layer, a variable admittance controller is designed, and three training modes of human exoskeleton cooperative movement, namely a passive mode, an active mode and a passive-to-active mode, are planned. In a low-level control loop, a method with a radial basis function neural network estimation function and a fixed-time convergence controller with input dead zone compensation are provided so as to ensure that an exoskeleton joint position tracks an expected trajectory output by an admittance loop. In order to avoid the Zeno phenomenon of the designed controller, an event trigger mechanism is used to determine the execution time of sampling and transmitting signals. Finally, the effectiveness of the proposed control strategy is verified through simulation and experimental results.

The invention belongs to the technical field of lower limb rehabilitation, and discloses a man-machine coupling system intelligent cooperative control method and device based on deterministic learning. Establishing a state-space equation of the lower limb exoskeleton robot, defining a tracking error, and designing a self-adaptive neural network controller of the lower limb exoskeleton robot; defining an expected position state vector and a real position state vector of the walking robot, solving a position state vector error, and designing a self-adaptive neural network controller of the walking robot; the method comprises the following steps: constructing a time-varying linear matrix, verifying the exponential stable convergence characteristic of the time-varying linear matrix under a continuous excitation condition on the basis of an adaptive neural network controller, storing nonlinear dynamic knowledge of a man-machine coupling system, and constructing a learning controller by using the learned knowledge. According to the method, while the unknown dynamic state of the man-machine coupling system is accurately modeled, the continuous excitation condition is ensured, so that accurate and stable trajectory tracking control is realized.

The invention discloses an exoskeleton decoupling control method based on a fixed time expansion state observer, and the method comprises the following steps: 1), building a dynamic model of a multi-input-multi-output exoskeleton robot, and extracting an unknown dynamic coupling part; 2) designing a fixed time extended state observer to estimate uncertain dynamic coupling and unknown external disturbance in the system, and performing compensation by adopting a feedforward mode; and 3) in combination with the observer, designing a fixed-time nonsingular terminal sliding mode controller to quickly track the expected trajectory of the exoskeleton so as to realize decoupling control of the exoskeleton system. According to the invention, for a multi-input multi-output exoskeleton robot system with model uncertainty, a fixed-time convergence decoupling control method is designed in combination with an extended state observer and a nonsingular terminal sliding mode controller; the robustness and the stability of the system are improved, and meanwhile, the tracking error of the system can be controlled at a low level within fixed time.

The invention discloses a high-compatibility self-calibration lower limb exoskeleton rehabilitation robot which comprises a first lower limb rehabilitation device, a second lower limb rehabilitation device and a hip joint adjusting device. The hip joint exoskeleton assembly comprises three active driving units, and the active driving units with the rotating surfaces corresponding to the horizontal plane are aligned with the rotating center of the hip joint through the remote motion center mechanism with the virtual axis. According to the high-compatibility self-calibration lower limb exoskeleton rehabilitation robot, rotation of a horizontal plane, a coronal plane and a sagittal plane can be completed at the position corresponding to the hip joint of a user, three degrees of freedom of forward flexion / backward extension, adduction / abduction and inward rotation / outward rotation of the hip joint of the human body are achieved respectively, and the exoskeleton robot is aligned to the rotation center of the hip joint of the human body; therefore, the rehabilitation effect of the high-compatibility self-calibration lower limb exoskeleton rehabilitation robot can be improved.

A kind of walking power-assisted exoskeleton robot, comprising: waist support structure module, waist rod module, joint drive module and binding system.Waist rod module includes waist rod module assembly, and waist rod module assembly is provided with two groups and is symmetrically arranged relative to waist support structure module.The design scheme proposed in the embodiment of the application can not only simplify the structure of the whole machine by reducing assembly process, but also reduce the complexity of the structure and improve the stability of the machine.By applying symmetric telescopic adjustment module, the machine can provide better wearing adaptability and comfort for people of different body shapes.Compared with the existing light walking exoskeleton robot, the present application has the advantages of wearing comfort and convenience, easy operation, high whole machine protection ability and strong power-assisted ability.In addition, the present application has small size and light weight, so it has strong market competitiveness in the same type of exoskeleton robot products.

