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.

Joint Estimation of SOC and Available Capacity of Power Lithium-Ion Battery

Temperature has an important effect on the battery model. A dual-polarization equivalent circuit model considering temperature is established to quantify the effect of temperature, and the initial parameters of the model are identified through experiments. To solve the defect of preset noise, the H-infinity filter algorithm is used to replace the traditional extended Kalman filter algorithm, without assuming that the process noise and measurement noise obey Gaussian distribution. To eliminate the influence of battery aging on SOC estimation, and considering the different time-varying characteristics of the battery states and parameters, the dual time scale double H-infinity filter is used to jointly estimate the revised SOC and available capacity. The simulation results at two temperatures show that, compared with the single time scale, the double time scale double H-infinity filter reduces the simulation time by nearly 90% under the premise that the accuracy is almost unchanged, which proves that the proposed joint estimation algorithm has the dual advantages of high precision and high efficiency.

Finite-Horizon H-Infinity Control for Time-Varying State-Saturated Systems under Stochastic Communication Protocol

In this paper, the H∞ control problem is investigated for a class of time-varying state-saturated systems with both stochastic parameters and nonlinearities under the stochastic communication protocol (SCP). The sensor-to-controller network is considered where only one sensor can obtain access to the communication network at each transmission instant. The SCP is adopted to mitigate the undesirable data collision phenomenon. The model transformation technique is employed to simplify the addressed problem, and then, the completing squares method is carried out to obtain a sufficient condition for the existence of the finite-horizon H∞ controller. The controller parameters are characterized by solving two coupled backward recursive Riccati-like difference equations. A simulation example is finally utilized to illustrate the effectiveness of the proposed controller design scheme.

Multi-model structured H-infinity design of a robust and aggressive turbofan jet engine controller

Jet engine control comprises tracking either the fan speed or engine pressure ratio setpoints. Further, safe operation entails maintaining several additional parameters, such as high-pressure turbine temperature, combustor pressure, core shaft acceleration and other ones within prescribed limits. A Min-Max selector that features PI controllers is frequently used to handle these requirements. However, this arrangement is overly conservative in the limits management, which unnecessarily slows down the engine response. To overcome this shortcoming, a new controller that adopts the traditional Min-Max structure in combination with the Ndot control, the Conditionally Active and the Conditioning Technique approaches is developed. PI regulators are replaced by dynamic output feedback controllers, which are designed according to a multi-model structured H-infinity methodology. This approach makes it possible to marry robustness with performance, which are two conflicting objectives. Singular value analysis tools demonstrate the robustness of the resulting design. Linear and nonlinear simulations indicate that the proposed controller optimizes the engine response time under the constraint of keeping a set of parameters within prescribed bounds. The features of the proposed design are lucrative for actual implementation in the industry.