Control
Fariba Nobakht; Hussein Eliasi
Abstract
This paper proposes a robust adaptive control strategy based on integral backstepping for nonlinear epidemic systems under input saturation, model uncertainties, and external disturbances. The proposed method combines backstepping for systematic control design, sliding mode control for robustness, and ...
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This paper proposes a robust adaptive control strategy based on integral backstepping for nonlinear epidemic systems under input saturation, model uncertainties, and external disturbances. The proposed method combines backstepping for systematic control design, sliding mode control for robustness, and adaptive control to handle unknown parameters dynamically. To address input saturation, a novel auxiliary design system combined with Nussbaum gain functions is introduced, mitigating saturation effects and ensuring stability. The epidemic dynamics are modeled using the SEIAR framework, which includes Susceptible, Exposed, Infected, Asymptomatic, and Recovered populations. The controller employs three control inputs—vaccination, social distancing measures, and treatment of infected individuals—to drive the populations of susceptible, exposed, and infected individuals to zero. Simulation results demonstrate that the proposed control scheme effectively eliminates infections, ensuring that the recovered population converges to the total population size. The method provides precise trajectory tracking despite input constraints and external disturbances. These findings highlight its strong potential for real-world epidemic management, particularly in resource-limited and uncertain environments.
Control
Hossein Zahmatkesh; Hussein Eliasi
Abstract
State estimation of nuclear reactors often plays a crucial role in accomplishing load-following control. This study presents a novel approach that leverages a weighted particle filter to address the challenges associated with estimating these crucial parameters, including relative precursor concentration ...
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State estimation of nuclear reactors often plays a crucial role in accomplishing load-following control. This study presents a novel approach that leverages a weighted particle filter to address the challenges associated with estimating these crucial parameters, including relative precursor concentration (C_r) and fuel temperature (T_f), under varying reactor power conditions. A high-fidelity nonlinear dynamic reactor model was developed, incorporating noises in both process and measurement models. The proposed method was evaluated by extensive simulations under a wide range of operational scenarios. The particle filter demonstrated exceptional performance in tracking the time-varying states of the nuclear reactor. Comparative analysis with a conventional Kalman filter and the extended Kalman filter revealed the superior robustness of the particle filter in handling nonlinearities inherent in nuclear systems. The proposed approach offers several advantages, including the ability to capture multimodal distributions, handle non-Gaussian noise, and provide probabilistic estimates. Despite the increased computational cost associated with particle filtering, the benefits in terms of estimation accuracy and reliability justify its application in nuclear power plant monitoring and control systems.
Control
Salehe Afsharian; Hussein Eliasi
Abstract
This article aims to derive new sufficient conditions to guarantee the stability of piecewise affine systems with time-varying delay (PWA-TVD). The set of delay-dependent linear matrix inequality (LMI) describes the novel stability criteria. This approach considers the PWA-TVD system with a time-delayed ...
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This article aims to derive new sufficient conditions to guarantee the stability of piecewise affine systems with time-varying delay (PWA-TVD). The set of delay-dependent linear matrix inequality (LMI) describes the novel stability criteria. This approach considers the PWA-TVD system with a time-delayed state-dependent switching signal. The newly suggested Lyapunov-Krasovskii functional (L-K-F) and improved estimation of its derivative have a crucial role in decreasing the complexity and conservatism of the proposed stability results. The suggested L-K-F belongs to the current and time-delayed states, the integral of the states over the time-varying delay, and time derivation of the states. A new inequality was used to obtain an upper bound (UB) for the time derivation of the Lyapunov functional. Then based on this UB, less conservative results are achieved. The theoretical results are applied to the numerical examples. The results confirm the effectiveness of the presented method. The conservative index is the maximum admissible UB of time delay.
Power systems
seyed hossein tabatabaei; Hussein Eliasi; Hamidreza Najafi; alireza jalilian
Abstract
A hybrid AC-DC microgrid consists of an AC and a DC subgrid that are connected to each other through an interlinking converter (IC). The main function of an IC under islanded conditions is to transfer power between the two subgrids. In this paper, a scheme is presented to reduce the voltage unbalance ...
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A hybrid AC-DC microgrid consists of an AC and a DC subgrid that are connected to each other through an interlinking converter (IC). The main function of an IC under islanded conditions is to transfer power between the two subgrids. In this paper, a scheme is presented to reduce the voltage unbalance factor in a hybrid AC-DC microgrid by using the free capacity of the IC. The free capacity of this converter is determined based on the current passing through each leg, and the amount of voltage unbalance compensation on the AC side of the microgrid is then obtained. The reference current of voltage unbalance compensation is calculated by using the positive, negative, and zero sequence components of the voltage of IC terminals. The total reference current is obtained by adding the reference current of voltage unbalance compensation and the current calculated for power transfer. Furthermore, a proportional-resonant (PR) controller is used in the control system of the four-leg inverter. Therefore, the reference current is properly tracked by the power stage of the inverter. Simulation results verify the accuracy of the proposed scheme under different conditions.
Control
Saeed Rahmati; Hussein Eliasi
Abstract
This paper presents a robust decentralized model predictive control scheme for a class of discrete-time interconnected systems subject to state and input constraints. Each subsystem is composed of a nominal LTI part and an additive time-varying perturbation function which presents the interconnections ...
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This paper presents a robust decentralized model predictive control scheme for a class of discrete-time interconnected systems subject to state and input constraints. Each subsystem is composed of a nominal LTI part and an additive time-varying perturbation function which presents the interconnections and is generally uncertain and nonlinear, but it satisfies a quadratic bound. Using the dual-mode MPC stability theory and Lyapunov theory for discrete-time systems, a sufficient condition is constructed for synthesizing the decentralized MPC’s stabilizing components; i.e. the local terminal cost function and the corresponding terminal set. To guarantee robust asymptotic stability, sufficient conditions for designing MPC stabilizing components are characterized in the form of an LMI optimization problem. The proposed control approach is applied to a system composed of five coupled inverted pendulums, which is a typical interconnected system, in a decentralized fashion. Simulation results show that the proposed robust MPC scheme is quite effective and has a remarkable performance.
