# Research on the Dynamic Control Method of CFETR Multi-Purpose Overload Robot  
[PERSON]  
at the Institute of Plasma Physics, CAS, has designed a CFETR multi-purpose overload robot (CMOR) based on the MPD to address the need for refined maintenance of CFETR core components. CMOR adopts a long cantilever hinged structure, the long robot arm with 9 degrees of freedom end load 2 tons, the short robot arm with 7 degrees of freedom end load 2.5 tons, position accuracy \(\pm\) 10 mm, maximum speed 100 mm/s. The CMOR redundant degree of freedom design can circumvent the physical limits of joints [10] surrounding obstacles [11] and motion singularities [12] to meet the requirement of full coverage of the vacuum chamber with \"D\" shaped cross section. Due to the rigid-flexible coupling deformation, the static deformation error of CMOR reaches 77 mm [13; 14], which makes accurate dynamic control very difficult. CMOR is a typical multi-body dynamical system, and the most commonly used dynamical modelling methods are the Lagrange equation, Newton-Euler equation, graph theory method, Kane method and variational method [15]. CMOR belongs to the flexible multi-body system with small deformation, mainly joint flexibility and link flexibility causing the structural deformation of the robot arm. The main methods for modelling CMOR flexible links are the finite element method, the hypothetical modal method and the centralized parameter method [16; 17]. [PERSON] et al. used the finite element method to model the flexibility of a robot with moving joints [18; 19]. [PERSON] et al. used the hypothetical modal method to model the flexibility of the robotic arm linkage to achieve accurate control of the robotic arm [20]. [PERSON] et al. used a centralized parametric method to model flexible links and achieve real-time control of the end trajectory of the robot arm [21]. [PERSON] et al. designed a robot controller using a neural network feedforward to compensate for kinetic uncertainty [22]. To optimize the kinematic constraints of the robot, [PERSON] et al. designed a robot control scheme combining obstacle interaction and path tracking [23]. [PERSON] et al. proposed a motion control algorithm for a robot in an unknown contact environment and analyzed the mapping relationship of the spatial torques of the robot joints [24]. [PERSON] et al. modelled the flexible joint of a robotic arm equivalent to a torsion spring, damped system [25]. [PERSON] et al. described the flexible joint by second-order time-varying equations and used the input and output parameters of the system to build an equivalent model of the flexible system [26]. For the problem of controller design for nonlinear, parameter uncertain systems, scholars have proposed a variety of advanced control techniques, including robust control techniques, sliding mode control techniques, adaptive control and neural network techniques [27]. Sliding mode control and adaptive control have outstanding advantages and are robust against system uncertainty and external disturbances [28]. [PERSON] et al. designed a new adaptive sliding mode controller to reduce the sliding mode surface oscillations and improve the robustness of the robot under various practical uncertainty conditions [29]. For the parameter error compensation problem, [PERSON] et al. used a genetic particle swarm algorithm to optimize a neural network prediction error model to achieve the compensation of target points in the whole workspace of the robot [30]. [PERSON] et al. constructed an artificial neural network that can simultaneously identify the robot's kinematic and flexibility parameters to compensate for the unmodeled error [31; 32; 33]. [PERSON] et al. compensated the joint flexibility error by modeling the geometric and flexibility errors [34]. The research and application of CMOR are still in the exploration stage, and its precise operation control problem is an urgent challenge to be solved.