Robust mechanism synthesis with random and interval variables

Robust mechanism synthesis minimizes the impact of uncertainties on the mechanism performance. It has traditionally been performed by either a probabilistic approach or a worst case approach. Both approaches treat uncertainty as either random variables or interval variables. In reality, uncertainty can be a mixture of both. In this paper, methods are developed for robustness assessment and robust mechanism synthesis when random and interval variables are involved. Monte Carlo simulation is used to perform robustness assessment under an optimization framework for mechanism synthesis.