Delay-Based Controller Design for Continuous-Time and Hybrid Applications

Motivated by the availability of different types of delays in embedded systems and biological circuits, the objective of this work is to study the benefits that delay can provide in simplifying the implementation of controllers for continuous-time systems. Given a continuous-time linear time-invariant (LTI) controller, we propose three methods to approximate this controller arbitrarily precisely by a simple controller composed of delay blocks, a few integrators and possibly a unity feedback. Different problems associated with the approximation procedures, such as finding the optimal number of delay blocks or studying the robustness of the designed controller with respect to delay values, are then investigated. We also study the design of an LTI continuous-time controller satisfying given control objectives whose delay-based implementation needs the least number of delay blocks. A direct application of this work is in the sampled-data control of a real-time embedded system, where the sampling frequency is relatively high and/or the output of the system is sampled irregularly. Based on our results on delay-based controller design, we propose a digital-control scheme that can implement every continuous-time stabilizing (LTI) controller. Unlike a typical sampled-data controller, the hybrid controller introduced here -— consisting of an ideal sampler, a digital controller, a number of modified second-order holds and possibly a unity feedback -— is robust to sampling jitter and can operate at arbitrarily high sampling frequencies without requiring expensive, high-precision computation

Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties

Robots operating in real world settings must navigate and maintain safety while interacting with many heterogeneous agents and obstacles. Multi-Agent Control Barrier Functions (CBF) have emerged as a computationally efficient tool to guarantee safety in multi-agent environments, but they assume perfect knowledge of both the robot dynamics and other agents' dynamics. While knowledge of the robot's dynamics might be reasonably well known, the heterogeneity of agents in real-world environments means there will always be considerable uncertainty in our prediction of other agents' dynamics. This work aims to learn high-confidence bounds for these dynamic uncertainties using Matrix-Variate Gaussian Process models, and incorporates them into a robust multi-agent CBF framework. We transform the resulting min-max robust CBF into a quadratic program, which can be efficiently solved in real time. We verify via simulation results that the nominal multi-agent CBF is often violated during agent interactions, whereas our robust formulation maintains safety with a much higher probability and adapts to learned uncertainties

On quantized consensus by means of gossip algorithm - Part II: Convergence time

This paper deals with the distributed averaging problem over a connected network of agents, subject to a quantization constraint. It is assumed that at each time update, only a pair of agents can update their own numbers in terms of the quantized data being exchanged. The agents are also required to communicate with one another in a stochastic fashion. In the first part of the paper, it was shown that the quantized consensus is reached by means of a stochastic gossip algorithm proposed in a recent paper, for any arbitrary quantization. The current part of the paper considers the expected value of the time at which the quantized consensus is reached. This quantity (corresponding to the worst case) is lower and upper bounded in terms of the topology of the graph, for uniform quantization. In particular, it is shown that the upper bound is related to the principal minors of the weighted Laplacian matrix. A convex optimization is also proposed to determine the set of probabilities (used to pick a pair of agents) which leads to the fast convergence of the gossip algorithm

Gentamicin-attenuated leishmania infantum vaccine: protection of dogs against canine visceral leishmaniosis in endemic area of southeast of Iran

An attenuated line of Leishmania infantum (L. infantum H-line) has been established by culturing promastigotes in vitro under gentamicin pressure. A vaccine trial was conducted using 103 naive dogs from a leishmaniosis non-endemic area (55 vaccinated and 48 unvaccinated) brought into an endemic area of southeast Iran. No local and/or general indications of disease were observed in the vaccinated dogs immediately after vaccination. The efficacy of the vaccine was evaluated after 24 months (4 sandfly transmission seasons) by serological, parasitological analyses and clinical examination. In western blot analysis of antibodies to L. infantum antigens, sera from 10 out of 31 (32.2%) unvaccinated dogs, but none of the sera from vaccinated dogs which were seropositive at >100, recognized the 21 kDa antigen of L. infantum wild-type (WT). Nine out of 31 (29%) unvaccinated dogs, but none of vaccinated dogs, were positive for the presence of Leishmania DNA. One out of 46 (2.2%) vaccinated dogs and 9 out of 31 (29%) unvaccinated dogs developed clinical signs of disease. These results suggest that gentamicin-attenuated L. infantum induced a significant and strong protective effect against canine visceral leishmaniosis in the endemic area