Optimizing Three-Tank Liquid Level Control: Insights from Prairie Dog Optimization

The management of chemical process liquid levels poses a significant challenge in industrial process control, affecting the efficiency and stability of various sectors such as food processing, nuclear power generation, and pharmaceutical industries. While Proportional-Integral-Derivative (PID) control is a widely-used technique for maintaining liquid levels in tanks, its efficacy in optimizing complex and nonlinear systems has limitations. To overcome this, researchers are exploring the potential of metaheuristic algorithms, which offer robust optimization capabilities. This study introduces a novel approach to liquid level control using the Prairie Dog Optimization (PDO) algorithm, a metaheuristic algorithm inspired by prairie dog behavior. The primary objective is to design and implement a PID-controlled three-tank liquid level system that leverages PDO to regulate liquid levels effectively, ensuring enhanced stability and performance. The performance of the proposed system is evaluated using the ZLG criterion, a time domain metric-based objective function that quantifies the system's efficiency in maintaining desired liquid levels. Several analysis techniques are employed to understand the behavior of the system. Convergence curve analysis assesses the PDO-controlled system's convergence characteristics, providing insights into its efficiency and stability. Statistical analysis determines the algorithm's reliability and robustness across multiple runs. Stability analysis from both time and frequency response perspectives further validates the system's performance. A comprehensive comparison study with state-of-the-art metaheuristic algorithms, including AOA-HHO, CMA-ES, PSO, and ALC-PSODE, is conducted to benchmark the performance of PDO. The results highlight PDO's superior convergence, stability, and optimization capabilities, establishing its efficacy in real-world industrial applications. The research findings underscore the potential of PDO in PID control applications for three-tank liquid level systems. By outperforming benchmark algorithms, PDO demonstrates its value in industrial control scenarios, contributing to the advancement of metaheuristic-based control techniques and process optimization. This study opens avenues for engineers and practitioners to harness advanced control solutions, thereby enhancing industrial processes and automation

Vibration suppression of the horizontal flexible plate using proportional– integral–derivative controller tuned by particle swarm optimization

This paper presents the development of an active vibration control for vibration suppression of the horizontal flexible plate structure using proportional–integral–derivative controller tuned by a conventional method via Ziegler–Nichols and an intelligent method known as particle swarm optimization algorithm. Initially, the experimental rig was designed and fabricated with all edges clamped at the horizontal position of the flexible plate. Data acquisition and instrumentation systems were designed and integrated into the experimental rig to collect input–output vibration data of the flexible plate. The vibration data obtained through experimental study was used to model the system using system identification technique based on auto-regressive with exogenous input structure. The plate system was modeled using particle swarm optimization algorithm and validated using mean squared error, one-step ahead prediction, and correlation tests. The stability of the model was assessed using pole zero diagram stability. The fitness function of particle swarm optimization algorithm is defined as the mean squared error between the measured and estimated output of the horizontal flexible plate system. Next, the developed model was used in the development of an active vibration control for vibration suppression on the horizontal flexible plate system using a proportional–integral–derivative controller. The proportional–integral–derivative gains are optimally determined using two different ways, the conventional method tuned by Ziegler–Nichols tuning rules and the intelligent method tuned by particle swarm optimization algorithm. The performances of developed controllers were assessed and validated. Proportional–integral–derivative-particle swarm optimization controller achieved the highest attenuation value for first mode of vibration by achieving 47.28 dB attenuation as compared to proportional–integral–derivative-Ziegler–Nichols controller which only achieved 34.21 dB attenuation

Vibration suppression of the horizontal flexible plate using proportional– integral–derivative controller tuned by particle swarm optimization

This paper presents the development of an active vibration control for vibration suppression of the horizontal flexible plate structure using proportional–integral–derivative controller tuned by a conventional method via Ziegler–Nichols and an intelligent method known as particle swarm optimization algorithm. Initially, the experimental rig was designed and fabricated with all edges clamped at the horizontal position of the flexible plate. Data acquisition and instrumentation systems were designed and integrated into the experimental rig to collect input–output vibration data of the flexible plate. The vibration data obtained through experimental study was used to model the system using system identification technique based on auto-regressive with exogenous input structure. The plate system was modeled using particle swarm optimization algorithm and validated using mean squared error, one-step ahead prediction, and correlation tests. The stability of the model was assessed using pole zero diagram stability. The fitness function of particle swarm optimization algorithm is defined as the mean squared error between the measured and estimated output of the horizontal flexible plate system. Next, the developed model was used in the development of an active vibration control for vibration suppression on the horizontal flexible plate system using a proportional–integral–derivative controller. The proportional–integral–derivative gains are optimally determined using two different ways, the conventional method tuned by Ziegler–Nichols tuning rules and the intelligent method tuned by particle swarm optimization algorithm. The performances of developed controllers were assessed and validated. Proportional–integral–derivative-particle swarm optimization controller achieved the highest attenuation value for first mode of vibration by achieving 47.28 dB attenuation as compared to proportional–integral–derivative-Ziegler–Nichols controller which only achieved 34.21 dB attenuation

Evolutionary swarm algorithm for modelling and control of horizontal flexible plate structures

