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About 10,000 papers for “Advanced Control Systems Optimization”

Design and Control Co-Optimization for Advanced Vehicle Propulsion Systems

Jianning Zhao · theses.fr (ABES) · 2017

Des technologies avancées sont très demandées dans l'industrie automobile pour respecter les réglementations de consommation de carburant de plus en plus rigoureuses. La co-optimisation du dimensionnement et du contrôle des groupes motopropulseurs avec une efficacité de calcul améliorée est étudiée dans cette thèse.Les composants des groupes motopropulseurs, tels que le moteur, la batterie et le moteur électrique, sont modélisés analytiquement au niveau descriptif et prédictif afin de permettre une optimisation du contrôle rapide et une optimisation du dimensionnement scalable. La consommation d'énergie minimale des véhicules hybrides-électriques est évaluée par des nouvelles méthodes optimales. Ces méthodes – y compris Selective Hamiltonian Minimization et GRaphical-Analysis-Based energy Consumption Optimization – permettent d'évaluer une consommation minimale d'énergie avec une efficacité de calcul améliorée. De plus, la méthode de Fully-Analytic energy Consumption Evaluation (FACE) approxime la consommation d'énergie minimale sous forme analytique en fonction des caractéristiques de la mission et des paramètres de conception des composants du groupe motopropulseur. Plusieurs cas d’études sont présentées en détail par rapport aux approches de co-optimisation à bi-niveaux et à uni-niveau, ce qui montre une réduction efficace du temps de calcul requis par le processus global de co-optimisation.

Cited by 3Open access

Investigation of Advanced Engine Cooling Systems - Optimization and Nonlinear Control

Wang Tian-wei · TigerPrints (Clemson University) · 2016

Advanced automotive engine cooling systems can positively impact the performance, fuel economy, and reliability of internal combustion engines. A smart engine cooling system typically features multiple real time computer controlled actuators: a three way linear smart valve, a variable speed coolant pump, and electric radiator fan(s). In this dissertation, several innovative comprehensive nonlinear control and optimization operation strategies for the next generation smart cooling application will be analyzed. First, the optimal control has been investigated to minimize the electric energy usage of radiator fan matrix. A detailed mathematical model of the radiator fan(s) matrix operation and the forced convection heat transfer process was developed to establish a mixed integer nonlinear programming problem. An interior points approach was introduced to solve the energy consumption minimization problem. A series of laboratory tests have been conducted with different fan configurations and rotational shaft speed combinations, with the objective to cool a thermal loaded engine. Both the mathematical approach and the laboratory test results demonstrated the effectiveness of similar control strategies. Based on the tests data and mathematical analysis, an optimization control strategy reduced the fan matrix power consumption by up to 67%. Second, a series of experimental laboratory tests were implemented to investigate the contributions of each electro-mechanical device in automotive thermal management system. The test results established a basis for several key operating conclusions. The smart valve and variable speed pump impacted the engine temperature by adjusting the heat transfer rate between the engine and the radiator through coolant redirection and/or coolant flow rate. On the other hand, the radiator fan(s) operation affects the engine's temperature by modifying the heat rejection rate of the radiator which can influence the entire cooling system. In addition, the smart valve's operation changes the engine's temperature magnitude the greatest amount followed by the radiator fan(s) and the coolant pump. Furthermore, from a power consumption aspect, the radiator fan(s) consumes the most engine power in comparison to the two other actuators. Third, a Lyapunov based nonlinear control strategy for the radiator fan matrix was studied to accommodate transient engine temperature tracking at heavy heat load. A reduced order mathematical model established a basis for the closed-loop real time feedback system. Representative numerical and experimental tests demonstrated that the advanced control strategy can regulate the engine temperature tracking error within 0.12°C and compensate the unknown heat load. The nonlinear controller provided superior performance in terms of power consumption and temperature tracking as evident by the reduced magnitude when compared to a classical proportional integral with lookup table based controller and a bang bang controller. Fourth, a nonlinear adaptive multiple-input and multiple-output (NAMIMO) controller to operate the smart valve and radiator fans has been presented. This controller regulates the engine temperature while compensating for unknown wide range heat loads and ram air effects. A nonlinear adaptive backstepping (NAB) control strategy and a state flow (SF) control law were introduced for comparisons. The test results indicated that the NAMIMO successfully regulated the engine temperature to a desired value (tracking error, |e|<0.5°C, at steady state) subject to various working conditions. In contrast, the NAB control law consumes the least radiator fan power but demonstrated a larger average temperature tracking error (40% greater than the NAMIMO controller), a longer response time (34% greater than the NAMIMO controller), and defected when the heat load was low. Lastly, the SF controller, characterized by greater oscillation and electrical power consumption (18.9% greater than the NAMIMO controller), was easy to realize and maintained the engine temperature to within |e|<5°C. An important aspect of engineering research is the knowledge gained from learning materials to fully understand the thermal management. As part of the dissertation, advanced three-dimensional (3D) visualization and virtual reality (VR) technology based engineering education methods has been studied. A series of computer aided design (CAD) models with storyboards have been created to provide a step to step guide for developing the learning modules. The topics include automotive, aerospace, and manufacturing. The center for aviation and automotive technological education using virtual e-schools (CA2VES) at Clemson University has developed a comprehensive e-learning system integrated with eBooks, mini video lectures, 3D virtual reality technologies, and online assessments as supplementary materials to engineering education.

