A suite of novel robust controllers is introduced for the pickup operation of microscale objects in a microelectromechanical system (MEMS). In MEMS, adhesive, surface tension, friction, and van der Waals forces are dominant. Moreover, these forces are typically unknown. The proposed robust controller overcomes the unknown contact dynamics and ensures its performance in the presence of actuator constraints by assuming that the upper bounds on these forces are known. On the other hand, for the robust adaptive critic-based neural network (NN) controller, the unknown dynamic forces are estimated online. It consists of an action NN for compensating the unknown system dynamics and a critic NN for approximating a certain strategic utility function and tuning the action NN weights. by using the Lyapunov approach, the uniform ultimate boundedness of the closed-loop manipulation error is shown for all the controllers for the pickup task. To imitate a practical system, a few system states are considered to be unavailable due to the presence of measurement noise. An output feedback version of the adaptive NN controller is proposed by exploiting the separation principle through a high-gain observer design. The problem of measurement noise is also overcome by constructing a reference system. Simulation results are presented and compared to substantiate the theoretical conclusions.
Q. Yang and J. Sarangapani, "A Suite of Robust Controllers for the Manipulation of Microscale Objects," IEEE Transactions on Systems, Man and Cybernetics, Institute of Electrical and Electronics Engineers (IEEE), Feb 2008.
The definitive version is available at http://dx.doi.org/10.1109/TSMCB.2007.909943
Electrical and Computer Engineering
Keywords and Phrases
Adaptive Neural Network (ANN); Micromanipulation; Reinforcement Learning; Robust Controller
Article - Journal
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