Control of the COVID-19 System by Vaccination Using a New Control Engineering Technique

In this work, a novel control engineering method is proposed to achieve a control strategy by vaccination for the COVID-19 epidemic. A proper mathematical model with vaccination control is developed for the COVID-19 system based on the Susceptible-Exposed-Infectious-Recovered (SEIR) epidemiological model after conducting some analyses and assumptions that reflect the COVID-19 features. Then, the proposed control law is designed using the feedback linearization approach and the H-infinity control framework. In addition, a model reference control is incorporated to ensure that satisfactory time responses are obtained. The Black Hole Optimization (BHO) technique is used to attain the optimality of the proposed control method. Following that, the reported statistics and vaccination plan of the Lombardy region of Italy are utilized to assess the effectiveness of the proposed control law. Ultimately, the simulation results illustrate that the proposed control law can effectively control the COVID-19 system and correctly perform the vaccination plan by tackling the system’s nonlinearity and uncertainty and realizing elegant asymptotic tracking characteristics with reasonable control effort.

Assessment of α-Cypermethrin Pour-On Application and Diminazene Aceturate for Treating Trypanosome-Related Diseases Caused by Tsetse Flies on Cattle in Mô, Togo

The effects of tsetse-transmitted trypanosomosis control in high tsetse flies (Glossina spp.) challenge and trypanocidal drug resistance settings remain poorly understood in Togo owing to poor data coverage on the current disease impact. From March 2014 to November 2017, a database of zoo-sanitary surveys integrating the evolution of disease incidence and intervention coverage made it possible to quantify the apparent effects attributable to the control effort, focused on all sedentary cattle breeds in the 1,000 km² area of Mô in Togo. The strategy involved an initial phase with cross-sectional entomological and parasitological. Then, three times a year, 20% of the bovine animals of the study area received α-cypermethrin pour-on, and infected cattle with poor health (798 cattle in 2014 and 358 in 2017) were individually given diminazene aceturate at 7 mg/kg of body weight. The tsetse density in the area decreased significantly, from 1.78 ± 0.37 in March 2014 before the α-cypermethrin application to 0.48 ± 0.07 in February 2017. The α-cypermethrin pour-on application and diminazene aceturate treatment of cattle led to the largest reduction in disease incidence, from 28.1% in 2014 to 7.8% in 2017, an improvement in hematocrit from 24.27 ± 4.9% to 27.5 ± 4.6%, and a reduction in calf mortality from 15.9 ± 11% to 5.9%. Improved access to these interventions for different types of livestock and maintaining their effectiveness, despite high tsetse (Diptera: Glossinidae) challenges, should be the primary focus of control strategies in many areas of Togo.

Real-time Trajectory Planning for Automated Vehicle Safety and Performance in Dynamic Environments

Safe trajectory planning for high-performance automated vehicles in an environment with both static and moving obstacles is a challenging problem. Part of the challenge is developing a formulation that can be solved in real-time while including the following set of specifications: minimum time-to-goal, a dynamic vehicle model, minimum control effort, both static and moving obstacle avoidance, simultaneous optimization of speed and steering, and a short execution horizon. This paper presents a nonlinear model predictive control-based trajectory planning formulation, tailored for a large, high-speed unmanned ground vehicle, that includes the above set of specifications. The ability to solve this formulation in real-time is evaluated using NLOptControl, an open-source, direct-collocation based, optimal control problem solver in conjunction with the KNITRO nonlinear programming problem solver. The formulation is tested with various sets of the specifications. A parametric study relating execution horizon and obstacle speed indicates that the moving obstacle avoidance specification is not needed for safety when the planner has a small execution horizon and the obstacles are moving slowly. However, a moving obstacle avoidance specification is needed when the obstacles are moving faster, and this specification improves the overall safety without, in most cases, increasing the solve-times. The results indicate that (i) safe trajectory planners for high-performance automated vehicles should include the entire set of specifications mentioned above, unless a static or low-speed environment permits a less comprehensive planner; and (ii) the resulting formulation can be solved in real-time.

Path Following Control for Miniature Fixed Wing Unmanned Aerial Vehicle Under Uncertainties and Disturbances：A Two-Layered Framework

In this paper a solution to the path following control problem for miniature fixed wing unmanned aerial vehicle (MAV) in the presence of inaccuracy modelling parameters and environmental disturbances is presented. We introduce a two-layered framework to collaborate guidance level with control level. A modified vector fields based path following methodology is proposed in the kinematics phase to track a Dubins path with straight line segments and circle ones. Then a Proportional-Integral-Derivative (PID) controller based on feedback linearization and gain scheduling techniques is designed such that the MAV can reject nonlinear dynamics, system uncertainties and disturbances by using a robust fuzzy control scheme. Eventually, by giving comparison test with control effort and track error as assessment metrics, both the practicality of the framework and the outperformance of the proposed algorithm are well demonstrated.

Fishing for Feral Cats in a Naturally Fragmented Rocky Landscape Using Movement Data

Feral cats are one of the most damaging predators on Earth. They can be found throughout most of Australia’s mainland and many of its larger islands, where they are adaptable predators responsible for the decline and extinction of many species of native fauna. Managing feral cat populations to mitigate their impacts is a conservation priority. Control strategies can be better informed by knowledge of the locations that cats frequent the most. However, this information is rarely captured at the population level and therefore requires modelling based on observations of a sample of individuals. Here, we use movement data from collared feral cats to estimate home range sizes by gender and create species distribution models in the Pilbara bioregion of Western Australia. Home ranges were estimated using dynamic Brownian bridge movement models and split into 50% and 95% utilisation distribution contours. Species distribution models used points intersecting with the 50% utilisation contours and thinned by spacing points 500 m apart to remove sampling bias. Male cat home ranges were between 5 km2 (50% utilisation) and 34 km2 (95% utilisation), which were approximately twice the size of the female cats studied (2–17 km2). Species distribution modelling revealed a preference for low-lying riparian habitats with highly productive vegetation cover and a tendency to avoid newly burnt areas and topographically complex, rocky landscapes. Conservation management can benefit by targeting control effort in preferential habitat.