Cited by 2Open access

Optimization of energy efficiency in HVAC systems using advanced control strategies

Anyaegbu, Ifechukwu · Theseus (Ammattikorkeakoulujen) · 2024

The focus of this project was optimizing energy efficiency in industrial Heating, Ventilation, and Air Conditioning (HVAC) systems in Nigeria through advanced control strategies. With rising energy costs and environmental concerns, enhancing energy efficiency has become necessary, especially in developing countries like Nigeria. The project was aimed at evaluating existing control strategies and investigating advanced techniques such as model predictive control (MPC), artificial intelligence (AI) algorithms and identifying their profitability and marketability as better solutions that can significantly reduce energy consumption. The thesis was commissioned by Savonia.
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\nThe methodologies employed include a comprehensive literature review covering HVAC systems, energy consumption patterns, and control strategies. A comparative analysis of selected control strategies, focusing on their strengths, weaknesses, and applicability to the Nigerian context was made. Key performance indicators (KPIs) were established to evaluate energy efficiency, comfort levels, and cost-effectiveness.
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\nAs a result of the study, it can be said that the profitability of advanced control methods is influenced by several factors. It was found that each method offers several strengths, and each organisation may implement the most suitable option according to their needs and resources. The study discovered that the initial investment and requirements for implementing these advanced strategies might pose as potential obstacles for some organisations. However, it was found that the strengths and possibilities of improvement surpass the weaknesses.

Cited by 1Open access

Advanced color control for multicolor LED illumination systems with parametric optimization

Lukas Lohaus, Emanuel Leicht, Stefan Dietrich et al. · 2013

LED based luminaires are capable of providing innovative functionalities, such as color control and color temperature adaption. In fact, precise control of these two parameters has turned out to be very complex and challenging. This paper presents an advanced control technique for a very accurate and robust adjustment of both color and color temperature. Additionally, the proposed methodology enables system optimization of a LED based luminaire regarding various parameters, e.g. maximum color rendering index (CRI). The control signals for any PWM-switched LED driver can be tuned accordingly by the system controller. The algorithm is tested in simulations for three different luminaire realizations, namely RGB-W, RGB-A and RGB-CAW. The CIEDE2000 color difference formula is applied for computing the perceivable color difference ΔE by the human eye. The developed color control algorithm achieves a superior minimization of the absolute color distance to ΔE ≤ 0.21, which is not even perceivable by the human eye. This performance can be realized over a wide color temperature range from 3200K to 7500 K while luminous output is kept constant. Due to the applied optimization, a CRI of ≥ 90 can be maintained over the same color temperature range for all three scenarios. Simultaneously, the deviation of correlated color temperature ΔCCT is minimized to less than 50 K, which is a precision of more than 98 %. The novel algorithm considers strict hardware limitations, such as finite switching speed or limited computation resolution, to account for implementations on an ASIC or a μC.

Cited by 17

Advanced Control Scheme Optimization for Stand-Alone Photovoltaic Water Pumping Systems

