Related papers: A Model Predictive Control Approach to Dual-Axis A…
Introduction. This article deals with the optimization of the energy conversion of a grid-connected photovoltaic system. The novelty is to develop an intelligent maximum power point tracking technique using artificial neural network…
This paper presents a Model Predictive Control (MPC) scheme for flight scheduling and energy management of electric aviation networks, where electric aircraft transport passengers between electrified airports equipped with sustainable…
One of the important technologies in renewable energy is the Photovoltaic (PV), which is the direct conversion of light into electricity at the atomic level, and improving the efficiency of PV systems is one of the very important factors in…
This paper presents a control strategy for sun trackers which adapts continuously to different sources of error, avoiding the necessity of any kind of calibration by analyzing the produced electric power to sense the position of the Sun.…
This paper presents a distributionally robust model predictive control (DRMPC) framework for the optimal Virtual Power Plant (VPP) operation under electricity price uncertainty. A unified VPP model is formulated that captures the…
This article explores the optimization of plant characteristics and controller parameters for electrified mobility. Electrification of mobile transportation systems, such as automobiles and aircraft, presents the ability to improve key…
Next-generation power grids will likely enable concurrent service for residences and plug-in electric vehicles (PEVs). While the residence power demand profile is known and thus can be considered inelastic, the PEVs' power demand is only…
An important parameter that affects PV panel performance of a solar power system is the incident solar radiation with the panel or panel's area of exposure to the sun. The direction and tilt angle of a PV panel are two important factors in…
We propose a robust nonlinear model predictive control (MPC) scheme for trajectory-tracking control of autonomous vehicles at the limits of handling on non-planar road surfaces. We derive the dynamics from first principles and selectively…
This paper proposes a Model Predictive Control (MPC) algorithm for target tracking amongst static and dynamic obstacles. Our main contribution lies in improving the computational tractability and reliability of the underlying non-convex…
The hybridization process has recently touched also the world of agricultural vehicles. Within this context, we develop an Energy Management Strategy (EMS) aiming at optimizing fuel consumption, while maintaining the battery state of…
Power-to-gas (P2G) can be employed to balance renewable generation because of its feasibility to operate at fluctuating loading power. The fluctuating operation of low-temperature P2G loads can be achieved by controlling the electrolysis…
Robots and automated systems are increasingly being introduced to unknown and dynamic environments where they are required to handle disturbances, unmodeled dynamics, and parametric uncertainties. Robust and adaptive control strategies are…
Model Predictive Control (MPC) is a versatile approach capable of accommodating diverse control requirements that holds significant promise for a broad spectrum of industrial applications. Noteworthy challenges associated with MPC include…
A conventional way to handle model predictive control (MPC) problems distributedly is to solve them via dual decomposition and gradient ascent. However, at each time-step, it might not be feasible to wait for the dual algorithm to converge.…
Model Predictive Control (MPC) has shown to be a successful method for many applications that require control. Especially in the presence of prediction uncertainty, various types of MPC offer robust or efficient control system behavior. For…
The photovoltaic system uses the photovoltaic array as a source of electrical power for the direct conversion of the sun radiation to direct current without any environmental hazards. The main purpose of this research is to design a…
Kinodynamic planning for car-like vehicles on uneven terrain requires simultaneously optimizing competing objectives such as path efficiency and pose stability. This work presents an adaptive epsilon-constraint method integrated into a…
Model Predictive Control (MPC) method is a class of advanced control techniques most widely applied in industry. The major advantages of the MPC are its straightforward procedure which can be applied for both linear and nonlinear system.…
The aim of this work is to control the longitudinal position of an autonomous vehicle with an internal combustion engine. The powertrain has an inherent dead-time characteristic and constraints on physical states apply since the vehicle is…