漂浮基刚、柔混合空间机器人系统智能控制研究
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摘要
随着空间探索的深入，空间机器人将发挥越来越重要的作用。柔性臂空间机器人系统更是由于具有诸多优点而成为近二十年来空间机器人研究领域的热点。为了保证控制器的控制精度，在为柔性臂空间机器人系统进行轨迹跟踪控制器设计时必须同时考虑对柔性振动的抑制。针对系统惯性参数存在不确定或未知的情况下，研究了漂浮基刚、柔空间机器人系统的建模、轨迹跟踪控制问题，并系统地研究了柔性空间机器人系统的振动主动抑制问题。
首先使用拉格朗日方程和假设模态法分别建立了刚、柔空间机器人系统的动力学方程，通过研究分析发现，该系统动力学方程为选择的系统组合惯性参数的线性函数。
其次为刚性双臂空间机器人系统分别设计了自适应控制、模糊神经网络控制和鲁棒自适应神经网络控制来保证机器人载体姿态、关节角和末端爪手的轨迹跟踪。自适应控制可以克服系统部分参数未知对控制器的影响；模糊神经网络控制则完全不需要预知系统惯性参数便可对空间机器人系统进行控制；鲁棒自适应神经网络控制则可更好地利用已知的系统参数。以上三种控制方案还具有不需要测量载体位置、移动速度、移动加速度的优点。
再次，分别对柔性臂空间机器人系统柔性振动不主动控制、复合控制算法、奇异摄动法和混合轨迹控制算法进行了研究。当不对柔性振动进行主动控制时，计算力矩法和模糊神经网络控制均可保证系统刚性轨迹的跟踪，但系统同时存在着较大的振动现象。使用自适应控制算法对柔性振动进行抑制时，可以快速实时地抑制柔性振动，但该复合控制算法理论上会削弱刚性轨迹控制器的鲁棒性。利用奇异摄动法，可将柔性臂空间机器人系统分解为慢变、快变两个子系统，在两个时间尺度的假设下分别对其进行控制，从理论上解决了控制器直接叠加的问题。基于虚拟控制力观念，设计了同时反应柔性振动和刚性期望轨迹的混合期望轨迹，并基于该混合期望轨迹分别设计了滑模变结构控制和滑模神经网络控制，这种混合轨迹控制方案不会出现振动控制器影响轨迹控制器性能的问题。
最后，对所设计的系统控制器进行数值仿真分析。
仿真结果表明，所有轨迹跟踪控制器均能保证系统轨迹跟踪，柔性振动抑制方案均能对振动进行快速实时地抑制。
With the development of space exploration, space robot will play more and moreimportant functions. Especially, the flexible space robot system becomes the majorsubject over the past two decades because of their advantages over rigid space robot.In order to ensure the operation of high accuracy, the vibration control of flexiblespace robot system must be considered during the controller design. In the presence ofunknown or uncertain system parameters unknown, the dynamics modeling, trajectorytracking controlling, and vibration suppression of rigid and flexible space-based spacerobot system are investigated.
Firstly, the dynamic equations of rigid and flexible space robot system arederived by Lagrange equation and assumed modes method. It is found by analysis thatthe dynamic equations can be linearly dependent on a group of system inertialparameters.
Secondly, the adaptive control, neural network fuzzy control, and robust-adaptiveneural network control are designed for dual-arm rigid space robot system toguarantee the trajectory tracking of base’s attitude, joint angles, and end-point. Inwhich, the adaptive control can overcome the effect of part unknown parameters. Theneural network fuzzy control doesn’t need the system parameters at all during thetrajectory tracking, while the robust-adaptive neural network control makes use of theknown system parameters better. In particular, it doesn't require measuring theposition, velocity nor acceleration of the base because of an effective exploitation ofthe particular property of the system dynamics.
Then, the control schemes of flexible space robot system without vibrationsuppression, using composite control, under singular perturbation theory, and usinghybrid control are proposed respectively. When the flexible vibration is not controlactively, the tracking error is very small by computed torque method and neuralnetwork fuzzy control, but there are large vibrations during the control. Usingcomposite control, a simple direct parameter adaptive rule is presented to suppress thevibration more quickly. Since this composite controller regard the flexible vibrationstabilizer as a perturbation of the whole system, the rigid motion controller obviously loses a certain degree of robustness. By singular perturbation theory, the singularperturbation model of the flexible space robot system is obtained. The flexiblevibration stabilizer and the rigid trajectory tracking controller can be designed intwo-time scale, it solve the superposition of the two controllers in theory. In hybridcontrol, the virtual control force concept is employed to generate a new hybrid desiredtrajectory reflecting the flexible vibration modes as well as the rigid desired trajectory.As the proposed sliding-mode control and sliding-mode neural network control trackthe hybrid desired trajectory, the flexible mode can be stabilized. The problem ofcontroller superposition is not appearing in the hybrid control.