The invention discloses a pathology adaptive prediction control method for a lower limb rehabilitation exoskeleton robot, and belongs to the technical field of rehabilitation robot control. The method comprises the steps that the joint angle, the angular speed and the man-machine interaction torque of the lower limb rehabilitation exoskeleton robot are collected in real time, and a kinetic equation of the lower limb rehabilitation exoskeleton robot is constructed; on the basis of the kinetic equation of the lower limb rehabilitation exoskeleton robot, parameter vectors representing the current pathological features of the patient are estimated online, so that a prediction model in the lower limb rehabilitation exoskeleton robot is updated, and the optimal auxiliary torque is solved. The intelligent brain of the exoskeleton robot can be deeply fused with the real-time state of the body of the patient, and rehabilitation training which is really personalized and self-adaptive and can effectively promote neural function recovery is provided for the patient.

The invention discloses a lower limb exoskeleton robot layered cooperative walking aid control method. The method comprises the steps of data acquisition and preprocessing; human-machine coupling dynamics modeling is carried out, and required moments tau < hip >, tau < knee > and tau < ankle > of the hip joint, the knee joint and the ankle joint are obtained; low-layer joint control, including hip joint moment, knee / ankle virtual impedance moment and friction compensation calculation; middle-layer multi-axis cooperation and gait phase judgment; high-rise energy is optimally distributed, assistance is distributed to three joints in a phase and a safety boundary, meanwhile, the requirements for labor saving and stability are considered, a minimization problem is written in a unified mode, J serves as the total amount, and torque of the hip joint, torque of the knee joint and torque of the ankle joint are solved in a combined mode; according to the step-by-step self-adaption of myoelectricity driving, in order to cope with human body fatigue changes, step-by-step updating is carried out on the virtual stiffness within the movement distance. The method has the advantages of improving man-machine cooperation level, man-machine adaptability and walking aid efficiency.

The invention belongs to the technical field of robots, and particularly relates to a rope-driven glenohumeral joint bionic double-arm exoskeleton robot and a control method.The rope-driven glenohumeral joint bionic double-arm exoskeleton robot comprises a power-assisted mechanical arm, and the power-assisted mechanical arm comprises a glenohumeral mechanism installed on the top of a wheelchair. The humeral mechanism comprises a first motor installed on the top of the wheelchair through a first motor installation base, a first driving rope wheel coaxially installed with an output shaft of the first motor and a first driven rope wheel synchronously rotating with the first driving rope wheel through a first driving rope assembly, and the transmission ratio of the first driving rope wheel to the first driven rope wheel is 1: 2. The first driving rope wheel and the first driven rope wheel are driven by the first motor to rotate, the first driving rope wheel moves by 1 degree, the first driven rope wheel moves by 2 degrees along with the first driving rope wheel, the movement rule is similar to the movement rule of the glenohumeral joint of the human body, the robot can better conform to the movement rule of the human body, and higher-quality and more flexible assistance is provided for the human body.

The invention belongs to the technical field of robots, and discloses a self-adaptive multi-scene exoskeleton robot control method and system, and the method comprises the steps: carrying out the denoising of collected multi-modal sensor data through employing a scene feature entropy-based Kalman filtering algorithm, carrying out the fusion of the denoised multi-modal sensor data, and carrying out the fusion of the denoised multi-modal sensor data, extracting spatial features and time sequence features in combination with a hybrid deep learning model to identify a current scene type; and according to a current scene type identification result, constructing an emotional state and motion intention mapping model, generating a corresponding power control instruction, and synchronously outputting pulse parameters of active stimulation muscles and passive stimulation muscles based on the power control instruction so as to perform man-machine cooperative control under multiple scenes. The method can adapt to different types of exoskeleton robots, and a more intelligent and accurate solution is provided for multiple application scenes.

