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Abstract-We present a method for designing robust controllers for dynamical systems with linear temporal logic specifications. We abstract the original system by a finite Markov Decision Process (MDP) that has transition probabilities in a specified uncertainty set. A robust control policy for the MDP is generated that maximizes the worst-case probability of satisfying the specification over all transition probabilities in the uncertainty set. To do this, we use a procedure from probabilistic model checking to combine the system model with an automaton representing the specification. This new MDP is then transformed into an equivalent form that satisfies assumptions for stochastic shortest path dynamic programming. A robust version of dynamic programming allows us to solve for a -suboptimal robust control policy with time complexity O(log1 ) times that for the non-robust case. We then implement this control policy on the original dynamical system.

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This study examines the design parameters affecting the stability characteristics of a novel fluid flow energy harvesting device powered by aeroelastic flutter vibrations. The energy harvester makes use of a modal convergence flutter instability to generate limit cycle bending oscillations of a cantilevered piezoelectric beam with a small flap connected to its free end by a revolute joint. The critical flow speed at which destabilizing aerodynamic effects cause self-excited vibrations of the structure to emerge is essential to the design of the energy harvester because it sets the lower bound on the operating wind speed and frequency range of the system. A linearized analytic model of the device that accounts for the three-way coupling between the structural, unsteady aerodynamic, and electrical aspects of the system is used to examine tuning several design parameters while the size of the system is held fixed. The effects on the aeroelastic system dynamics and relative sensitivity of the flutter stability boundary are presented and discussed. A wind tunnel experiment is performed to validate the model predictions for the most significant system parameters.