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Page 130                          Tong et al. Intell Robot 2024;4:125-45  I http://dx.doi.org/10.20517/ir.2024.08

               fifth joint of the robot device has a lightweight mechanical structure and works in the moment mode with
               almost zero resistance, the kinetic model is not established for this joint, and this operation can reduce the
               complexity of the robot model and improve the model accuracy. Therefore, the dynamics model of this robot
               is a 4-degree-of-freedom model.


               2.2.1 Kinetic model construction
               The Newton-Euler method is used to build a dynamics model for this robot. Compared with the Lagrangian
               modellingmethod, thisapproachcalculatesthroughtheinter-jointforceandmotionrelationshiplayerbylayer,
               the physical meaning is clear, and it does not involve derivation operation, so that the results can be obtained
               quickly and the calculation efficiency is high [34] . The standard form of its expression is given as

                                                                             
                                                             
                                                     
                                                                           
                                              =  (  ) ¥ +   (  , ¤) +   (  ) +   (  , ¤, ¥)             (3)
               where   , ¤, ¥ represent the position, velocity and acceleration vectors of the joint, respectively,    denotes the
                         
                           
               joint torque vector,   (  ) is the mass matrix,   (  , ¤) is the centrifugal and Koch force vectors,   (  ) denotes
                                                           
               the gravitational moment, and   (  , ¤, ¥) denotes the other compensating moment parameters.
                                               
                                                 
               2.2.2 Linearisation of the model
               The kinetic parameters of the robot are represented in the kinetic model in a nonlinear combination, which
               makesitdifficulttoidentifythekineticparameters. Throughtheparallelaxistheorem,thecoordinatesystemof
               theinertialparameterinthenonlineartermiscoordinatetransformedtocompletethelinearisationprocess [35] .
               The force and moment expressions in the kinetic model are given in
                                            +1                                   
                                        =   +1          +1 +       ¤      ×           +             ×       ×           +       ¤     
                                           +1              +1                                           (4)
                                       =           +1 +      +1 ×      ×       ¤      +       ×             +       ¤     
                                       +1              +1        +1 +         
               where    ,    are the force and moment on the articulated linkage,    +1    is the coordinate transformation matrix,
                                                                       
               ¤   ,   , ¤   represents the linear acceleration, angular velocity, and angular acceleration of the articulated linkage
                                                  
               of the rehabilitation robot, respectively,      +1 is the vector from the origin of the coordinate system of the     ℎ
               articulated linkage (i.e., the    coordinate system) to the origin of the coordinate system of the    +1  ℎ articulated
                                                            
               linkage,    isthemassofthearticulatedlinkage, and         representsthecentreofmassofthearticulatedlinkage.
                  denotes the inertia tensor matrix. The identification equation for the kinetic parameters can be expressed as:
                                                                
                                                                  
                                                            =   (  , ¤, ¥)                              (5)
                           
               where   (  , ¤, ¥) ∈      ×    denotes the observation matrix;    and    denote the number of kinetic parameters
                             
               in the kinetic parameter set and the number of robot rods, respectively.    ∈      ×1  is the set of dynamics
               parameters. Since some columns of   (  , ¤, ¥) are always zero and some columns have a linear relationship,
                                                      
                                                    
               no matter what value of   , ¤, ¥ is taken,    cannot make the columns full rank, and thus    cannot be solved
                                        
                                          
               uniquely by the least-squares method. In this section, we use the      decomposition method. The matrix   
               is decomposed to full rank, and the result of the decomposition is used to restructure the inertia parameters.
               The following formula can be obtained:
                                                            ˜                                           (6)
                                                           =              
                     ˜
               where    is the matrix of column full-rank coefficients obtained from the full-rank decomposition of   .            
               is the minimum inertia parameter after reorganisation.
               2.2.3 Fourier excitation trajectory
               In the realm of robot dynamics parameter identification, the judicious linearisation of the model ensures the
               unique convergence of identification results toward the target values. Discrepancies between target and true
               values primarily stem from kinematic parameter deviations and measurement noise. Rational design of iden-
               tification excitation trajectories serves to mitigate the impact of measurement noise on results and enhance
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