Auto tuned PID and neural network predictive controller for a flow loop pilot plant

Patil, Sanjay R.; Agashe, Sudhir

doi:10.1016/j.matpr.2022.08.501

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…They can be used to translate a point's coordinates from one reference frame to another and to explain the relationship between two reference frames. The Euler angles, which show how a body rotates around the axes of a coordinate system, are represented by the symbols ϕ, θ, and ψ for the roll, pitch, and yaw angles, respectively [37][38][39][40]. The three fundamental Euler angles can be combined to achieve any orientation for a rigid body.…”

Section: Mathematical Modeling Of Quadcopter Motionmentioning

confidence: 99%

“…and from the equation (33) This study optimizes controller design for agricultural drones by identifying a suitable penalty rate gain, understanding constraints, and drawing conclusions. It develops a design technique based on fuzzy PID control law, digitalizing traditional PID control law for modern applications and utilizing feedback for gain regulation [35][36][37][38][39].…”

Section: Control Design For Quadcopter System Stabilization Using Fuz...mentioning

confidence: 99%

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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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Section: Mathematical Modeling Of Quadcopter Motionmentioning

confidence: 99%

Section: Control Design For Quadcopter System Stabilization Using Fuz...mentioning

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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RelaxC controller and PID for time delay systems: Experimental test results on Boiler pilot plant

Patil,

Agashe

2023

Results in Control and Optimization

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Predictive functional control for chamber pressure: A quantum simultaneous whale optimization approach

Ghadiri,

Khodadadi

2024

Transactions of the Institute of Measurement and Control

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This study introduces a Constrained Predictive Functional Controller (CPFC) designed for regulating chamber pressure in a coke furnace. While the traditional controllers face challenges posed by the system’s complex multi-input multi-output (MIMO) structure, cross-coupling, time delays, physical constraints, and uncertainties, this article’s approach realized set-point tracking and disturbance rejection, addressing model mismatches while considering constraints on system inputs, input variations, and outputs. To tackle the constrained Model Predictive Control (MPC) optimization problem and minimize the cost function, the Quantum Simultaneous Whale Optimization Algorithm (QSWOA) is employed. This meta-heuristic optimization method integrates quantum coding and simultaneous search with the whale optimization algorithm, enhancing convergence speed and precision. Furthermore, the performance of the proposed CPFC-QSWOA controller is assessed through simulations of the chamber pressure control of a Coke furnace. Results demonstrate the superiority of the CPFC-QSWOA approach, showcasing high precision and robustness, particularly in the face of uncertainties and disturbances.

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Auto tuned PID and neural network predictive controller for a flow loop pilot plant

Cited by 3 publications

References 10 publications

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

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

RelaxC controller and PID for time delay systems: Experimental test results on Boiler pilot plant

Predictive functional control for chamber pressure: A quantum simultaneous whale optimization approach

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