Maissa Farhat, Óscar Barambones · Computation · 2024

This study introduces a novel method for controlling an autonomous photovoltaic pumping system by integrating a Maximum Power Point Tracking (MPPT) control scheme with variable structure Sliding Mode Control (SMC) alongside Perturb and Observe (P&O) algorithms. The stability of the proposed SMC method is rigorously analyzed using Lyapunov’s theory. Through simulation-based comparisons, the efficacy of the SMC controller is demonstrated against traditional P&O methods. Additionally, the SMC-based system is experimentally implemented in real time using dSPACE DSP1104, showcasing its robustness in the presence of internal and external disturbances. Robustness tests reveal that the SMC controller effectively tracks Maximum Power Points (MMPs) despite significant variations in load and solar irradiation, maintaining optimal performance even under challenging conditions. The results indicate that the SMC system can achieve up to a 70% increase in water flow rates compared with systems without MPPT controllers. Furthermore, SMC demonstrated high sensitivity to sudden changes in environmental conditions, ensuring efficient power extraction from the photovoltaic panels. This study highlights the advantages of integrating SMC into Photovoltaic Water Pumping Systems (PV-WPSs), providing enhanced control capabilities and optimizing system performance. The findings contribute to the development of sustainable water supply solutions, particularly in remote areas with limited access to the electrical grid.

Cited by 4Open access

Advanced control parameter optimization in DC motors and liquid level systems

Serdar Ekinci, Davut İzci, Mohammad H. Almomani et al. · Scientific Reports · 2025

In recent times, there has been notable progress in control systems across various industrial domains, necessitating effective management of dynamic systems for optimal functionality. A crucial research focus has emerged in optimizing control parameters to augment controller performance. Among the plethora of optimization algorithms, the mountain gazelle optimizer (MGO) stands out for its capacity to emulate the agile movements and behavioral strategies observed in mountain gazelles. This paper introduces a novel approach employing MGO to optimize control parameters in both a DC motor and three-tank liquid level systems. The fine-tuning of proportional-integral-derivative (PID) controller parameters using MGO achieves remarkable results, including a rise time of 0.0478 s, zero overshoot, and a settling time of 0.0841 s for the DC motor system. Similarly, the liquid level system demonstrates improved control with a rise time of 11.0424 s and a settling time of 60.6037 s. Comparative assessments with competitive algorithms, such as the grey wolf optimizer and particle swarm optimization, reveal MGO's superior performance. Furthermore, a new performance indicator, ZLG, is introduced to comprehensively evaluate control quality. The MGO-based approach consistently achieves lower ZLG values, showcasing its adaptability and robustness in dynamic system control and parameter optimization. By providing a dependable and efficient optimization methodology, this research contributes to advancing control systems, promoting stability, and enhancing efficiency across diverse industrial applications.

Cited by 12Open access

Advanced control system for reverse osmosis optimization in water reuse systems

Albert Galizia, Julian Mamo, Gaëtan Blandin et al. · Desalination · 2021

A main reverse osmosis (RO) limitation is the operational costs (including energy filtration costs), which are significantly affected by energy costs and the expense of membrane maintenance. Both costs are affected by influent characteristics, including feedwater temperatures (T), electrical conductivity (EC), and recovery set points. A fuzzy logic advanced control system optimizes the RO operational costs considering feedwater EC and T as input values, and the RO recovery setpoint as a control action. Verification results show that, once calibrated, the simulation of the control system can save more than one million euros per year in large RO facilities (>30.000m3·day-1). Which represents a reduction of 0.11€·m-3 of influent treated water.

Cited by 21Open access

An Adaptive Optimization Control Strategy For Advanced Engine Thermal Management Systems

Kai Li, Yu Zhang, Xin Liu et al. · 2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI) · 2021

An engine thermal management system is required to remove the waste heat from the engine’s combustion process to ensure high efficiency operation within a safe temperature range. In advanced engine thermal management systems (AETMS), electric actuators replace the traditional thermostat, mechanical coolant pump, and radiator fans to improve the temperature control accuracy and system efficiency. In the presence of unknown disturbances in the system, it is a challenge to coordinately control multiple actuators to ensure accurate temperature control and low system parasitic energy consumption. This paper develops an adaptive optimization control strategy composed of a parameter estimator, a tracking controller and a balance point optimization block. In addition, the stability of the proposed strategy is proven by Lyapunov’s stability theory. Finally, the co-simulation results between Matlab and AEMsim are presented to show the effectiveness of the proposed approach.

