Hybrid control for low-regular nonlinear systems: application to an embedded control for an electric vehicle

This note presents an embedded automatic control strategy for a low consumption vehicle equipped with an "on/off" engine. The main difficulties are the hybrid nature of the dynamics, the non smoothness of the dynamics of each mode, the uncertain environment, the fast changing dynamics, and low cost/ low consumption constraints for the control device. Human drivers of such vehicles frequently use an oscillating strategy, letting the velocity evolve between fixed lower and upper bounds. We present a general justification of this very simple and efficient strategy, that happens to be optimal for autonomous dynamics, robust and easily adaptable for real-time control strategy. Effective implementation in a competition prototype involved in low-consumption races shows that automatic velocity control achieves performances comparable with the results of trained human drivers. Major advantages of automatic control are improved robustness and safety. The total average power consumption for the control device is less than 10 mW

New advances in H∞ control and filtering for nonlinear systems

The main objective of this special issue is to summarise recent advances in H∞ control and filtering for nonlinear systems, including time-delay, hybrid and stochastic systems. The published papers provide new ideas and approaches, clearly indicating the advances made in problem statements, methodologies or applications with respect to the existing results. The special issue also includes papers focusing on advanced and non-traditional methods and presenting considerable novelties in theoretical background or experimental setup. Some papers present applications to newly emerging fields, such as network-based control and estimation

Verification of Uncertain POMDPs Using Barrier Certificates

We consider a class of partially observable Markov decision processes (POMDPs) with uncertain transition and/or observation probabilities. The uncertainty takes the form of probability intervals. Such uncertain POMDPs can be used, for example, to model autonomous agents with sensors with limited accuracy, or agents undergoing a sudden component failure, or structural damage [1]. Given an uncertain POMDP representation of the autonomous agent, our goal is to propose a method for checking whether the system will satisfy an optimal performance, while not violating a safety requirement (e.g. fuel level, velocity, and etc.). To this end, we cast the POMDP problem into a switched system scenario. We then take advantage of this switched system characterization and propose a method based on barrier certificates for optimality and/or safety verification. We then show that the verification task can be carried out computationally by sum-of-squares programming. We illustrate the efficacy of our method by applying it to a Mars rover exploration example.Comment: 8 pages, 4 figure

Vehicle-in-the-loop validation of autonomous cars

Validation of autonomous driving (AD) cars is a difficult task because of the complexity that results from the integration of multiple systems and the variety of operating conditions. To this end, testing with real vehicles is crucial to ensure a thorough validation of AD cars. However, testing AD vehicles in public roads is not viable in early stages of the development cycle. An alternative is to conduct tests in controlled environments, such as proving grounds.This thesis proposes a framework for modelling, analysis, and control of tests-scenarios for validation of autonomous cars by exposing the vehicle-under-test to a traffic scenario at a test track, where mobile test-targets represent other road users. The framework is suitable for leader-follower, multi-agent systems where the motion of the followers should be coordinated with the motion of an externally controlled leader. Scenarios are modelled as switched systems. The feasibility of the scenario is investigated using backward reachability analysis. A constrained optimal control problem is formulated to control the state of the multi-agent system through a sequence of goal sets. Simulation results illustrate the usefulness of the framework.A second contribution in this thesis is a novel method for decentralized computation of backward reachable sets and robust control invariant sets. The method is applicable to large-scale systems arising from the interconnection of multiple subsystems with linear dynamics. Polyhedral constraints and additive disturbances are considered. Compared to the standard centralized procedure for computation of control invariant sets, the proposed method is more efficient for large-scale systems where the coupling among the subsystems is sparse

μ-Dependent model reduction for uncertain discrete-time switched linear systems with average dwell time

In this article, the model reduction problem for a class of discrete-time polytopic uncertain switched linear systems with average dwell time switching is investigated. The stability criterion for general discrete-time switched systems is first explored, and a μ-dependent approach is then introduced for the considered systems to the model reduction solution. A reduced-order model is constructed and its corresponding existence conditions are derived via LMI formulation. The admissible switching signals and the desired reduced model matrices are accordingly obtained from such conditions such that the resulting model error system is robustly exponentially stable and has an exponential H∞ performance. A numerical example is presented to demonstrate the potential and effectiveness of the developed theoretical results

Mathematical control of complex systems

Copyright © 2013 ZidongWang et al.This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Towards an Autonomous Walking Robot for Planetary Surfaces

In this paper, recent progress in the development of the DLR Crawler - a six-legged, actively compliant walking robot prototype - is presented. The robot implements a walking layer with a simple tripod and a more complex biologically inspired gait. Using a variety of proprioceptive sensors, different reflexes for reactively crossing obstacles within the walking height are realised. On top of the walking layer, a navigation layer provides the ability to autonomously navigate to a predefined goal point in unknown rough terrain using a stereo camera. A model of the environment is created, the terrain traversability is estimated and an optimal path is planned. The difficulty of the path can be influenced by behavioral parameters. Motion commands are sent to the walking layer and the gait pattern is switched according to the estimated terrain difficulty. The interaction between walking layer and navigation layer was tested in different experimental setups