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.

Model Reference Control Applied on the Velocity Tracking of a Welding Robot

Robotics has been expanding over last decades, employed mainly to the activities that are most harmful to human beings. Considering that welding is one of the most risky activities in industries, studies and researches in the process automation are quite important. In this context, this work contributes to the control of the velocity tracking of the displacement of a linear welding robot. The mathematical modelling of the robot is presented, and the chosen control technique is Model Reference Control, which allows project controller based on the desired behaviour for the robot. To corroborate controller effectiveness, simulation and experimental results are presented and discussed, proving that proposed technique is adequate to control the robot velocity.

Model reference control in quasi-linear systems with a parametric feed-forward compensator and state-feedback stabilization controller

In this paper, a parametric feed-forward compensator and a parametric state-feedback stabilization controller are proposed for the model reference control to a class of quasi-linear systems. Quasi-linear systems are a special type of nonlinear systems whose coefficient matrices contain the state variables and also a time-varying parameter vector. The parametric state-feedback stabilization controller guarantees the stability of the closed-loop system and the parametric feed-forward compensator compensates the effect of the reference model state to the tracking error. The complete parametrization of the parametric feed-forward compensator is established based on a complete parametric solution to a class of generalized Sylvester matrix equations and solution of a coefficient matrix such that two matrix equations are satisfied. The established parametric state-feedback stabilization controller only needs a complete parametric solution to the same generalized Sylvester matrix equations but with different sets of freely designed parameters that represent the degrees of design freedom and may be further utilized to improve the system performance. A linear closed-loop form with the desired eigenstructure can be derived with the proposed parametric feed-forward compensator and parametric state-feedback stabilization controller, and a constant linear can even be obtained in certain cases. A numerical example and the application in spacecraft rendezvous are provided to illustrate the effectiveness of the proposed approach.

Wireless stepper motor control and optimization based on robust control theory

<img src="blob:http://ijra.iaescore.com/f3842e5c-715f-4e1e-9ffd-952309f5ccb1" alt="" />Stepper motors are broadly utilized in actual systems, which are marked by non-linear parameters such as internal, external noises and uncertainties from wireless network. As well, a suitable controller is required when the problem is to track the target signal. In this paper, robust controller based on model reference are investigated to wireless control and optimize position and time in stepper motors. The core impression to build a robust controller is to use a model reference control system. Furthermore, simulations are implemented to control stepper motor position and time in two cases: first, when the wireless network without any delay and packet dropout. Second, uncertain equations when the wireless network with time delays and packet dropout. Simulation results demonstrate that proposed controller has achieved and enhanced the performance in tracking and robustness.

Neural Network-Based Model Reference Control of Braking Electric Vehicles

The problem of energy recovery in braking of an electric vehicle is solved here, which ensures high quality blended deceleration using electrical and friction brakes. A model reference controller is offered, capable to meet the conflicting requirements of intensive and gradual braking scenarios at changing road surfaces. In this study, the neural network controller provides torque gradient control without a tire model, resulting in the return of maximal energy to the hybrid energy storage during braking. The torque allocation algorithm determines how to share the driver’s request between the friction and electrical brakes in such a way as to enable regeneration for all braking modes, except when the battery state of charge and voltage levels are saturated, and a solo friction brake has to be used. The simulation demonstrates the effectiveness of the proposed coupled two-layer neural network capable of capturing various dynamic behaviors that could not be included in the simplified physics-based model. A comparison of the simulation and experimental results demonstrates that the velocity, slip, and torque responses confirm the proper car performance, while the system successfully copes with the strong nonlinearity and instability of the vehicle dynamics.