Strategy Synthesis for Data-Aware Dynamic Systems with Multiple Actors

The integrated modeling and analysis of dynamic systems and the data they manipulate has been long advocated, on the one hand, to understand how data and corresponding decisions affect the system execution, and on the other hand to capture how actions occurring in the systems operate over data. KR techniques proved successful in handling a variety of tasks over such integrated models, ranging from verification to online monitoring. In this paper, we consider a simple, yet relevant model for data-aware dynamic systems (DDSs), consisting of a finite-state control structure defining the executability of actions that manipulate a finite set of variables with an infinite domain. On top of this model, we consider a data-aware version of reactive synthesis, where execution strategies are built by guaranteeing the satisfaction of a desired linear temporal property that simultaneously accounts for the system dynamics and data evolution.

Stiffness Control of an Active Transtibial Prosthesis

Active, transtibial prostheses typically use finite state control algorithms that struggle with cadence and gait variability of the amputee. Recent work in artificial neural networks (ANN) have shown the possibility to predict the users intent based on EMG activity and the current position of the ankle, which can be used as an input signal into an improved controller. This paper examines how to implement an ANN signal into a zero order impedance controller, i.e., a stiffness controller, on a specific active transtibial prosthesis. The prosthesis incorporates a linear spiral spring in parallel with a four-bar mechanism. In order to implement stiffness control, the spring was moved to being in series with the four-bar mechanism to establish a relationship between the torque of the spring and the position of the motor. To ensure stiffness control is feasible, a MATLAB Simulink model of the system was created to test the robustness of the controller and the effect of moving the spring from parallel to series. The robustness of the controller was verified as the ankle position and torque requirements are met in the simulation. The Simulink model accurately models the new system and can be used in the future to optimize the motor or the four-bar mechanism for this new type of control.