Robust attitude control of the three-dimensional unknown chaotic satellite system

This paper proposes a novel continuous-time robust direct adaptive controller for the attitude control of the three-dimensional unknown chaotic spacecraft system. It considers that the plant’s nonlinear terms, exogenous disturbances, and model uncertainties are unknown and bounded; the controller design is independent of the system’s nonlinear terms. These controller attributes flourish the robust performance of the closed-loop and establish smooth state vector convergence to zero. The proposed controller consists of three parts: (1) a linear controller establishes the stability of the closed-loop at the origin, (2) a nonlinear controller component that autonomously adjusts the feedback gain, and (3) a nonlinear adaptive controller compensates for the model uncertainties and external disturbances using the online estimates of bounds and model uncertainties. The output of this part remains within a given upper and lower bound. The feedback controller gain is large when the state variables are away from the origin and become small in the origin’s vicinity. This feature is novel and contributes to the synthesis of smooth control effort that establishes robust fast and oscillation-free convergence of the state variables to zero. The Lyapunov direct stability analysis assures the global asymptotic robust stability of the closed-loop. Computer simulations and comparative analysis are included to verify the theoretical findings.

Multi-Objective Swarm Intelligence Trajectory Generation for a 7 Degree of Freedom Robotic Manipulator

This work is aimed to demonstrate a multi-objective joint trajectory generation algorithm for a 7 degree of freedom (DoF) robotic manipulator using swarm intelligence (SI)—product of exponentials (PoE) combination. Given a priori knowledge of the end-effector Cartesian trajectory and obstacles in the workspace, the inverse kinematics problem is tackled by SI-PoE subject to multiple constraints. The algorithm is designed to satisfy finite jerk constraint on end-effector, avoid obstacles, and minimize control effort while tracking the Cartesian trajectory. The SI-PoE algorithm is compared with conventional inverse kinematics algorithms and standard particle swarm optimization (PSO). The joint trajectories produced by SI-PoE are experimentally tested on Sawyer 7 DoF robotic arm, and the resulting torque trajectories are compared.

A trim problem formulation for maximum control authority using the Attainable Moment Set geometry

This paper presents a generic trim problem formulation, in the form of a constrained optimization problem, which employs forces and moments due to the aircraft control surfaces as decision variables. The geometry of the Attainable Moment Set (AMS), i.e. the set of all control forces and moments attainable by the control surfaces, is used to define linear equality and inequality constraints for the control forces decision variables. Trim control forces and moments are mapped to control surface deflections at every solver iteration through a linear programming formulation of the direct Control Allocation algorithm. The methodology is applied to an innovative box-wing aircraft configuration with redundant control surfaces, which can partially decouple lift and pitch control, and allow direct lift control. Novel trim applications are presented to maximize control authority about the lift and pitch axes, and a “balanced” control authority. The latter can be intended as equivalent to the classic concept of minimum control effort. Control authority is defined on the basis of control forces and moments, and interpreted geometrically as a distance within the AMS. Results show that the method is able to capitalize on the angle of attack or the throttle setting to obtain the control surfaces deflections which maximize control authority in the assigned direction. More conventional trim applications for minimum total drag and for assigned angle of elevation are also explored.

Learning Neuroplastic Matching of Robot Dynamics in Closed-loop CPGs

Legged robots have the potential to show locomotion performance with reduced control effort and energy efficiency by leveraging elastic structures inspired by animals' elastic tendons and muscles. However, it remains a challenge to match the natural dynamics of complex legged robots and their control task dynamics. Here we present a framework to match control task dynamics and natural dynamics based on the neuroelasticity and neuroplasticity concept. Inspired by animals we design quadruped robot Morti with strong natural dynamics as a testing platform. It is controlled through a bioinspired closed-loop central pattern generator (CPG) that is designed to neuroelastically mitigate short term perturbations using sparse contact feedback. We use the amount of neuroelastic activity as a proxy to quantify the dynamics' mismatching. By minimizing neuroelastic activity, we neuroplastically match the control task dynamics to the robot's natural dynamics. Through matching the robot learns to walk within one hour with only sparse feedback and improves its energy efficiency without explicitly minimizing it in the cost function.

Energy-Aware Controllability of Complex Networks

Understanding the fundamental principles and limitations of controlling complex networks is of paramount importance across natural, social, and engineering sciences. The classic notion of controllability does not capture the effort needed to control dynamical networks, and quantitative measures of controllability have been proposed to remedy this problem. This article presents an introductory overview of the practical (i.e., energy-related) aspects of controlling networks governed by linear dynamics. First, we introduce a class of energy-aware controllability metrics and discuss their properties. Then, we establish bounds on these metrics, which allow us to understand how the structure of the network impacts the control energy. Finally, we examine the problem of optimally selecting a set of control nodes so as to minimize the control effort, and compare the performance of some simple strategies to approximately solve this problem. Throughout the article, we include examples of structured and random networks to illustrate our results. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.