Decoupled nonlinear adaptive control of position and stiffness for pneumatic soft robots
Decoupled nonlinear adaptive control of position and stiffness for pneumatic soft robots
复制标题
气动软机器人位置和刚度的解耦非线性自适应控制
DOI：
10.1177/0278364920903787
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
A. Fagiolini
中科院分区：
文献类型：
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作者：
Maja Trumić;K. Jovanovic;A. Fagiolini
针对一类具有刚性连杆和柔性关节的对抗性气动软机器人，研究了关节刚度和位置的同时鲁棒闭环控制问题。通过引入刚度变量的一阶动力学方程，并利用嵌入气动执行器矩阵零空间的附加控制自由度，提出了一种由自适应补偿器和动态解耦器组成的新型控制方法。该方法建立在已有的自适应控制理论的基础上，为气动变刚度执行器关节刚度的闭环控制提供了一种方法。在一个涉及惯性和执行器矩阵的非常温和的假设下，该解能够处理模型的不确定性，并且当期望的刚度恒定或缓慢变化时，也能够处理气动执行器的不确定性。通过引入控制器内部状态变量的一阶微分方程来实现位置和刚度的解耦，该方程考虑了刚度动态中压力的时间导数。给出了位置跟踪误差和刚度跟踪误差稳定性的形式证明。这种方法的一个吸引人的特性是，它不需要位置的更高导数或任何刚度导数。该解决方案针对几个用例进行了验证，首先是在模拟中，然后是通过一个带有McKibben肌肉的真实气动软机器人。与现有技术的比较表明，位置和刚度跟踪技术更加稳健。
This article addresses the problem of simultaneous and robust closed-loop control of joint stiffness and position, for a class of antagonistically actuated pneumatic soft robots with rigid links and compliant joints. By introducing a first-order dynamic equation for the stiffness variable and using the additional control degree of freedom, embedded in the null space of the pneumatic actuator matrix, an innovative control approach is introduced comprising an adaptive compensator and a dynamic decoupler. The proposed solution builds upon existing adaptive control theory and provides a technique for closing the loop on joint stiffness in pneumatic variable stiffness actuators. Under a very mild assumption involving the inertia and actuator matrices, the solution is able to cope with uncertainties of the model and, when the desired stiffness is constant or slowly varying, also of the pneumatic actuator. Position and stiffness decoupling is achieved by the introduction of a first-order differential equation for an internal state variable of the controller, which takes into account the time derivative of pressure in the stiffness dynamics. A formal proof of the stability of the position and stiffness tracking errors is provided. An appealing property of the approach is that it does not require higher derivatives of position or any derivatives of stiffness. The solution is validated with respect to several use-cases, first in simulation and then via a real pneumatic soft robot with McKibben muscles. A comparison with respect to existing techniques reveals a more robust position and stiffness tracking skill.