Safe Control under Uncertainty

By Dorsa Sadigh, Ashish Kapoor. 2015

25 citations recorded by the source corpus.

Controller synthesis for hybrid systems that satisfy temporal specifications expressing various system properties is a challenging problem that has drawn the attention of many researchers. However, making the assumption that such temporal properties are deterministic is far from the reality. For example, many of the properties the controller has to satisfy are learned through machine learning techniques based on sensor input data. In this paper, we propose a new logic, Probabilistic Signal Temporal Logic (PrSTL), as an expressive language to define the stochastic properties, and enforce probabilistic guarantees on them. We further show how to synthesize safe controllers using this logic for cyber-physical systems under the assumption that the stochastic properties are based on a set of Gaussian random variables. One of the key distinguishing features of PrSTL is that the encoded logic is adaptive and changes as the system encounters additional data and updates its beliefs about the latent random variables that define the safety properties. We demonstrate our approach by synthesizing safe controllers under the PrSTL specifications for multiple case studies including control of quadrotors and autonomous vehicles in dynamic environments.

Subfields: Computational Theory and Mathematics, Control and Systems Engineering, Hardware and Architecture

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Citations in the corpus

Safe Model-based Reinforcement Learning with Stability Guarantees (2017, methodology): “Other approaches use model predictive control with constraints, a model-based technique where the control actions are optimized online.”
Safe Control under Uncertainty with Probabilistic Signal Temporal Logic (2016, methodology): “Starting with an initial state x0, the MPC scheme aims to determine the optimal strategy uH given the dynamics model of the system defined in equation (2), while satisfying the STL formula ϕ.”
Planning for Autonomous Cars that Leverage Effects on Human Actions (2016, background): “But when it comes to other human drivers, they tend to rely on simplistic models: for example, assuming that other drivers will be bounded disturbances [9, 20], they will keep moving at the same velocity [16, 21, 26], or they will approximately follow one of a set of known trajectories [10, 25].”