In this paper, the issue of model uncertainty in safety-critical control is addressed with a data-driven approach. For this purpose, we utilize the structure of an input-ouput linearization controller based on a nominal model along with a Control Barrier Function and Control Lyapunov Function based Quadratic Program (CBF-CLF-QP). Specifically, we propose a novel reinforcement learning framework which learns the model uncertainty present in the CBF and CLF constraints, as well as other control-affine dynamic constraints in the quadratic program. The trained policy is combined with the nominal modelbased CBF-CLF-QP, resulting in the Reinforcement Learningbased CBF-CLF-QP (RL-CBF-CLF-QP), which addresses the problem of model uncertainty in the safety constraints. The performance of the proposed method is validated by testing it on an underactuated nonlinear bipedal robot walking on randomly spaced stepping stones with one step preview, obtaining stable and safe walking under model uncertainty.
bstract. More than 90% of harlequin frog species (Atelopus spp.), endemic to the Americas, are currently threatened with extinction. We report the discovery of the only currently known hreeding population of the Critically Endangered/4. varius in Costa Rica. This population was located in 2008 on a private property in Las Tablas Protected Zone near San Vito, Coto Brus at 1300 m elevation. Previously, the only known remaining/remnant population of this species and genus was a single location near Manuel Antonio, Puntarenas, Costa Rica, where two individuals were documented in 2004. Subsequent searches at this location have yielded no additional sightings. Delineating the spatial limits of this population, quantifying demographics and resource use, and implementing conservation actions are necessary to ensure persistence of this population. Conducting additional surveys in this region to ascertain occurrence of additional populations is warranted.
Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs) are popular tools for enforcing safety and stability of a controlled system, respectively. They are commonly utilized to build constraints that can be incorporated in a min-norm quadratic program (CBF-CLF-QP) which solves for a safety-critical control input. However, since these constraints rely on a model of the system, when this model is inaccurate the guarantees of safety and stability can be easily lost. In this paper, we present a Gaussian Process (GP)based approach to tackle the problem of model uncertainty in safety-critical controllers that use CBFs and CLFs. The considered model uncertainty is affected by both state and control input. We derive probabilistic bounds on the effects that such model uncertainty has on the dynamics of the CBF and CLF. Then, we use these bounds to build safety and stability chance constraints that can be incorporated in a min-norm convex optimization program, called GP-CBF-CLF-SOCP. As the main theoretical result of the paper, we present necessary and sufficient conditions for pointwise feasibility of the proposed optimization problem. We believe that these conditions could serve as a starting point towards understanding what are the minimal requirements on the distribution of data collected from the real system in order to guarantee safety. Finally, we validate the proposed framework with numerical simulations of an adaptive cruise controller for an automotive system.
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