Cited by 2

Optimization of New Advanced Combustion Systems Using Real-Time Combustion Control

Rainer Leithgoeb, F. Henzinger, Alois Fuerhapter et al. · SAE technical papers on CD-ROM/SAE technical paper series · 2003

<div class="htmlview paragraph">Due to stricter fuel consumption and emissions requirements new gasoline engine concepts using advanced combustion systems such as fully variable valve train and controlled auto-ignition (HCCI) become important.</div> <div class="htmlview paragraph">Classical generic methods developed for system optimization with an increased number of variabilities (e.g. DoE - <u>D</u>esign <u>o</u>f <u>E</u>xperiments), need extensions, because closed loop control is required for critical system parameters like valve train actuation and combustion behavior.</div> <div class="htmlview paragraph">This paper describes a new approach called “controlled optimization”, which is an extension of DoE using real-time controllers for critical system parameters (e. g. combustion timing). This ensures stable engine operation and protection under all conditions. Less critical parameters are still used as simple DoE-set values. Combustion timing, directly related to spark advance in conventional gasoline engines, is also strongly influenced by valve timing parameters. Consequently this has a direct representation in the combustion controllers structure. ECU-structures for advanced combustion systems are still under development. Using “controlled optimization” leads to significant benefits in this area, because deep insight concerning parameter effects, interactions and controller structures can be obtained in a short time.</div>

Cited by 11

Advanced Modelling, Control and Energy Optimization of Railway Traction Power Supply Systems

Xing, Chen · White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York) · 2023

As part of the effort to achieve the Paris agreement climate goals, in June 2019, the UK government passed the legislation and set a stringent target aiming for net zero greenhouse gas (GHG) emissions by 2050, becoming the first major economy to take concrete steps to tackle the issue of climate change. It is gradually acknowledged that greenhouse gas removal (GGR) is an essential measure for the UK to meet its objectives of carbon emission reduction.
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\nAs reported, the total emissions of the UK in 2021 reached 447, where the surface transport sector accounted to 23%, which became the largest source of emissions in the UK. Amongst, personal road transport means emitted more carbon dioxide on average than other land transport modes, therefore shifting towards a higher share of public and mass transit will be essential. Railways are recognised as one of the most energy-efficient modes of transportation for both passengers and freight. However, in the UK, only 38% of the existing railway network has been electrified by year 2021. 
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\nIn this context, on the one hand, the electrification of the railway sector needs to be further promoted. On the other hand, there are still great potentials to enhance the energy efficiency for the existing electrified AC railway traction power supply system. Meanwhile, with significant penetration of renewable energy and integration of energy storage systems (ESS), it brings great opportunities to achieve railway net-zero, but also more challenges to the design, control, stability and reliability, and energy management of the railway power supply system. The trend moving from traditional railway power supply system to more efficient smart one has become predominant. 
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\nThe primary objective of this thesis is to explore various techniques that can be used to optimize energy efficiency in AC railway traction power supply systems. This research begins with a comprehensive review of the scope of railway traction power supply system and energy optimization in Chapter 2. Then, targeting at the raised challenges, in Chapter 3, a multi-objective optimization power compensation strategy based on the NSGA-II algorithm has been proposed for the railway co-phase power supply system with renewable energy integration, to reduce the system operation capacity while addressing the power quality issues. In Chapter 4, continuing to focus on the railway co-phase power supply system with ESS integration, a DQN-based thermal constrained energy management strategy has been proposed to achieve the peak load shaving and constrain the temperature increase for both the power electronics modules and ESS so as to enhance their reliability. In Chapter 5, for the train operation side, a bi-objective robust optimization model for the train speed trajectories has been formulated to minimize both the energy consumption and journey time, in which the robustness against the uncertain train mass is also considered and viewed as a decision maker (DM) preference. A novel multi-objective optimization
\nalgorithm namely p-NSGA-II has been proposed to handle the formulated problem, incorporating the original NSGA-II and a proposed preference dominance criterion. The energy optimization for the AC railway traction power supply system is investigated from both railway power supply side and train operation side. This includes the development of advanced optimization algorithms and techniques, the use of regenerative braking power, renewable energy and energy storage systems, and the optimization of train trajectory planning. The research also addresses the challenges associated with complex system modelling of the railway traction power supply system with renewable and ESS integration, system reliability and robustness enhancement, and power quality improvement. 
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\nOverall, this research presents a comprehensive framework on the modeling, control and energy optimization to support the development of efficient smart railway traction power supply systems with a transition from the legacy systems, and provides valuable insights into various key issues relating to the construction of sustainable railway transportation systems.

