Two nonlinear control techniques are proposed for an atomic force microscopesystem. Initially, a learning-based control algorithm is developed for the microcantilever-samplesystem that achieves asymptotic cantilever t...Two nonlinear control techniques are proposed for an atomic force microscopesystem. Initially, a learning-based control algorithm is developed for the microcantilever-samplesystem that achieves asymptotic cantilever tip tracking for periodic trajectories. Specifically, thecontrol approach utilizes a learning-based feedforward term to compensate for periodic dynamics andhigh-gain terms to account for non-periodic dynamics. An adaptive control algorithm is thendeveloped to achieve asymptotic cantilever tip tracking for bounded tip trajectories despiteuncertainty throughout the system parameters. Simulation results are provided to illustrate theefficacy and performance of the control strategies.展开更多
文摘Two nonlinear control techniques are proposed for an atomic force microscopesystem. Initially, a learning-based control algorithm is developed for the microcantilever-samplesystem that achieves asymptotic cantilever tip tracking for periodic trajectories. Specifically, thecontrol approach utilizes a learning-based feedforward term to compensate for periodic dynamics andhigh-gain terms to account for non-periodic dynamics. An adaptive control algorithm is thendeveloped to achieve asymptotic cantilever tip tracking for bounded tip trajectories despiteuncertainty throughout the system parameters. Simulation results are provided to illustrate theefficacy and performance of the control strategies.