Lastly, the numerical simulations are carried out for all system controllers.
Simulations results confirm that all proposed trajectory tracking control schemescan dominates the trajectory tracking for space robot system, and all vibrationstabilizers can suppress the flexible vibration actively and quickly in real time.
首先使用拉格朗日方程和假设模态法分别建立了刚、柔空间机器人系统的动力学方程，通过研究分析发现，该系统动力学方程为选择的系统组合惯性参数的线性函数。
其次为刚性双臂空间机器人系统分别设计了自适应控制、模糊神经网络控制和鲁棒自适应神经网络控制来保证机器人载体姿态、关节角和末端爪手的轨迹跟踪。自适应控制可以克服系统部分参数未知对控制器的影响；模糊神经网络控制则完全不需要预知系统惯性参数便可对空间机器人系统进行控制；鲁棒自适应神经网络控制则可更好地利用已知的系统参数。以上三种控制方案还具有不需要测量载体位置、移动速度、移动加速度的优点。
再次，分别对柔性臂空间机器人系统柔性振动不主动控制、复合控制算法、奇异摄动法和混合轨迹控制算法进行了研究。当不对柔性振动进行主动控制时，计算力矩法和模糊神经网络控制均可保证系统刚性轨迹的跟踪，但系统同时存在着较大的振动现象。使用自适应控制算法对柔性振动进行抑制时，可以快速实时地抑制柔性振动，但该复合控制算法理论上会削弱刚性轨迹控制器的鲁棒性。利用奇异摄动法，可将柔性臂空间机器人系统分解为慢变、快变两个子系统，在两个时间尺度的假设下分别对其进行控制，从理论上解决了控制器直接叠加的问题。基于虚拟控制力观念，设计了同时反应柔性振动和刚性期望轨迹的混合期望轨迹，并基于该混合期望轨迹分别设计了滑模变结构控制和滑模神经网络控制，这种混合轨迹控制方案不会出现振动控制器影响轨迹控制器性能的问题。
最后，对所设计的系统控制器进行数值仿真分析。
仿真结果表明，所有轨迹跟踪控制器均能保证系统轨迹跟踪，柔性振动抑制方案均能对振动进行快速实时地抑制。
With the development of space exploration, space robot will play more and moreimportant functions. Especially, the flexible space robot system becomes the majorsubject over the past two decades because of their advantages over rigid space robot.In order to ensure the operation of high accuracy, the vibration control of flexiblespace robot system must be considered during the controller design. In the presence ofunknown or uncertain system parameters unknown, the dynamics modeling, trajectorytracking controlling, and vibration suppression of rigid and flexible space-based spacerobot system are investigated.
Firstly, the dynamic equations of rigid and flexible space robot system arederived by Lagrange equation and assumed modes method. It is found by analysis thatthe dynamic equations can be linearly dependent on a group of system inertialparameters.
Secondly, the adaptive control, neural network fuzzy control, and robust-adaptiveneural network control are designed for dual-arm rigid space robot system toguarantee the trajectory tracking of base’s attitude, joint angles, and end-point. Inwhich, the adaptive control can overcome the effect of part unknown parameters. Theneural network fuzzy control doesn’t need the system parameters at all during thetrajectory tracking, while the robust-adaptive neural network control makes use of theknown system parameters better. In particular, it doesn't require measuring theposition, velocity nor acceleration of the base because of an effective exploitation ofthe particular property of the system dynamics.
Then, the control schemes of flexible space robot system without vibrationsuppression, using composite control, under singular perturbation theory, and usinghybrid control are proposed respectively. When the flexible vibration is not controlactively, the tracking error is very small by computed torque method and neuralnetwork fuzzy control, but there are large vibrations during the control. Usingcomposite control, a simple direct parameter adaptive rule is presented to suppress thevibration more quickly. Since this composite controller regard the flexible vibrationstabilizer as a perturbation of the whole system, the rigid motion controller obviously loses a certain degree of robustness. By singular perturbation theory, the singularperturbation model of the flexible space robot system is obtained. The flexiblevibration stabilizer and the rigid trajectory tracking controller can be designed intwo-time scale, it solve the superposition of the two controllers in theory. In hybridcontrol, the virtual control force concept is employed to generate a new hybrid desiredtrajectory reflecting the flexible vibration modes as well as the rigid desired trajectory.As the proposed sliding-mode control and sliding-mode neural network control trackthe hybrid desired trajectory, the flexible mode can be stabilized. The problem ofcontroller superposition is not appearing in the hybrid control.
Lastly, the numerical simulations are carried out for all system controllers.
Simulations results confirm that all proposed trajectory tracking control schemescan dominates the trajectory tracking for space robot system, and all vibrationstabilizers can suppress the flexible vibration actively and quickly in real time.
引文
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