Cited by 0

Design, Control, and Optimization of Robots with Advanced Energy Regenerative Drive Systems

Poya Khalaf · EngagedScholarship @ Cleveland State University (Cleveland State University) · 2019

We investigate the control and optimization of robots with ultracapacitor based regenerative drive systems. A subset of the robot joints are conventional, in the sense that external power is used for actuation. Other joints are energetically self-contained passive systems that use ultracapacitors for energy storage. An electrical interconnection known as the star configuration is considered for the regenerative drives that allows for direct electric energy redistribution among joints, and enables higher energy utilization efficiencies. A semi-active virtual control strategy is used to achieve control objectives. We find closed-form expressions for the optimal robot and actuator parameters (link lengths, gear ratios, etc.) that maximize energy regeneration between any two times, given motion trajectories. In addition, we solve several trajectory optimization problems for maximizing energy regeneration that admit closed-form solutions, given system parameters. Optimal solutions are shown to be global and unique. In addition, closed-form expressions are provided for the maximum attainable energy. This theoretical maximum places limits on the amount of energy that can be recovered. Numerical examples are provided in each case to demonstrate the results. For problems that don't admit analytical solutions, we formulate the general nonlinear optimal control problem, and solve it numerically, based on the direct collocation method. The optimization problem, its numerical solution and an experimental evaluation are demonstrated using a PUMA manipulator with custom regenerative drives. Power flows, stored regenerative energy and efficiency are evaluated. Experimental results show that when following optimal trajectories, a reduction of about 10-22% in energy consumption can be achieved. Furthermore, we present the design, control, and experimental evaluation of an energy regenerative powered transfemoral prosthesis. Our prosthesis prototype is comprised of a passive ankle, and an active regenerative knee joint. A novel varying impedance control approach controls the prosthesis in both the stance and swing phase of the gait cycle, while explicitly considering energy regeneration. Experimental evaluation is done with an amputee test subject walking at different speeds on a treadmill. The results validate the effectiveness of the control method. In addition, net energy regeneration is achieved while walking with near-natural gait across all speeds.

Cited by 1Open access

Experimental Multi-site Testbed for Advanced Control and Optimization of Hybrid Energy Systems

Arash Omidi, Tanmay Mishra, Mads Almassalkhi · ArXiv.org · 2025

This paper presents a hybrid energy system (HES) experimental testbed developed at the University of Vermont, featuring a dual-site architecture that integrates on-campus laboratory facility with an off-campus solar and meteorological station. This supports the prototyping and validation of advanced HES control and optimization strategies. The platform integrates hardware-in-the-loop (HIL) simulations with a reconfigurable set of kVA-scale assets.A unified monitoring and communication architecture supports real-time data acquisition, model validation, and control implementation. The capabilities of the testbed are demonstrated through an HIL experiment in which a battery systems participate in solar PV smoothing.

Cited by 0Open access

Advanced strategies for the efficient optimization and control of industrial compressed air systems

D. Salomone-González · Results in Engineering · 2025

This article analyzes the integration of a master system as the central control unit for compressed air stations through impact testing on the energy consumption of real-world scenarios. Based on the results obtained, it aims to highlight the system's potential in control processes and data transmission, ensuring maximum efficiency in any compression equipment configuration. Real-time data transmission, powered by big data algorithms, enables continuous analysis to optimize the overall performance of the compressor plant. This constant data flow facilitates fine-tuned adjustments that align energy consumption and allow for preventive maintenance tailored to system-specific needs, safeguarding the system's efficiency throughout its life-cycle. What makes this article novel is its focus on a point that has been largely overlooked within the industry, with very limited existing literature. This makes the study a crucial contribution to industrial energy efficiency research. The solution is highly replicable across any industry, offering the potential to generate a global impact. With real-time service data available from the compressor station for analysis, operators can determine ideal maintenance intervals, minimizing unexpected downtime. Additionally, by optimizing usage times, more compressors can remain off for longer periods, which will significantly reduce the costs of external services from the different brands of compressor suppliers. Compressors are activated sequentially to prevent excessive unload times, ensuring the optimal utilization of variable-speed compressors while maintaining low energy costs. Due to the ease of use and the simplicity and compactness of the solution, this technology is replicable across any industrial plant with compressed air service. For this research, a real and empirical case study was conducted, demonstrating a significant improvement by reducing the energy efficiency indicator from 0.192 kWh/m 3 to 0.12 kWh/m 3 , achieving remarkable energy savings of around 40%. Furthermore, predictive and demand-based maintenance enhanced supply reliability and reduced costs, while providing continuous visibility into failures, losses and meaningful improvements, showcasing the system's potential for long-term operational excellence. • A master control system optimizes industrial air compressor efficiency. • Real-time data transmission enables adaptive energy regulation. • Energy consumption reduced from 0.192 kWh/m 3 to 0.12 kWh/m 3 (≈40% savings). • Predictive maintenance improves reliability and reduces operational costs. • The system is scalable across different industrial compressed air plants.

Cited by 3Open access