CAJ | 학술논문

　　针对番茄收获机械手动力学模型不精确和外界扰动问题，该文采用计算力矩-模糊补偿相结合的控制方法进行了番茄收获机械手轨迹跟踪控制研究。通过自适应模糊逻辑系统补偿机械手动力学模型中的不确定部分，模糊逻辑系统的参数基于Lynaponv稳定性理论自适应调节，并利用ADAMS与MATLAB进行仿真试验。结果表明，对比计算力矩法，计算力矩-模糊补偿控制算法中各关节轨迹跟踪误差明显减小且收敛趋势明显。关节1至关节7平均轨迹跟踪精度分别提高了70.29%、94.72%、0.61%、74.29%、89.75%、86.41%和67.14%。该控制方案中各关节控制力（矩）均呈规律性变化，增加扰动信号亦未使输出力（力矩）出现抖振和突变，启动力（矩）最大出现在移动关节2和转动关节4，分别为453N和98.33 N·m。研究结果可为番茄收获机械手轨迹跟踪控制系统的深入研究奠定基础。
　　침대번가수획궤계수동역학모형불정학화외계우동문제，해문채용계산력구-모호보상상결합적공제방법진행료번가수획궤계수궤적근종공제연구。통과자괄응모호라집계통보상궤계수동역학모형중적불학정부분，모호라집계통적삼수기우Lynaponv은정성이론자괄응조절，병이용ADAMS여MATLAB진행방진시험。결과표명，대비계산력구법，계산력구-모호보상공제산법중각관절궤적근종오차명현감소차수렴추세명현。관절1지관절7평균궤적근종정도분별제고료70.29%、94.72%、0.61%、74.29%、89.75%、86.41%화67.14%。해공제방안중각관절공제력（구）균정규률성변화，증가우동신호역미사수출력（력구）출현두진화돌변，계동력（구）최대출현재이동관절2화전동관절4，분별위453N화98.33 N·m。연구결과가위번가수획궤계수궤적근종공제계통적심입연구전정기출。
The tomato harvesting manipulator is apt to work in a complicated and unstructured production environment. The research on the trajectory tracking control is a key task. The tomato harvesting manipulator studied in the paper is a redundant manipulator with 7-DOF including two prismatic joints and five revolute joints, which is a multivariable nonlinear system. It is difficult to obtain its accurate dynamic model during control due to the external disturbance and the nonlinear friction, etc. The traditional control algorithm based on this model has poor robustness and cannot achieve global asymptotic stability. A fuzzy logic system can approximate a nonlinear function with arbitrary precision, which has more and more application in the manipulator control. To realize trajectory tracking with high accuracy and stability, a control method based on compute torque-fuzzy logic compensation is proposed to control trajectory tracking for a tomato harvesting manipulator. This method compensates the uncertain part of the dynamic model of the tomato harvesting manipulator via an adaptive fuzzy logic system, and parameters of the fuzzy compensator are adaptively adjusted by using a tuning algorithm derived from the Lyapunov stability theory. The dynamic model of the manipulator is set up based on a Lagrange method. In addition, the uncertain part of each joint in the model is approached by a separate function in order to reduce fuzzy rules and improve the real-time control. To realize universal approximation, the joint deviation and deviation rate membership function is defined as a Gauss type function. The trajectory tracking controller is designed to include the compute torque controller and self-adaptation fuzzy compensation controller. At the same time, the virtual prototype of the manipulator is structured via adding constraints and drives based on a modular design method. A co-simulation platform is established by ADAMS and MATLAB, which consists of a control program module, virtual prototype module, trajectory input module, and display module, etc. The trajectory tracking control system is simulated on the tomato harvesting manipulator using the compute torque-fuzzy logic compensation method and the computed torque method respectively. The trajectory tracking error and the force (torque) output of each joint are analyzed. The results show that the trace tracking average error from the 1st joint to the 7th joint by the computed torque method are 2.238×10-3m, 0.0242m, 0.0132 rad, 0.0526rad, 0.113 rad, 0.1075 rad and 0.0388 rad, while they are 6.65×10-4m, 1.278×10-3m, 0.0131rad, 0.0135 rad, 0.0116rad, 0.0146 rad and 0.0127rad by the compute torque-fuzzy logic compensation control. The control accuracy from the 1st joint to the 7th joint are increased by 70.29%, 94.72%, 0.61%, 74.29%, 89.75%, 86.41%, 67.14%respectively. The trace tracking errors in the compute torque-fuzzy logic compensation vary smoothly with a rapid convergence of the position error. The joints can reach the desired trajectory within 2-3s. The fuzzy compensation force (torque) of each joint varies smoothly with no significant change. The starting force (torque) is highest in the prismatic joint 2 and the revolute joint 4 when the initial errors are the largest, which are 453.127N and 98.33N·m..The force (torque) output of the 1st joint to the 4th joint which are close to the foundation of the manipulator is relatively larger than the other three joints. The torque output becomes more and smaller while nearing to the end-effector. The result provides a reference for motor choosing of each joint. In addition, the whole output force (torque) of each joint is stable and regular without severe vibration during the whole control process. The compute torque-fuzzy logic compensation control method can improve control accuracy and has great robustness, which will lay a foundation for further control study of the tomato manipulator.