Electrical Engineering And Systems Science

We consider a novel, analytical queuing model for vehicle coordination at signal-free intersections. Vehicles arrive at an intersection according to Poisson processes, and the crossing times are constants dependent of vehicle types. We use this model to quantitatively relate key operational parameters (vehicle speed/acceleration, inter-vehicle headway) to key performance metrics (throughput and delay) under the first-come-first-serve rule. We use the Foster-Lyapunov drift condition to obtain stability criteria and an upper bound for average time delay. Based on these results, we compare the efficiency of signal free intersections with conventional vehicles and with connected and autonomous vehicles. We also validate our results in Simulation of Urban Mobility (SUMO).

This paper considers a particular parameter estimator for switched systems and analyzes its properties. The estimator in question is defined as the map from the data set to the solution set of an optimization problem where the to-be-optimized cost function is a sum of pointwise infima over a finite set of sub-functions. This is a hard nonconvex problem. The paper studies some fundamental properties of this problem such as uniqueness of the solution or boundedness of the estimation error regardless of computational considerations. The interest of the analysis is to lay out the main influential properties of the data on the performance of this (ideal) estimator.

The Fifth Generation (5G) New Radio (NR) does not support data transmission during the random access (RA) procedures, which results in unnecessary control signalling overhead and power consumption, especially for small data transmission. Motivated by this, we propose two new RA schemes based on the existing grant-based (4-step) and grant-free (2-step B) RA schemes, which are NR Early Data Transmission (NR EDT) and 2-step A RA schemes, with the aim to enable data transmission during RA procedures in Radio Resource Control (RRC) Inactive state. To compare our proposed schemes with the benchmark schemes, we provide a spatio-temporal analytical framework to evaluate the RA schemes, which jointly models the preamble detection, Physical Uplink Shared Channel (PUSCH) decoding, and data transmission procedures. Based on this analytical model, we derive the analytical expressions for the overall packet transmission success probability of four RA schemes in each time slot. We also derive the throughput and the average energy consumption for a successful packet transmission of each scheme. Our results show that the 2-step A and 2-step B RA schemes provide the highest overall packet transmission success probability, the 2-step A RA scheme provides the lowest average energy consumption in low device intensity scenario, and 2-step B RA provides the lowest average energy consumption in high device intensity scenario.

In this paper, we focus on the rendezvous problem for the autonomous cooperative landing of an unmanned aerial vehicle (UAV) on an unmanned surface vehicle (USV). These heterogeneous agents with nonlinear dynamics are dynamically decoupled but share a common cooperative rendezvous task. The underlying control scheme is based on the Distributed Model Predictive Control (MPC). One of our main contributions is a rendezvous algorithm with an online update rule of the rendezvous location. The algorithm requires that agents update the rendezvous location only when they are not guaranteed to reach it. Therefore, the exchange of information occurs aperiodically and proposed algorithm improves the communication efficiency. Furthermore, we prove the recursive feasibility of the algorithm. The simulation results show the effectiveness of our algorithm applied to the problem of autonomous cooperative landing.

In this paper, a novel machine learning derived control performance assesment (CPA) classification system is proposed. It is dedicated for a class of PID-based control loops with processes exhibiting second order plus delay time (SOPDT) dynamical properties. The proposed concept is based on deriving and combining a number of different, diverse control performance indices (CPIs) that separately do not provide sufficient information about the control performance. However, when combined together and used as discriminative features of the assessed control system, they can provide consistent and accurate CPA information. This concept is discussed in terms of the introduced extended set of CPIs, comprehensive performance assessment of different machine learning based classification methods and practical applicability of the suggested solution. The latter is shown and verified by practical application of the proposed approach to a CPA system for a laboratory heat exchange and ditribution setup.

The persistent depletion of fossil fuels has encouraged mankind to look for alternatives fuels that are renewable and environment-friendly. One of the promising and renewable alternatives to fossil fuels is bio-diesel produced by means of the batch transesterification process. Control of the batch transesterification process is difficult due to its complex and non-linear dynamics. It is expected that some of these challenges can be addressed by developing control strategies that directly interact with the process and learning from the experiences. To achieve the same, this study explores the feasibility of reinforcement learning (RL) based control of the batch transesterification process. In particular, the present study exploits the application of twin delayed deep deterministic policy gradient (TD3) based RL for the continuous control of the batch transesterification process. These results showcase that TD3 based controller is able to control batch transesterification process and can be a promising direction towards the goal of artificial intelligence-based control in process industries.

