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Register a new userPreview Reference Governors: A Constraint Management Technique for Systems With Preview Information
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This paper presents a constraint management strategy based on Scalar Reference Governors (SRG) to enforce output, state, and control constraints while taking into account the preview information of the reference and/or disturbances signals. The strategy, referred to as the Preview Reference Governor (PRG), can outperform SRG while maintaining the highly-attractive computational benefits of SRG. However, as it is shown, the performance of PRG may suffer if large preview horizons are used. An extension of PRG, referred to as Multi-horizon PRG, is proposed to remedy this issue. Quantitative comparisons between SRG, PRG, and Multi-horizon PRG on a one-link robot arm example are presented to illustrate their performance and computation time. Furthermore, extensions of PRG are presented to handle systems with disturbance preview and multi-input systems. The robustness of PRG to parametric uncertainties and inaccurate preview information is also explored.
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This paper presents a computationally efficient solution for constraint management of multi-input and multi-output (MIMO) systems. The solution, referred to as the Decoupled Reference Governor (DRG), maintains the highly-attractive computational features of Scalar Reference Governors (SRG) while having performance comparable to Vector Reference Governors (VRG). DRG is based on decoupling the input-output dynamics of the system, followed by the deployment of a bank of SRGs for each decoupled channel. We present two formulations of DRG: DRG-tf, which is based on system decoupling using transfer functions, and DRG-ss, which is built on state feedback decoupling. A detailed set-theoretic analysis of DRG, which highlights its main characteristics, is presented. We also show a quantitative comparison between DRG and the VRG to illustrate the computational advantages of DRG. The robustness of this approach to disturbances and uncertainties is also investigated.
Incorporating predictions of external inputs, which can otherwise be treated as disturbances, has been widely studied in control and computer science communities. These predictions are commonly referred to as preview in optimal control and lookahead in temporal logic synthesis. However, little work has been done for analyzing the value of preview information for safety control for systems with continuous state spaces. In this work, we start from showing general properties for discrete-time nonlinear systems with preview and strategies on how to determine a good preview time, and then we study a special class of linear systems, called systems in Brunovsky canonical form, and show special properties for this class of systems. In the end, we provide two numerical examples to further illustrate the value of preview in safety control.
This paper considers the problem of safety controller synthesis for systems equipped with sensor modalities that can provide preview information. We consider switched systems where switching mode is an external signal for which preview information is available. In particular, it is assumed that the sensors can notify the controller about an upcoming mode switch before the switch occurs. We propose preview automaton, a mathematical construct that captures both the preview information and the possible constraints on switching signals. Then, we study safety control synthesis problem with preview information. An algorithm that computes the maximal invariant set in a given mode-dependent safe set is developed. These ideas are demonstrated on two case studies from autonomous driving domain.
This paper deals with the lateral control of a convoy of autonomous and connected following vehicles (ACVs) for executing an Emergency Lane Change (ELC) maneuver. Typically, an ELC maneuver is triggered by emergency cues from the front or the end of convoy as a response to either avoiding an obstacle or making way for other vehicles to pass. From a safety viewpoint, connectivity of ACVs is essential as it entails obtaining or exchanging information about other ACVs in the convoy. This paper assumes that ACVs have reliable connectivity and that every following ACV has the information about GPS position traces of the lead and immediately preceding vehicles in the convoy. This information provides a discretized preview of the trajectory to be tracked. Based on the available information, this article focuses on two schemes for synthesizing lateral control of ACVs based on(a) a single composite ELC trajectory that fuses lead and preceding vehicles GPS traces and (b) separate ELC trajectories based on preview data of preceding and lead vehicles. The former case entails the construction of a single composite ELC trajectory, determine the cross-track error, heading and yaw rate errors with respect to this trajectory and synthesize a lateral control action. The latter case entails the construction of two separate trajectories corresponding to the lead vehicles and preceding vehicles data separately and the subsequent computation of two sets of associated errors and lateral control actions and combining them to provide a steering command. Numerical and experimental results corroborate the effectiveness of these two schemes.
We here present preliminary results on a parameter scan of the THDMa, a new physics model that extends the scalar sector of the Standard Model by an additional doublet as well as a pseudoscalar singlet. In the gauge-eigenbasis, the additional pseudoscalar serves as a portal to the dark sector, with a fermionic dark matter candidate. This model is currently one of the standard benchmarks for the LHC experimental collaborations. We apply all current theoretical and experimental constraints and identify regions in the parameter space that might be interesting for an investigation at possible future $e^+e^-$ facilities.
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