The Transverse Dynamic Force Microscope (TDFM) is unique as it uses a vertical cantilever, and genuinely permits scans without physical interaction with a specimen. Recently, we suggested a simple control scheme for true noncontact scans using the TDFM. This control scheme implemented in FPGA-systems (Field-Programmable Gate Arrays) was developed for a non-contact control task at specific points above a given specimen, but dynamic specimen placement requirements in the horizontal plane through an x-y stage were neglected. Considering the large range of the specimen, a practical approach has been developed which reconfigures the fixed-point-arithmetic control implementation and permits a dynamic scan in true non-contact mode. For this, an off-line numerical optimization has been developed which establishes the most suitable fixed-point ranges of the control algorithm, avoiding algorithm overflow. This creates an implementation robust to plant and sensor non-linearities and dynamic changes during non-contact scans. Experimental non-contact scanning results, for nano-spheres in water, demonstrate the imaging capacity of the TDFM.
In order to intercept a highly manoeuvering target with an ideal impact angle in the three-dimensional space, this paper promises to probe into the problem of three-dimensional terminal guidance. With the goal of the highly target acceleration and short terminal guidance time, a guidance law, based on the advanced fast non-singular terminal sliding mode theory, is designed to quickly converge the line-of-sight (LOS) angle and the LOS angular rate within a finite time. In the design process, the target acceleration is regarded as an unknown boundary external disturbance of the guidance system, and the RBF neural network is used to estimate it. In order to improve the estimation accuracy of RBF neural network and accelerate its convergence, the parameters of RBF neural network are adjusted online in real time. At the same time, an adaptive law is designed to compensate the estimation error of the RBF neural network, which improves the convergence speed of the guidance system. Theoretical analysis demonstrates that the state and the sliding manifold of the guidance system converge in finite time. According to Lyapunov theory, the stability of the system can be guaranteed by online adjusting the parameters of RBF neural network and adaptive parameters. The numerical simulation results verify the effectiveness and superiority of the proposed guidance law.
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