Nonlinear technological processes are characterized by strong dynamic coupling, multivariable interactions, and significant uncertainty caused by time-varying operating conditions and external disturbances. These characteristics substantially limit the performance of conventional control strategies, such as classical PID and fixed rule-based controllers, which often lack sufficient adaptability and robustness in complex industrial environments. Even conventional fuzzy controllers may exhibit degraded performance when operating regimes change rapidly or when multiple competing control actions must be evaluated simultaneously. Consequently, the development of advanced intelligent control approaches for nonlinear and uncertain systems remains a critical challenge in modern automation and control engineering. This paper proposes a quantum-inspired fuzzy inference-based intelligent control framework for nonlinear technological processes. The proposed approach integrates a fuzzy inference system as the core decision-making mechanism with quantum-inspired computational principles, including probabilistic state representation, parallel evaluation of alternative control actions, and adaptive weighting mechanisms. Unlike true quantum computation, the proposed method employs quantum-inspired concepts at the algorithmic level to enhance decision flexibility and robustness while remaining fully compatible with classical real-time control platforms. Fuzzy inference enables the incorporation of expert knowledge and linguistic uncertainty, whereas the quantum-inspired structure allows simultaneous assessment of multiple control scenarios within each control cycle. The controller is implemented in a closed-loop architecture and coupled with a dynamic nonlinear process model. Real-time process measurements are used to adapt inference parameters online, while quantum-inspired weighting reinforces favorable control actions and suppresses suboptimal ones. Simulation results demonstrate faster transient response, improved disturbance rejection, and higher operational efficiency compared to classical PID and conventional fuzzy controllers. The proposed methodology is applicable to a wide range of automation and control systems, including energy conversion and thermal processing units.

Modern automation and control systems applied in energy-conversion units, mechatronic platforms, and industrial processes increasingly operate under conditions characterized by strong nonlinear dynamics, parametric uncertainties, and time-varying external disturbances. These factors significantly degrade the performance of conventional control approaches, including PID and rule-based intelligent controllers, particularly in terms of robustness, transient response, and adaptability. Consequently, the development of advanced control frameworks capable of systematically addressing uncertainty and nonlinear behavior remains a critical challenge in automation and control engineering. This study proposes a quantum-inspired photon–spin control framework for robust automation of nonlinear dynamic systems operating under uncertain conditions. The controlled process is modeled using nonlinear state-space equations representative of automation-oriented dynamic plants. System states are mapped into a photon–spin probabilistic representation, where spin-like state variables and photon-inspired energy encoding enable a superposition-based description of multiple possible system behaviors within a unified control-oriented structure. This representation allows parallel evaluation of alternative control actions under uncertainty. A quantum-inspired inference and decision mechanism based on interference-driven selection logic is employed to identify the most probable optimal control action from the superposed state space. The selected control signal is decoded and applied to the plant through classical actuators, forming a hybrid quantum-inspired/classical feedback control loop that remains fully compatible with standard automation hardware and numerical simulation environments, without requiring physical quantum devices. Simulation studies conducted under significant parametric variations (up to ±15%) and external disturbances demonstrate that the proposed framework achieves approximately 25% reduction in steady-state control error and a 20-30% improvement in transient performance compared to conventional intelligent control strategies. The results confirm the effectiveness and robustness of the proposed approach for next-generation automation and control applications operating under uncertain and dynamically varying conditions.

Drone-based inspection has become an effective tool for improving safety and efficiency in construction applications; however, designing flight paths that balance coverage, inspection resolution, and limited flight time remains challenging. Conventional path planning approaches typically apply uniform flight patterns and fixed image resolutions across entire construction sites, leading to redundant scanning in low-complexity areas and insufficient inspection of critical regions. This paper presents an adaptive drone path planning framework for construction applications that integrates fractal image processing with deep learning-based hazard detection.

The proposed approach first captures a preliminary image of the construction site and applies a fractal quadtree algorithm to partition the site into regions of varying spatial resolution based on visual complexity. These partitions are clustered into multiple altitude levels, enabling resolution-aware path planning in which drones are deployed at different heights to efficiently inspect regions with distinct complexity requirements. High-complexity areas are assigned finer resolutions and lower flight altitudes, while low-complexity areas are inspected at coarser resolutions from higher altitudes.

