Optimal fractional-order PID controller based on fractional-order actor-critic algorithm

Shalaby, Raafat; El-Hossainy, Mohammad; Abozalam, Belal; Mahmoud, Tarek A.

doi:10.1007/s00521-022-07710-7

Cited by 31 publications

(6 citation statements)

References 74 publications

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“…A temperature control with a digital fractional order controller has been demonstrated [15]. Furthermore, the practical application and improvement of PID controllers using fractional order have also been shown [16][17][18]. Another fractional-order fuzzy PID controller design [19] considered tuning the orders in the fuzzy controller, and then used a genetic algorithm to optimize the controller by minimizing error indices.…”

Section: Introductionmentioning

confidence: 99%

Fractional-Order Fuzzy PID Controller with Evolutionary Computation for an Effective Synchronized Gantry System

Mao,

Chen,

Kao

2024

Algorithms

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Gantry-type dual-axis platforms can be used to move heavy loads or perform precision CNC work. Such gantry systems drive a single axis with two linear motors, and under heavy loads, a high driving force is required. This can generate a pulling force between the drive shafts in the coupling mechanism. In these situations, when a synchronization error becomes too large, mechanisms can become deformed or damaged, leading to damaged equipment, or in industrial settings, an additional power consumption. Effectively and accurately acquiring the synchronized movement of the platform is important to reduce energy consumption and optimize the system. In this study, a fractional-order fuzzy PID controller (FOFPID) using Oustaloup’s recursive filter is used to control a synchronous X–Y gantry-type platform. The optimized controller parameters are obtained by the measurement of control errors in a simulated environment. Four optimization methods are tested and compared: particle swarm optimization, invasive weed optimization, a gray wolf optimizer, and biogeography-based optimization. The systems were tested and compared in order to optimize the control parameters. Each of the four algorithms is simulated on four contour shapes: a circle, bow, heart, and star. The simulations and control scheme of the experiments are implemented using MATLAB, and the reference paths were planned using non-uniform rational B-splines (NURBS). After running the simulations to determine the optimal control parameters, each set of acquired control parameters is also tested and compared in the experiments and the results are recorded. Both the simulations and experiments show good results, and the tracking of the X–Y platform showed improved performance. Two performance indices are used to determine and validate the relative performance of the models and results.

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Section: Introductionmentioning

confidence: 99%

Fractional-Order Fuzzy PID Controller with Evolutionary Computation for an Effective Synchronized Gantry System

Mao,

Chen,

Kao

2024

Algorithms

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“…Comparing Equations ( 7) and (34), the control parameters of the primary control loop are obtained in this case:…”

Section: Fopd Controller Design For the Primary Control Loopmentioning

confidence: 99%

“…However, this is only verified by SISO systems. Recently, some modern metaheuristic methods [23] such as marine predators algorithm (MPA) [33] or a combination with reinforcement learning for online tuning [34] also have been proposed. From the review, most FOPID controllers are used for SISO or non-linear systems, there are only a few works that use fractional-order controllers for cascade structures in the literature.…”

Section: Introductionmentioning

confidence: 99%

Robust Fractional-Order PI/PD Controllers for a Cascade Control Structure of Servo Systems

Chuong,

Nam,

Giang

et al. 2024

Fractal Fract

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In this paper, a cascade control structure is suggested to control servo systems that normally include a servo motor in coupling with two kinds of mechanism elements, a translational or rotational movement. These kinds of systems have high demands for performance in terms of fastest response and no overshoot/oscillation to a ramp function input. The fractional-order proportional integral (FOPI) and proportional derivative (FOPD) controllers are addressed to deal with those control problems due to their flexibility in tuning rules and robustness. The tuning rules are designed in the frequency domain based on the concept of the direct synthesis method and also ensure the robust stability of controlled systems by using the maximum sensitivity function. The M-Δ structure, using multiplicative output uncertainties for both control loops simultaneously, is addressed to justify the robustness of the controlled systems. Simulation studies are considered for two kinds of plants that prove the effectiveness of the proposed method, with good tracking of the ramp function input under the effects of the disturbances. In addition, the robustness of the controlled system is illustrated by a structured singular value (µ) plot in which its value is less than 1 over the frequency range.

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“…Wan [ 31 ] proposed a fractional-order PID using a cloud-model-based genetic algorithm (CQGA), which is more effective in tuning the parameters of the FOPID controller compared to the genetic algorithm. Shalaby [ 32 ] proposed a machine learning-based method for online tuning of FOPID controller parameters that effectively addresses the effects of parameter uncertainty and disturbances in uncertain nonlinear systems. In this paper, we introduce the PID algorithm into kNN query processing and propose a variable query step kNN query processing algorithm based on the PID feedback mechanism.…”

Section: Related Workmentioning

confidence: 99%

A PID-Based kNN Query Processing Algorithm for Spatial Data

Qiao

Chen

et al. 2022

Sensors

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As a popular spatial operation, the k-Nearest Neighbors (kNN) query is widely used in various spatial application systems. How to efficiently process a kNN query on spatial big data has always been an important research topic in the field of spatial data management. The centralized solutions are not suitable for spatial big data due to their poor scalability, while the existing distributed solutions are not efficient enough to meet the high real-time requirements of some spatial applications. Therefore, we introduce the Proportional Integral Derivative (PID) control technology into kNN query processing and propose a PID-based kNN query processing algorithm (PIDKNN) for spatial big data based on Spark. In this algorithm, the whole data space is divided into grid cells of the same size using the grid partition method, and the grid-based index is constructed. On this basis, the grid-based density peak clustering algorithm is used to cluster spatial data, and the corresponding PID parameters are set for each cluster. When performing kNN queries, the PID algorithm is used to estimate the radius growth step size of kNN queries, thereby realizing kNN query processing with a variable query radius growth step based on a feedback mechanism, which greatly improves the efficiency of kNN query processing. A series of experimental results show that the PIDKNN algorithm has good performance and scalability and is superior to the existing parallel kNN query processing methods.

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Optimal fractional-order PID controller based on fractional-order actor-critic algorithm

Cited by 31 publications

References 74 publications

Fractional-Order Fuzzy PID Controller with Evolutionary Computation for an Effective Synchronized Gantry System

Fractional-Order Fuzzy PID Controller with Evolutionary Computation for an Effective Synchronized Gantry System

Robust Fractional-Order PI/PD Controllers for a Cascade Control Structure of Servo Systems

A PID-Based kNN Query Processing Algorithm for Spatial Data

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