The use of nonlinear model-predictive methods for path planning and following has the advantage of concurrently solving problems of obstacle avoidance, feasible trajectory selection, and trajectory following, while obeying constraints on control inputs and state values.

However, such approaches are computationally intensive, and may not be guaranteed to return a result in bounded time when performing a nonconvex optimization. This problem is an interesting application to cyber-physical systems due to their reliance on computation to carry out complex control. The computational burden can be addressed through model reduction, at a cost of potential (bounded) model error over the prediction horizon. In this paper we introduce a metric called uncontrollable divergence, and discuss how the selection of the model to use for the predictive controller can be addressed by evaluating this metric, which reveals the divergence between predicted and true states caused by return time and model mismatch. A map of uncontrollable divergence plotted over the state space gives the criterion to judge where reduced models can be tolerated when high update rate is preferred (e.g. at high speed and small steering angles), and where high-fidelity models are required to avoid obstacles or make tighter curves (e.g. at large steering angles). With this metric, we design a hybrid controller that switches at runtime between predictive controllers in which respective models are deployed.

Domain-specific modeling languages effectively constrain struc- tural concepts, but constraints that are not easily captured with structural constraints are still important to fix at design time. In practice these kinds of constraints are implicitly left to be carried out by the domain modelers. This paper explores the process of in- corporating system behavioral (not just structural) constraints into a DSML, and studies the way of generating feasible transformation solutions if those constraints fail, based on a transformation library constructed in advance. Our approach is to carry out the verifica- tion process through code generation, but utilize the results of veri- fication as an input to a model transformation generator. The output transformation then operates on the original model. As a case study, we applied the approach to finite state machine (FSM) models that control a cyber-physical system.

%B The 14th Workshop on Domain-Specific Modeling %P 1-6 %8 2014 %G eng %U http://dx.doi.org/10.1145/2688447.2688448 %R 10.1145/2688447.2688448 %0 Conference Paper %B Engineering of Computer Based Systems (ECBS), 2013 20th IEEE International Conference and Workshops on the %D 2013 %T Model-Based Software Synthesis for Self-Reconfigurable Sensor Network in Water Monitoring %A Kun Zhang %A Jonathan Sprinkle %K accelerometers %K Code Generation %K communication task %K computation task %K Computational modeling %K concurrent engineering %K concurrent tasks specification %K control tasks %K cyber-physical systems %K domain-specific modeling language %K drifters %K embedded programming %K environmental monitoring (geophysics) %K environmental science computing %K floating sensor testbed %K formal specification %K Global Positioning System %K GPS sensors %K hand-written code %K Instruction sets %K mobile computing %K mobile phone %K mobile radio %K mobile sensing platforms %K model-based software synthesis %K model-integrated computing %K program compilers %K Programming %K self-reconfigurable sensor network %K Smart phones %K software synthesis %K ubiquitous mobile device %K Unified modeling language %K water flow monitoring %K wireless sensor networks %B Engineering of Computer Based Systems (ECBS), 2013 20th IEEE International Conference and Workshops on the %P 40-48 %8 April %G eng %U http://dx.doi.org/10.1109/ECBS.2013.34 %R 10.1109/ECBS.2013.34 %0 Journal Article %J (under review) %D 0 %T Automobile Localization with Commodity Sensors %A Kun Zhang %A Jonathan Sprinkle %B (under review) %8 Submitted %G eng