Real Time Optimization by Extremum Seeking Control
Real Time Optimization by Extremum Seeking Control
Adaptive inverse control of linear and nonlinear systems using dynamic neural networks
IEEE Transactions on Neural Networks
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This paper presents an investigation of the control problem of aiming a laser beam under dynamic disturbances, using light intensity for feedback. The beam is steered with a bi-axial MEMS mirror, which is driven by a control signal generated by processing the beam intensity sensed by a single photodiode. Since the pointing location of the beam is assumed to be not available for real-time control, a static nonlinear mapping from the two-dimensional beam location to the sensor measurement is estimated with the use of the least-squares algorithm, using data from an optical position sensor (OPS). The previous formulation results in a state-space system model with a nonlinear output function. The controller design problem is addressed with the integration of an extended Kalman filter (EKF) and a pair of linear time-invariant (LTI) single-input/single-output (SISO) controllers into one system. In order to demonstrate the effectiveness of the proposed approach, experimental results of a case relevant to free-space optics for communications and directed energy applications is presented here.