3 research outputs found
A Risk-Averse Preview-based -Learning Algorithm: Application to Highway Driving of Autonomous Vehicles
A risk-averse preview-based -learning planner is presented for navigation
of autonomous vehicles. To this end, the multi-lane road ahead of a vehicle is
represented by a finite-state non-stationary Markov decision process (MDP). A
risk assessment unit module is then presented that leverages the preview
information provided by sensors along with a stochastic reachability module to
assign reward values to the MDP states and update them as scenarios develop. A
sampling-based risk-averse preview-based -learning algorithm is finally
developed that generates samples using the preview information and reward
function to learn risk-averse optimal planning strategies without actual
interaction with the environment. The risk factor is imposed on the objective
function to avoid fluctuation of the values, which can jeopardize the
vehicle's safety and/or performance. The overall hybrid automaton model of the
system is leveraged to develop a feasibility check unit module that detects
unfeasible plans and enables the planner system to proactively react to the
changes of the environment. Theoretical results are provided to bound the
number of samples required to guarantee -optimal planning with a high
probability. Finally, to verify the efficiency of the presented algorithm, its
implementation on highway driving of an autonomous vehicle in a varying traffic
density is considered
Correct-By-Construction Fault-Tolerant Control
Correct-by-construction control synthesis methods refer to a collection of model-based techniques to algorithmically generate controllers/strategies that make the systems satisfy some formal specifications. Such techniques attract much attention as they provide formal guarantees on the correctness of cyber-physical systems, where corner cases may arise due to the interaction among
different modules. The controllers synthesized through such methods, however, may still malfunction due to faults, such as physical component failures and unexpected operating conditions, which lead to a sudden change of the system model. In these cases, we want to guarantee that the performance of the faulty system degrades gracefully, and hence achieve fault tolerance.
This thesis is about 1) incorporating fault detection and detectability analysis algorithms in correct-by-construction control synthesis,
2) formalizing the graceful degradation specification for fault tolerant systems with temporal logic, and 3) developing algorithms to synthesize correct-by-construction controllers that achieve such graceful degradation, with possible delay in the fault detection. In particular, two sets of
approaches from the temporal logic planning domain, i.e., abstraction-based synthesis and optimization-based path planning, are considered.
First, for abstraction-based approaches, we propose a recursive algorithm to reduce the fault tolerant controller synthesis problem into multiple small synthesis problems with simpler specifications. Such recursive reduction leverages the structure of the fault propagation and hence avoids the high
complexity of solving the problem monolithically as one general temporal logic game. Furthermore, by exploring the structural properties in the specifications, we show that, even when the fault is detected with delay, the problem can be solved by a similar recursive algorithm without constructing the belief space.
Secondly, optimization-based path planning is considered. The proposed approach leverages the recently developed temporal logic encodings and state-of-art mixed integer programming (MIP) solvers. The novelty of this work is to enhance the open-loop strategy obtained through solving the MIP so that it can react contingently to faults and disturbance.
Finally, the control synthesis techniques developed for discrete state systems is shown to be applicable to continuous states systems. This is demonstrated by fuel cell thermal management application. Particularly, to apply the abstraction-based synthesis methods to complex systems such as the fuel cell thermal management system, structural properties (e.g., mixed monotonicity) of the system are explored and leveraged to ease abstraction computation, and techniques are developed to improve the scalability of synthesis process whenever the system has a large number of control actions.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155031/1/yliren_1.pd