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Friction compensation techniques are studied for control of a flexible-link robot based on the LuGre friction model. To overcome the problem of uncertain parameters in the friction model, adaptive control schemes are used for two different types of parametric uncertainties. A novel dual-observer technique is proposed to estimate the internal state inside the friction model. A distributed-parameter dynamic model is used for the flexible arm to design the controllers. The Lyapunov stability theorem is used to guarantee the global asymptotic stability of the controllers. The performance of position tracking and link vibration attenuation is verified through experimental results. The results also confirm the effectiveness of the proposed friction compensation schemes.

The system matrix (SM) being a main part of statistical image reconstruction algorithms establishes relationship between the object and projection space. The aim was to determine it in a short duration time, towards obtaining the best quality of contrast images. In this study, a new analytical method based on Cavalieri's principle as subdividing common regions has been proposed in which the precision of the amounts of estimated areas was improved by increasing the number of divisions (NOD), and consequently the total SM's time was increased. An important issue is the tradeoff between the NODs and computational time. For this purpose, a Monte Carlo simulated Jaszczak phantom study was performed by the Monte Carlo N-Particle transport code version 5 (MCNP5) in which the tomographic images of resolution and contrast phantoms were reconstructed by maximum likelihood expectation maximization (MLEM) algorithm, and the influence of NODs variations was investigated. The results show that the lowest and best quality have been obtained at the NODs of 0 and 8, respectively and in the optimum case, the SM's total time at NOD of 8 was 925 s, which was much lower than those of the conventional Monte Carlo simulations and experimental test.

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