A Behavioral Study of Different Controllers and Algorithms in Real-Time Applications

Maheedhar, Marabathina; Deepa, T.

doi:10.1080/03772063.2022.2153089

Cited by 4 publications

(3 citation statements)

References 102 publications

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“…[28] compared between the CRONE and

{PI}^{λ} D^{μ}

‐controller, where the advantages and disadvantages of the two controllers are discussed. According to MATLAB simulations, the controller provided better disturbance rejection and command tracking than the CRONE controller.…”

Section: Control Of the Wecsmentioning

confidence: 99%

Advanced modeling and control of wind conversion systems based on hybrid generators using fractional order controllers

Mseddi,

Wali,

Abid

et al. 2024

Asian Journal of Control

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The focus of this study is on modeling and management of a Wind Energy Conversion System (WECS) based on a Hybrid Excitation Synchronous Generator (HESG). Using a wind simulator, two controllers, CRONE and H∞, are evaluated for Maximum Power Point Tracking (MPPT) and optimal rotation speed. The results show the CRONE controller's higher tracking capability, and robustness tests investigate the influence of parametric uncertainty. Space harmonic effects, commutation effects, and turbine shaft flexibility are all addressed in the sophisticated models. The introduction of Fractional Order Proportional‐Integral (FOPI) control for MPPT is a game changer. Although fractional calculus‐based accuracy and robustness are uncommon in WECS, they show promise for emissions reduction and increased energy efficiency. This work validates a dependable control approach inside an isolated HESG‐based WECS's power‐maximizing range. Extensive investigation of the parameters impacting FOPI control efficiency yields useful insights for effective MPPT control. A variable‐speed wind turbine (WT) is linked to a HESG through a multiplier, with a controller controlling generator coil excitation voltages and a rectifier connecting the WECS to a load. A thorough 3 kW HESG electrical model that includes generator space harmonics and converter commutation effects is among the contributions. For HESG‐based WECS stability, frequency analysis finds important resonant and anti‐resonant frequencies. These issues are addressed by a FOPI control technique, which ensures system stability and performance. The necessity of exact frequency analysis in HESG‐based WECS is highlighted by a step‐by‐step controller parameter tuning approach.

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“…[28] compared between the CRONE and

{PI}^{λ} D^{μ}

Section: Control Of the Wecsmentioning

confidence: 99%

Advanced modeling and control of wind conversion systems based on hybrid generators using fractional order controllers

Mseddi,

Wali,

Abid

et al. 2024

Asian Journal of Control

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“…The orientation of a rigid body in a coordinate system can be characterized based on the three Euler angles, which can also be used to translate a point's coordinates from one reference frame to another and to explain the relationship between two reference frames [32][33][34][35]. The Euler angles, which show how a body rotates around the axes of a coordinate system, are represented by the symbols for the roll, pitch, and yaw angles.…”

Section: Mathematical Modeling Of Quadcopter Motionmentioning

confidence: 99%

Modeling of Unmanned Aerial Vehicles for Smart Agriculture Systems Using Hybrid Fuzzy PID Controllers

Amertet,

Gebresenbet,

Alwan

2024

Applied Sciences

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Unmanned aerial vehicles have a wide range of uses in the military field, non-combat situations, and civil works. Due to their ease of operation, unmanned aerial vehicles (UAVs) are highly sought after by farmers and are considered the best agricultural technologies, since different types of controller algorithms are being integrated into drone systems, making drones the most affordable option for smart agriculture sectors. PID controllers are among the controllers frequently incorporated into drone systems. Although PID controllers are frequently used in drones, they have some limitations, such as sensitivity to noise and measurement errors, which can lead to instability or oscillations in the system. On the other hand, PID controllers provide improved accuracy in drone system responses. When using PID controllers to achieve the best performance in a drone system, it is better to share the advantages of PID controllers with other intelligence controllers. One promising option is the fuzzy PID controller. The aim of this study was to control quadcopter states (rolling, altitude, and airspeed) by leveraging quadcopter technology and adding hybrid fuzzy PID controls into the system. The quadcopter system and its controllers were mathematically modeled using the Simulink/MATLAB platform, and the system was controlled by fuzzy PID controllers. For validation purposes, the fuzzy PID controller was compared with a classically tuned PID controller. For roll, height, and airspeed, the fuzzy PID controller provided an improvement of 41.5%, 11%, and 44%, respectively, over the classically tuned PID controller. Therefore, the fuzzy PID controller best suits the needs of farmers and is compatible with smart agriculture systems.

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“…For instance, PI controllers have limited controllability making them less effective in transient responses. Tuning complexity, high sensitivity to model parameters, and sub-optimal performance in non-linear systems are among other disadvantages of PI controllers [15,16]. While model predictive control (MPC) has been shown to address some of the challenges of classical control algorithms, it still requires extensive tuning of parameters such as the prediction horizon and control weights to achieve optimal performance [17].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning for closed-loop regulation of cardiovascular system with vagus nerve stimulation: a computational study

Sarikhani,

Hsu,

Zeydabadinezhad

et al. 2024

J. Neural Eng.

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(Objective). Vagus nerve stimulation (VNS) is being investigated as a potential therapy for cardiovascular diseases including heart failure, cardiac arrhythmia, and hypertension. The lack of a systematic approach for controlling and tuning the VNS parameters poses a significant challenge. Closed-loop VNS strategies combined with artificial intelligence (AI) approaches offer a framework for systematically learning and adapting the optimal stimulation parameters. In this study, we presented an interactive AI framework using reinforcement learning (RL) for automated data-driven design of closed-loop VNS control systems in a computational study. (Approach). Multiple simulation environments with a standard application programming interface were developed to facilitate the design and evaluation of the automated data-driven closed-loop VNS control systems. These environments simulate the hemodynamic response to multi-location VNS using biophysics-based computational models of healthy and hypertensive rat cardiovascular systems in resting and exercise states. We designed and implemented the RL-based closed-loop VNS control frameworks in the context of controlling the heart rate (HR) and the mean arterial pressure (MAP) for a set point tracking task. Our experimental design included two approaches; a general policy using deep RL algorithms and a sample-efficient adaptive policy using probabilistic inference for learning and control (PILCO). (Main results). Our simulation results demonstrated the capabilities of the closed-loop RL-based approaches to learn optimal VNS control policies and to adapt to variations in the target set points and the underlying dynamics of the cardiovascular system. Our findings highlighted the trade-off between sample-efficiency and generalizability, providing insights for proper algorithm selection. Finally, we demonstrated that transfer learning improves the sample efficiency of Deep RL algorithms allowing the development of more efficient and personalized closed-loop VNS systems. (Significance). We demonstrated the capability of RL-based closed-loop VNS systems. Our approach provided a systematic adaptable framework for learning control strategies without requiring prior knowledge about the underlying dynamics.

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A Behavioral Study of Different Controllers and Algorithms in Real-Time Applications

Cited by 4 publications

References 102 publications

Advanced modeling and control of wind conversion systems based on hybrid generators using fractional order controllers

Advanced modeling and control of wind conversion systems based on hybrid generators using fractional order controllers

Modeling of Unmanned Aerial Vehicles for Smart Agriculture Systems Using Hybrid Fuzzy PID Controllers

Reinforcement learning for closed-loop regulation of cardiovascular system with vagus nerve stimulation: a computational study

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