相关论文: Fuzzy Based Stator Flux Optimizer Design For Direc…
This paper proposes a novel load frequency control (LFC) approach formulated on an optimal structure of the coefficient diagram method (CDM) in a two-area thermal power system. As part of a realistic analysis, nonlinearities related to…
Dynamic Traffic Assignment (DTA) provides an approach to determine the optimal path and/or departure time based on the transportation network characteristics and user behavior (e.g., selfish or social). In the literature, most of the…
This paper proposes a method for direct torque control of Brushless DC (BLDC) motors. Evaluating the trapezium of back-EMF is needed, and is done via a sliding mode observer employing just one measurement of stator current. The effect of…
Self-optimizing control is a strategy for selecting controlled variables, where the economic objective guides the selection and design of controlled variables, with the expectation that maintaining the controlled variables at constant…
Directed fuzzing is a dynamic testing technique that focuses exploration on specific, pre targeted program locations. Like other types of fuzzers, directed fuzzers are most effective when maximizing testing speed and precision. To this end,…
This paper describes a PI fuzzy adaptive control structure. Based on classical PI fuzzy control structure (with integration on controller output) the authors have developed, implemented and validated an on-line self-tuning mechanism of…
In the interconnected power system network, instability problems are caused mainly by the low frequency oscillations of 0.2 to 2.5 Hz. The supplementary control signal in addition with AVR and high gain excitation systems are provided by…
In this paper, we introduce Voltage Phasor Control (VPC), also known as Phasor Based Control, as a novel way of implementing Optimal Power Flow (OPF). Unlike conventional OPF, in which the power flow optimization broadcasts power…
This research proposes a robust adaptive fuzzy sliding mode control (AFSMC) approach to enhance the trajectory tracking performance of cylindrical robotic manipulators, extensively utilized in applications such as CNC and 3D printing. The…
This article proposes an improved trajectory optimization approach for stochastic optimal control of dynamical systems affected by measurement noise by combining optimal control with maximum likelihood techniques to improve the reduction of…
This paper proposed a straightforward and efficient current control solution for induction machines employing deep symbolic regression (DSR). The proposed DSR-based control design offers a simple yet highly effective approach by creating an…
Adaptive fuzzy control strategies are established to achieve global prescribed performance with prescribed-time convergence for strict-feedback systems with mismatched uncertainties and unknown nonlinearities. Firstly, to quantify the…
Active flow control is applied to improve the aerodynamic performance of a NACA0018 airfoil operating as a single-bladed vertical axis wind turbine (VAWT). Results computed by wall-resolved large-eddy simulations (LES) highlight the…
An optimization model of a stormwater pond is developed to improve the performance of the system in terms of water quantity and quality. Nowadays, stormwater management systems play an important role in mitigating the impacts of…
Controllable and realistic traffic simulation is critical for developing and verifying autonomous vehicles. Typical heuristic-based traffic models offer flexible control to make vehicles follow specific trajectories and traffic rules. On…
Transportation is a major contributor to CO2 emissions, making it essential to optimize traffic networks to reduce energy-related emissions. This paper presents a novel approach to traffic network control using Differentiable Predictive…
The method for controlling a DC-DC converter is proposed to ensures the high quality control at large fluctuations in load currents by using differential gain control coefficients and second derivative control. Various implementations of…
The validation of autonomous driving systems benefits greatly from the ability to generate scenarios that are both realistic and precisely controllable. Conventional approaches, such as real-world test drives, are not only expensive but…
We consider stochastic optimal control of linear dynamical systems with additive non-Gaussian disturbance. We propose a novel, sampling-free approach, based on Fourier transformations and convex optimization, to cast the stochastic optimal…
This paper proposes a data-driven control framework to regulate an unknown, stochastic linear dynamical system to the solution of a (stochastic) convex optimization problem. Despite the centrality of this problem, most of the available…