To enable automated safety inspection, a YOLO-based deep learning model is employed to identify construction hazards from images captured by drone-mounted cameras. The detection model is trained offline using labeled construction site imagery and is capable of recognizing multiple hazard types under varying environmental conditions. Simulation results using real construction site images demonstrate that the proposed method significantly reduces the number of required scan locations compared to traditional random walk and zigzag flight patterns while maintaining sufficient image quality for reliable hazard detection. The proposed framework provides an efficient and scalable solution for adaptive drone-based construction site inspection.

Continuous stirred tank reactors (CSTRs) are challenging to control because of their nonlinear dynamics and the strong interaction between concentration and temperature. These challenges become more pronounced when operating conditions vary and reaction kinetics are uncertain, which often limits the effectiveness of conventional control strategies. In this study, a data-driven predictive control approach is developed for a generic nonlinear CSTR by integrating a Long Short-Term Memory (LSTM) neural network within an MPC framework. A benchmark exothermic CSTR described by coupled mass and energy balance equations with Arrhenius-type kinetics and jacket heat exchange is used as a reference process. Reactor concentration and temperature are selected as the state variables, while the coolant temperature serves as the manipulated input. The first-principles model is employed to generate operational data and to evaluate closed-loop performance. Dynamic simulation data are generated over 300 min with a sampling time of 0.5 min and cover multiple operating regions. Disturbances in feed concentration (±10%) and feed temperature (±5 K), together with 1% measurement noise, are introduced to reflect realistic operating conditions. An LSTM network with two hidden layers of 32 units each is trained to perform multi-step prediction of reactor states. On unseen test data, the model achieves root-mean-square errors of approximately 0.02 kmol/m³ for concentration and 2.0 K for temperature. The trained LSTM is embedded into an MPC scheme with a prediction horizon of 10 steps and explicit input and temperature constraints. Closed-loop simulations indicate that the proposed LSTM-based MPC improves set-point tracking and disturbance rejection compared with conventional PID control and nominal model-based MPC, while achieving reduced overshoot and faster stabilization. The results suggest that data-driven predictive control provides a practical alternative for nonlinear CSTR systems when accurate mechanistic models are difficult to obtain.

The control of biogas production in anaerobic digestion systems is inherently challenging due to pronounced nonlinear dynamics, biological uncertainty, long time delays, and limited availability of reliable online measurements. In response, a range of advanced control strategies—including model-based, data-driven, and artificial intelligence-assisted approaches—have been proposed to enhance methane productivity while maintaining process stability. Nevertheless, a systematic and quantitatively grounded comparison of these strategies from an application-oriented perspective remains limited. This study presents a comparative assessment of novel biogas control strategies using a Multi-Criteria Decision Analysis (MCDA) framework. The evaluated alternatives include fuzzy supervisory control, adaptive neuro-fuzzy inference systems (ANFIS), mechanistic model predictive control (MPC), data-driven MPC employing machine-learning predictors, reinforcement learning-based control, and hybrid architectures that integrate soft sensors with intelligent supervisory layers. Eight evaluation criteria were defined to reflect the requirements of full-scale anaerobic digestion systems, including stability and risk prevention, methane productivity, constraint handling capability, robustness to feedstock variability, sensor practicality, implementability in PLC/SCADA environments, explainability, and lifecycle effort. The MCDA results indicate that hybrid strategies combining soft sensing with supervisory control achieved the highest aggregated performance score (0.82 on a normalized scale), followed by fuzzy (0.76) and ANFIS-based (0.74) supervisory controllers. MPC-based strategies exhibited superior constraint handling performance (criterion scores above 0.85) but were comparatively penalized due to higher modeling and implementation effort. The reported literature suggests that data-driven predictive control can improve methane yield by approximately 5–10%, while intelligent supervisory control supported by soft sensors may reduce acidification risk indicators by 20–30% relative to baseline operation. Reinforcement learning approaches demonstrated high theoretical optimization potential but the lowest industrial readiness. Overall, the proposed MCDA framework highlights hybrid intelligent control architectures as the most balanced solution, offering a practical compromise between performance enhancement, robustness, and deployability in biogas production systems.