The invention discloses a compact mining whole-body power-assisted exoskeleton robot with an anti-explosion shell and a posture monitoring function, and relates to the technical field of operation robots, the compact mining whole-body power-assisted exoskeleton robot comprises a trunk wearing assembly, two driving upper limb assemblies, two driving lower limb assemblies, a flexible connecting frame and a posture detection assembly; the two upper limb driving assemblies are connected with the trunk wearing assembly, and shoulder supporting structures are installed at the positions, close to the trunk wearing assembly, of the two upper limb driving assemblies. The flexible connecting frame can be bent and deformed; the two driving lower limb assemblies are connected with the trunk wearing assembly through a flexible connecting frame; the upper limb driving assembly and the lower limb driving assembly can be quickly connected and matched; the driving upper limb assembly can also be quickly connected and matched with an external execution tool; the posture detection assembly is used for detecting motion postures of the upper limb driving assembly and the lower limb driving assembly. According to the exoskeleton robot, the stooping operation comfort can be improved, various kinds of work assistance can be achieved, and the adaptability is better.

The invention discloses a hip joint power-assisted exoskeleton robot for fire rescue, and relates to the technical field of power-assisted exoskeleton robots. Comprising a back frame, an upper body binding module, power-electric control integrated modules and leg modules, the upper body binding module comprises a waist binding belt and straps, the power-electric control integrated modules are arranged on the two sides of the waist binding belt, each power-electric control integrated module comprises a shell, a mounting plate, a battery pack, a motor and a control assembly, and the shell is divided into an inner shell and an outer shell; the inner shell and the outer shell can be matched to form a containing cavity, the installation plate is installed in the inner shell, the battery pack, the control assembly and the motor are sequentially and fixedly installed on the installation plate from top to bottom, when the inner shell is covered with the outer shell, the installation plate, the battery pack, the motor and the control assembly are all located in the containing cavity, and the leg module comprises a leg rod assembly and a leg bandage. The protection capability of the robot can be effectively improved, and meanwhile, the back frame better fitting the back curve of the human body is adopted, so that the robot is more comfortable to wear.

The invention discloses a multi-protocol fusion exoskeleton robot component collaborative test system and method. The test system comprises a joint motor twin trawling test module, a force sensor test module, an attitude sensor test module and a control module. The control module is designed based on a ZYNQ chip, and the PS part is used for operating a general control upper computer and outputting control signals to the force sensor test module and the attitude sensor test module; the PL part comprises a signal output unit, a signal acquisition unit and a signal storage unit, and is used for outputting a motor control signal and acquiring a sensor signal of the test module in real time; and after a control signal and a sensor signal are stored, the acquired signals are transmitted to the PS part for processing through an AXI bus. According to the invention, three components of the exoskeleton robot can be controlled and driven in a centralized manner, the problem that precise working condition simulation cannot be carried out on the joint motor in the prior art is solved, and reliable support is provided for performance evaluation and calibration under complex working conditions.

The invention relates to an exoskeleton robot control method and device and an exoskeleton robot. The method comprises the following steps: determining an activity parameter according to a signal collected by a sensor; identifying a plurality of motion intentions according to the activity parameters, judging motion identifiers under the motion intentions, and determining a plurality of motion identifiers; performing state transition according to the plurality of action identifiers by utilizing a finite-state machine to obtain a target sub-state; a plurality of motion modes are preset in the finite-state machine, and each motion mode corresponds to at least one sub-state; and controlling the power unit according to the control instruction corresponding to the target sub-state. According to the method, the motion intention recognition and the finite-state machine are combined to complete the operation state transition, so that the misjudgment rate of state recognition can be reduced, and the robustness of the finite-state machine is improved; the multiple preset motion modes of the finite-state machine are divided into the sub-states, the situation of wrong assistance can be eliminated to a certain degree, misoperation of the exoskeleton robot is avoided, and the assistance control accuracy is improved.