The promising increase of Electric Vehicles (EVs) in our society poses a challenging problem on the impact assessment of their charging/discharging to power distribution grids. This paper addresses the assessment problem in a framework of nonlinear differential equations. Specifically, we address the nonlinear ODE (Ordinary Differential Equation) model for representing the spatial profile of voltage phasor along a distribution feeder, which has been recently introduced in literature. The assessment problem is then formulated as a two-point boundary value problem of the nonlinear ODE model. In this paper we derive an asymptotic charcterisation of solutions of the problem through the standard regular perturbation method. This provides a mathematically-rigor and quantitative method for assessing how the charging/discharging of EVs affects the spatial profile of distribution voltage. Effectiveness of the asymptotic charcterisation is established with simulations of both simple and practical configurations of the power distribution grid.

This paper investigates a resilient distributed Nash equilibrium (NE) seeking problem on a directed communication network subject to malicious cyber-attacks. The considered attacks, named as Denial-of-Service (DoS) attacks, are allowed to occur aperiodically, which refers to interruptions of communication channels carried out by intelligent adversaries. In such an insecure network environment, the existence of cyber-attacks may result in undesirable performance degradations or even the failures of distributed algorithm to seek the NE of noncooperative games. Hence, the aforementioned setting can improve the practical relevance of the problem to be addressed and meanwhile, it poses some technical challenges to the distributed algorithm design and exponential convergence analysis. In contrast to the existing distributed NE seeking results over a prefect communication network, an attack-resilient distributed algorithm is presented such that the NE can be exactly reached with an exponential convergence rate in the presence of DoS attacks. Inspired by the previous works in [21]-[26], an explicit analysis of the attack frequency and duration is investigated to enable exponential NE seeking with resilience against attacks.Examples and numerical simulation results are given to show the effectiveness of the proposed design.

Agile attitude maneuvering maximizes the utility of remote sensing satellite constellations. By taking into account a satellite's physical properties and its actuator specifications, we may leverage the full performance potential of the attitude control system to conduct agile remote sensing beyond conventional slew-and-stabilize maneuvers. Employing a constellation of agile satellites, coordinated by an autonomous and responsive scheduler, can significantly increase overall response rate, revisit time and global coverage for the mission. In this paper, we use recent advances in sequential convex programming based trajectory optimization to enable rapid-target acquisition, pointing and tracking capabilities for a scheduler-based constellation. We present two problem formulations. The Minimum-Time Slew Optimal Control Problem determines the minimum time, required energy, and optimal trajectory to slew between any two orientations given nonlinear quaternion kinematics, gyrostat and actuator dynamics, and state/input constraints. By gridding the space of 3D rotations and efficiently solving this problem on the grid, we produce lookup tables or parametric fits off-line that can then be used on-line by a scheduler to compute accurate estimates of minimum-time and maneuver energy for real-time constellation scheduling. The Minimum-Effort Multi-Target Pointing Optimal Control Problem is used on-line by each satellite to produce continuous attitude-state and control-input trajectories that realize a given schedule while minimizing attitude error and control effort. The optimal trajectory may then be achieved by a low-level tracking controller. We demonstrate our approach with an example of a reference satellite in Sun-synchronous orbit passing over globally-distributed, Earth-observation targets.

The human insulin-glucose metabolism is a time-varying process, which is partly caused by the changing insulin sensitivity of the body. This insulin sensitivity follows a circadian rhythm and its effects should be anticipated by any automated insulin delivery system. This paper presents an extension of our previous work on automated insulin delivery by developing a controller suitable for humans with Type 1 Diabetes Mellitus. Furthermore, we enhance the controller with a new kernel function for the Gaussian Process and deal with noisy measurements, as well as, the noisy training data for the Gaussian Process, arising therefrom. This enables us to move the proposed control algorithm, a combination of Model Predictive Controller and a Gaussian Process, closer towards clinical application. Simulation results on the University of Virginia/Padova FDA-accepted metabolic simulator are presented for a meal schedule with random carbohydrate sizes and random times of carbohydrate uptake to show the performance of the proposed control scheme.