Numerous advantages offered by the horizontal flexible structure have attracted increasing industrial applications in many engineering fields particularly in the airport baggage conveyor system, micro hand surgery and semiconductor manufacturing industry. Nevertheless, the horizontal flexible structure is often subjected to disturbance forces as vibration is easily induced in the system. The vibration reduces the performance of the system, thus leading to the structure failure when excessive stress and noise prevail. Following this, it is crucial to minimize unwanted vibration so that the effectiveness and the lifetime of the structure can be preserved. In this thesis, an intelligent proportional-integral-derivative (PID) controller has been developed for vibration suppression of a horizontal flexible plate structure. Initially, a flexible plate experimental rig was designed and fabricated with all clamped edges boundary conditions at horizontal position. Then, the data acquisition and instrumentation systems were integrated into the experimental rig. Several experimental procedures were conducted to acquire the input-output vibration data of the system. Next, the dynamics of the system was modeled using linear auto regressive with exogenous, which is optimized with three types of evolutionary swarm algorithm, namely, the particle swarm optimization (PSO), artificial bee colony (ABC) and bat algorithm (BAT) model structure. Their effectiveness was then validated using mean squared error, correlation tests and pole zero diagram stability. Results showed that the PSO algorithm has superior performance compared to the other algorithms in modeling the system by achieving lowest mean squared error of 6103947.4 , correlation of up to 95 % confidence level and good stability. Next, five types of PID based controllers were chosen to suppress the unwanted vibration, namely, PID-Ziegler Nichols (ZN), PID-PSO, PID-ABC, Fuzzy-PID and PID-Iterative Learning Algorithm (ILA). The robustness of the controllers was validated by exerting different types of disturbances on the system. Amongst all controllers, the simulation results showed that PID tuned by ABC outperformed other controllers with 47.60 dB of attenuation level at the first mode (the dominant mode) of vibration, which is equivalent to 45.99 % of reduction in vibration amplitude. By implementing the controllers experimentally, the superiority of PID-ABC based controller was further verified by achieving an attenuation of 23.83 dB at the first mode of vibration and 21.62 % of reduction in vibration amplitude. This research proved that the PID controller tuned by ABC is superior compared to other tuning algorithms for vibration suppression of the horizontal flexible plate structure

Bi-velocity discrete particle swarm optimization and its application to multicast routing problem in communication networks

This paper proposes a novel bi-velocity discrete particle swarm optimization (BVDPSO) approach and extends its application to the NP-complete multicast routing problem (MRP). The main contribution is the extension of PSO from continuous domain to the binary or discrete domain. Firstly, a novel bi-velocity strategy is developed to represent possibilities of each dimension being 1 and 0. This strategy is suitable to describe the binary characteristic of the MRP where 1 stands for a node being selected to construct the multicast tree while 0 stands for being otherwise. Secondly, BVDPSO updates the velocity and position according to the learning mechanism of the original PSO in continuous domain. This maintains the fast convergence speed and global search ability of the original PSO. Experiments are comprehensively conducted on all of the 58 instances with small, medium, and large scales in the OR-library (Operation Research Library). The results confirm that BVDPSO can obtain optimal or near-optimal solutions rapidly as it only needs to generate a few multicast trees. BVDPSO outperforms not only several state-of-the-art and recent heuristic algorithms for the MRP problems, but also algorithms based on GA, ACO, and PSO

Parametric Analysis of BFOA for Minimization Problems Using a Benchmark Function

This paper presents the social foraging behavior of Escherichia coli (E. Coli) bacteria based on Bacteria Foraging Optimization algorithms (BFOA) to find optimization and distributed control values. The search strategy for E. coli is very complex to express and the dynamics of the simulated chemotaxis stage in BFOA is analyzed with the help of a simple mathematical model. The methodology starts from a detailed analysis of the parameters of bacterial swimming and tumbling (C) and the probability of elimination and dispersion (Ped), then an adaptive variant of BFOA is proposed, in which the size of the chemotherapeutic step is adjusted according to the current suitability of a virtual bacterium. To evaluate the performance of the algorithm in obtaining optimal values, the resolution was applied to one of the benchmark functions, in this case the Ackley minimization function, a comparative analysis of the BFOA is then performed. The simulation results have shown the validity of the optimal values (minimum or maximum) obtained on a specific function for real world problems, with a function belonging to the benchmark group of optimization functions

A practical implementation of Robust Evolving Cloud-based Controller with normalized data space for heat-exchanger plant

The RECCo control algorithm, presented in this article, is based on the fuzzy rule-based (FRB) system named ANYA which has non-parametric antecedent part. It starts with zero fuzzy rules (clouds) in the rule base and evolves its structure while performing the control of the plant. For the consequent part of RECCo PID-type controller is used and the parameters are adapted in an online manner. The RECCo does not require any off-line training or any type of model of the controlled process (e.g. differential equations). Moreover, in this article we propose a normalization of the cloud (data) space and an improved adaptation law of the controller. Due to the normalization some of the evolving parameters can be fixed while the new adaptation law improves the performance of the controller in the starting phase of the process control. To assess the performance of the RECCo algorithm, firstly a comparison study with classical PID controller was performed on a model of a plate heat-exchanger (PHE). Tuning the PID parameters was done using three different techniques (Ziegler–Nichols, Cohen–Coon and pole placement). Furthermore, a practical implementation of the RECCo controller for a real PHE plant is presented. The PHE system has nonlinear static characteristic and a time delay. Additionally, the real sensor's and actuator's limitations represent a serious problem from the control point of view. Besides this, the RECCo control algorithm autonomously learns and evolves the structure and adapts its parameters in an online unsupervised manner

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area