The invention discloses an exoskeleton robot abnormal working condition automatic response and risk avoiding decision-making method, and relates to the technical field of exoskeleton, and the method comprises the steps: deploying a plurality of types of sensors to synchronously collect environment data, human physiological data and exoskeleton operation data, and carrying out the preprocessing to remove interference; through fusion of abnormal harmonic detection and a finite-state machine, dangerous working conditions such as wind speed abrupt change are identified, and grades are judged; monitoring eye movement and heart rate variability data in real time, quantifying cognitive load and grading; a decision rule base is constructed, and joint locking and other adaptive risk avoiding decisions are generated in combination with working condition types, grades and cognitive loads; the controller drives the execution mechanism to execute a decision, continuously monitors data at a frequency, dynamically adjusts the decision or recovers a normal mode. According to the method, dangerous working conditions are accurately identified through multi-source data fusion, adaptive risk avoiding measures are automatically triggered, a power assisting strategy is dynamically adjusted in combination with cognitive loads, and operation flexibility and physical output are balanced.

The present invention discloses an active upper limb exoskeleton robot, belonging to the field of exoskeleton robot technology, comprising: a back exoskeleton, a left arm exoskeleton, and a right arm exoskeleton. The back exoskeleton is a flexible structure, the front surface of which contacts the human back, and can bend and deform along with the human spine when the human body bends. The left arm exoskeleton and the right arm exoskeleton are symmetrically arranged on either side of the back exoskeleton and are respectively rotatably mounted on the top of the back exoskeleton via shoulder joints. The shoulder joints have three degrees of freedom: flexion / extension, internal rotation / external rotation, and adduction / abduction, and the rotation axes of the three degrees of freedom are respectively consistent with the rotation axes of the corresponding degrees of freedom of the human shoulder joint. The robot realizes flexion / extension, internal rotation / external rotation, and adduction / abduction movements, and the robot's degrees of freedom of movement are consistent with the human joint's degrees of freedom. The elbow joint has one degree of freedom. The elastic lumbar vertebra can adapt to the bending and deformation of the human lumbar vertebra, increase the bending range, and improve wearer comfort.

The invention discloses an exoskeleton robot control parameter determination method and system, and particularly relates to the technical field of robot control, and the method comprises the steps: synchronously collecting rigid joint torque, flexible cable tension and inertia signals, generating a dual-channel response spectrum through Fourier transform, and obtaining a frequency ratio vector; a kinetic energy coupling index and a viscosity lag index are calculated, and a bandwidth difference index is generated through the cooperation phase model in combination with the inertia-damping signature vector; a rigid gain matrix and a flexible gain matrix are obtained in a self-adaptive mode under the energy consumption constraint through sparse Bayesian optimization; constructing a weighting function according to the motion prediction phase and the energy consumption constraint, and synthesizing the two gain matrixes into a composite control instruction stream; and expected and actually measured moment residual errors are monitored, a gain increment is generated according to gradient perception when the threshold is exceeded, and a coupling feature extraction unit is written back, so that millisecond-level gain correction is realized, and the problem that coupling oscillation is caused by unified gain mismatch due to the fact that energy mutual feedback and phase delay cannot be accurately quantified in the prior art is solved.

The invention discloses an exoskeleton system based on man-machine symbiosis and a construction method, and aims to improve the suitability of a rehabilitation exoskeleton robot and people. Comprising a control system hardware platform, a teleoperation robot, a digital twinborn body of the teleoperation robot, an exoskeleton operator, a digital twinborn body of the exoskeleton operator, an exoskeleton robot and a digital twinborn body of the exoskeleton robot. By integrating various sensors, comprehensive acquisition of motion states and biological signals of exoskeleton operators is realized. In addition, the system further comprises an electroencephalogram signal analysis module and a voice semantic analysis module which are used for evaluating the concentration degree and intention of the user. According to the method, biomechanical consistency is ensured through a digital twinning technology, and autonomous learning and optimization adjustment are performed by using a large model, so that the functions of motion mode prediction, kinetic parameter optimization, personalized rehabilitation scheme suggestion and the like are realized. The method not only improves the effect and efficiency of rehabilitation training, but also enhances the user experience and the safety and reliability of the system.