Precision air conditioning (PAC) systems are prone to various types of failures, leading to inefficiencies, increased energy consumption, and possible reductions in equipment performance. This study proposes an automatic real-time fault detection and diagnosis system. It classifies events as either faulty or normal by analyzing key status signals such as pressure, temperature, current, and voltage. This research is based on data-driven models and machine learning, where a specific strategy is proposed for five types of system failures. The work was carried out on a Rittal PAC, model SK3328.500 (cooling unit), installing capacitive pressure sensors, Hall effect current sensors, electromagnetic induction voltage sensors, infrared temperature sensors, and thermocouple-type sensors. For the implementation of the system, a dataset of PAC status signals was obtained, initially consisting of 31,057 samples after a preprocessing step using the Random Under-Sampler (RUS) module. A database with 20,000 samples was obtained, which includes normal and failed operating events generated in the PAC. The selection of the models is based on accuracy criteria, evaluated by testing in both offline (database) and real-time conditions. The Support Vector Machine (SVM) model achieved 93%, Decision Tree (DT) 93%, Gradient Boosting (GB) 91%, K-Nearest Neighbors (KNN) 83%, and Naive Bayes (NB) 77%, while the Random Forest (RF) model stood out, having an accuracy of 96% in deferred tests and 95.28% in real-time. Finally, a validation test was performed with the best-selected model in real time, simulating a real environment for the PAC system, achieving an accuracy rate of 93.49%.

Hydroponics in recirculation systems faces significant challenges in regulating critical parameters, such as pH and electrical conductivity (EC), especially in adverse environmental conditions, such as high altitudes. This paper presents the design and validation of a fuzzy controller integrated with IoT for NFT-type hydroponic systems, implemented on low-cost hardware and tested in the city of Cusco (3339 m.a.s.l.). Unlike previous studies that are limited to simulations or laboratory tests, the proposal was validated under real growing conditions, demonstrating its practical viability. The system incorporates a fuzzy controller based on simple rules, an IoT module with ESP32 for remote monitoring via Blynk, and an accessible and replicable architecture. The results demonstrate stable performance in pH and EC regulation, with adequate response times, minimal overshoot, and reduced errors, achieving levels comparable to those of higher-cost commercial solutions. The main contribution of this study is the demonstration that an intelligent, economical, and replicable system can be applied in agricultural environments with limited resources, offering a viable alternative for improving productivity in high-altitude hydroponic systems.

Measuring solar irradiance is key to assessing the conversion efficiency of photovoltaic (PV) modules. Also, PV modules can be used to estimate irradiance through their electrical response to solar radiation using closed-form models (CFMs). This paper presents a prototype design for irradiance estimation based on evaluating three CFMs by implementing a maximum power point tracking (MPPT) system and a surface temperature measurement system. The system employs an incremental conductance (IC)-based control algorithm, which is optimized to eliminate oscillations at the maximum power point (MPP) and ensure efficient MPP tracking. Experimental validation of the implemented circuits is carried out using Arduino Nano, calibrated sensors, and low-cost electronic devices. Tests in real conditions were performed for four days under different irradiance scenarios, using two monocrystalline PV modules: one with 10 years of use and one new one. The accuracy of the CFMs was evaluated using the mean absolute percentage error (MAPE) and root mean squared error indicators, comparing their estimates with measurements from a Davis Instruments pyranometer. The most accurate CFM obtained a MAPE of 4.38% with the 10-year module and 3.26% with the new module. The results show that the proposed methodology provides estimates with an error of less than 5%, which validates its applicability under various climatic conditions, even with old PV modules.

Conventional scalar voltage–frequency (V/f) control is widely adopted in industrial drives due to its simplicity and low cost; however, at very low operating frequencies, it often suffers from poor transients, speed oscillations, and excessive start-up currents when a proportional–integral controller (PIC) is used in the speed loop. These limitations hinder stable operation and reliable start-up under load, motivating the need for a more robust low-speed control scheme. This paper presents the design and experimental validation of an improved V/f scalar control based on a Mamdani fuzzy logic controller tuned by particle swarm optimization (PSO). A physical prototype was implemented using speed and current sensing and LN CO3636-3B that enables the control strategies, updates K_p and K_i through Mamdani inference in a discrete PI law, and drives a self-commutated inverter via PWM. Experiments were performed under variable conditions, demonstrating that the proposed controller at 6.06% of rated speed achieves stable regulation with M_p = 10% and t_s = 0.445 s, whereas the PIC exhibits instability with steady oscillations and M_p up to 44%. The proposed controller keeps the inrush current within the design limit below 1.615 A. Overall, the PSO-tuned Mamdani fuzzy controller improves low-speed robustness while respecting transient design constraints.

This article presents the design, simulation, and real-time implementation of an intelligent water level control system using Deep Reinforcement Learning (DRL) with the Deep Deterministic Policy Gradient (DDPG) algorithm. The control policy was initially trained in a MATLAB-based simulation environment, where actor–critic neural networks were trained and optimized to ensure accurate and robust performance under dynamic and nonlinear conditions. The trained policy was subsequently deployed on a low-cost embedded platform (Arduino Uno), demonstrating its feasibility for real-time embedded applications. Experimental results confirm the controller’s ability to adapt to external disturbances. Quantitatively, the proposed controller achieved a steady-state error of less than 0.05 cm and an overshoot of 16% in the physical implementation, outperforming conventional proportional–integral–derivative (PID) control by 22% in tracking accuracy. The combination of the DDPG algorithm and low-cost hardware implementation demonstrates the feasibility of real-time deep learning-based control for intelligent water management. Furthermore, the proposed architecture is directly applicable to low-cost Internet of Things (IoT)-based water management systems, enabling autonomous and adaptive control in real-world hydraulic infrastructures. This proposal demonstrates its potential for smart agriculture, distributed sensor networks, and scalable and resource-efficient water systems. Finally, the main novelty of this work is the deployment of a DRL-based controller on a resource-constrained microcontroller, validated under real-world perturbations and sensor noise.

Efforts have continually been directed toward optimizing processes in various fields, and the application in induction motors is no exception. Scalar control V/f offers a straightforward method to regulate the speed of a three-phase induction motor (TIM). However, it faces challenges at low speeds or proportionally at low frequencies, often failing to operate below 20% of its rated speed. This control typically pairs with a PI controller (PIC) for closed loop speed regulation, but its limited control range hinders performance at low speeds. Although intelligent methods have been developed to improve scalar V/f control, attention is often focused on high speeds, while control at low speeds is overlooked. This paper presents the simulation of a fuzzy controller (FC) with a Mamdani-type structure designed to achieve effective low-speed control of a TIM using the V/f scalar control technique. The results not only show improvements in overshoot and settling time but also reveal that the FC can control speeds as low as 6.06% of the rated speed, and it ensures a starting current below the designed motor current under load. Comparative analysis indicates that the FC outperforms the PIC in low-speed control, and it provides an optimal inrush current across different low speeds.