Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.00001

Research on Optimized Fuzzy PID Temperature Control Strategy Based on Improved Particle Swarm Optimization

Renjie Jin
View original source ↗Read source document ↗Export metadata ↓

Abstract

Precise temperature control is critical in industrial automation, governing product quality in processes from chemical reactors to furnaces. However, high-order inertia, time delays, and parameter drift render traditional PID and manual fuzzy controllers inadequate. To surmount these hurdles, this study presents a robust framework: a Fuzzy PID strategy optimized by a novel Levy-flight Improved Particle Swarm Optimization (LMPSO) algorithm. Addressing the "curse of dimensionality" in fuzzy tuning, LMPSO integrates Levy flight mutation to shatter premature convergence and an Elite Memory Pool to secure evolutionary efficiency. Simulations on a First-Order Plus Dead Time (FOPDT) model reveal the algorithm's potency: it slashes settling time to 105.5 s -- approximately 46.7% faster than standard PSO and 42.5% faster than competitive improved PSO variants -- while achieving an optimal ITAE value. Robustness tests confirm superior stability under severe model mismatches, proving its viability for high-precision industrial applications.

Explore related subjects

cs.NE

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Renjie Jin. 2026-05-14. Research on Optimized Fuzzy PID Temperature Control Strategy Based on Improved Particle Swarm Optimization. https://doi.org/10.1145/3807246.3807306

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

cs.NE subject

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related papers

arXiv2026

Breaking the Central Bias: Spatially Partitioned Experts for Coordinate-Based Neuroevolution

Evolvable-Substrate HyperNEAT (ES-HyperNEAT), a bio-inspired indirect encoding that determines neuron placement and connection weights from spatial coordinates, exhibits a failure mode on MNIST as a diagnostic benchmark. Because input pixels map to a coordinate space centered at the origin, evolved networks converge on a small central cluster of input pixels, a spatial-concentration bias; prior work observed only 21% mean accuracy in this regime. Is this bias an optimization artifact or an architectural ceiling? Inspired by Mixture-of-Experts (MoE) principles, we partition the input into non-overlapping spatial segments, each assigned to a separately evolved specialist network. With 13 such experts, this design reaches 43% mean accuracy, a 106% relative improvement over the baseline. The architectural gain does not depend on data-driven aggregation: equal-weighted averaging, which uses no validation data, already yields a 70% improvement; the gain comes from partitioning, not the weighting. Receptive-field analysis shows the mechanism: partitioning forces evolution to discover features across the entire image, expanding active pixel coverage from 4% to 79%. Absolute accuracy stays below gradient-trained baselines, but the relative gain points to central bias, not the evolutionary search. Two tools are designed to generalize beyond MNIST: a receptive-field diagnostic for silent input-coverage collapse, and a spatial-partitioning remedy that restores coverage.

cs.NE↗
arXiv2026

A Bio-Plausible Visual Neural Network for Locust-Inspired Collision Perception

Locust visual systems have long served as an important biological paradigm for studying looming perception and collision avoidance. Numerous computational models have successfully reproduced the selective responses of Lobula Giant Movement Detector (LGMD) neurons to approaching objects, thereby emulating the fundamental functionality of the biological system. However, existing models remain limited in biological plausibility and robustness when operating in complex and dynamic visual environments. To address these limitations, we propose a biologically plausible neural network for locust-inspired looming detection. The proposed framework incorporates a spatially isotropic sampling strategy that mimics the ommatidial organization of the locust compound eye, a population-voting mechanism inspired by population coding in biological neural systems, and leaky integrate-and-fire neuronal dynamics to replace conventional sigmoid-based membrane activation. Systematic experiments on synthetic stimuli, laboratory sequences, and real-world driving scenarios demonstrate that the proposed model improves robustness under challenging visual conditions while preserving computational efficiency and enhancing biological fidelity. These results highlight the potential of biologically grounded neural computation for robust and efficient collision perception.

cs.NE↗
arXiv2026

Structural Fusion of Bayesian Networks with Limited Treewidth Using Genetic Algorithms

This paper introduces an evolutionary computation approach for consensus in structural Bayesian Network (BN) fusion under the constraint of limited treewidth. The consensus BN aims to reconcile multiple input BNs into a single one that retains key structural features present in the original networks. Treewidth, a graph-based parameter associated with computationally tractable inference, is utilized to restrict the complexity of the resulting network. A genetic algorithm is proposed to look for a BN that codifies as much information about the unrestricted fusion as possible while ensuring the treewidth restriction. Experimental evaluation demonstrates the genetic algorithm's ability to obtain consensus BNs with limited treewidth, providing a valuable tool for aggregating information from diverse sources while returning a computationally actionable model.

cs.NE↗