Automation Control Systems

The Automation Control Systems Section of Processes serves as a dedicated venue for high-quality original research, reviews, and communications at the frontiers of automation, control engineering, and intelligent computing, as applied to engineering processes and systems. Consistent with the journal's process- and system-oriented scope—spanning the fields of chemistry, biology, energy, manufacturing, and pharmaceutics, as well as allied fields—the Section welcomes contributions that advance the design, analysis, and implementation of control and automation solutions in complex, real-world process environments.

Scope and Topics of Interest
This Section covers a broad and evolving range of research topics, organized across the following areas:

Control Theory and Methods—Advanced control techniques including improved PID control, model predictive control (MPC), adaptive control, robust control, nonlinear control, optimal control, sliding mode control, H∞ control, feedback linearization, backstepping, fractional-order control, event-triggered control, sampled data control, time-delay systems, iterative learning control, and resilient control.

Intelligent and Learning-Based Control—Reinforcement learning, deep reinforcement learning, artificial neural networks, fuzzy logic control, neuro-fuzzy systems, genetic algorithms, evolutionary computation, swarm intelligence, multi-agent systems, hierarchical control, autonomous control, self-tuning systems, explainable AI for control, and human-in-the-loop control.

Process Automation and Monitoring—Industrial and manufacturing process automation, SCADA systems, distributed control systems (DCS), programmable logic controllers (PLC), supervisory control, real-time control, batch and continuous process control, smart sensors and actuators, human–machine interface (HMI) design, control loop tuning, instrumentation, and open process automation.

Fault Detection, Diagnosis, and Prognosis—Fault detection and diagnosis (FDD), condition monitoring, anomaly detection, remaining useful life (RUL) prediction, predictive maintenance (PdM), vibration analysis, model-based, signal-based, and knowledge-based fault diagnosis, large language model-based fault diagnosis, residual generation, prognostics and health management (PHM), alarm management, degradation modeling, and sensor fusion for diagnostics.

Data-Driven Methods and Machine Learning—Machine learning for process control, deep learning, convolutional and recurrent neural networks, LSTM networks, transformer models, transfer learning, federated learning, semi-supervised and unsupervised learning, principal component analysis (PCA), partial least squares (PLS), Gaussian process regression, soft sensors, feature extraction and fusion, time-series analysis, and industrial big data analytics.

Optimization and Decision-Making—Metaheuristic and evolutionary optimization, particle swarm optimization (PSO), multi-objective optimization, real-time and stochastic optimization, convex optimization, dynamic programming, Bayesian optimization, economic MPC, energy management optimization, resource allocation, process scheduling, and process intensification.

Digital Twins and Simulation—Digital twins for process and automation systems, virtual commissioning, hardware-in-the-loop (HIL) and software-in-the-loop (SIL) simulation, dynamic process modeling, physics-informed neural networks, hybrid and grey box modeling, system identification, reduced-order modeling, and simulation-based process optimization.

Robotics, Mechatronics, and Drive Systems—Robot control, industrial and collaborative robots (cobots), autonomous mobile robots (AMR) for industrial logistics, trajectory planning and motion control, servo and electrical drive control, permanent magnet synchronous motor (PMSM) control, variable frequency drives (VFDs), mechatronic system design, drone and UAV control for process inspection and monitoring, human–robot interaction, rehabilitation and assistive robotics, parallel and cable-driven mechanisms, soft robotics, and gripper and end-effector design.

Energy Process Control and Renewable Energy Systems—Wind turbine and solar PV control, energy storage management, microgrid and smart grid automation, power converter control, maximum power point tracking (MPPT), grid-forming inverters, fuel cell systems, hydrogen production process control, demand response, battery management systems (BMS), and offshore energy systems, hybrid energy systems.

Industrial Process Applications—Automation and control in chemical, petrochemical, pharmaceutical, food, water treatment, agricultural, mining, aerospace, automotive, semiconductor, and bioprocess industries; smart and additive manufacturing; VSC-HVAC control; nuclear process control; and logistics automation.

Micro, Nano, and Precision Systems—Micro-electro-mechanical systems (MEMS) and nano-positioning control, atomic force microscopy, precision motion control, microfluidic and lab-on-chip automation, piezoelectric actuator control, micro-robotics, and nano-sensor integration.

Process Safety, Reliability, and Resilience—Functional safety (IEC 61508/61511), safety instrumented systems (SISs), risk assessment, hazard analysis (HAZOP), reliability engineering, system resilience, fault-tolerant systems, safety-critical control, alarm rationalization, and cyber resilience in process environments.

Industry 4.0/5.0 and Emerging Technologies—Smart factory, cyber–physical production systems, human-centric automation, cognitive manufacturing, augmented and mixed reality HMI, explainable AI (XAI), trustworthy AI in automation, sustainable and green process automation, interoperability standards (OPC-UA, RAMI 4.0), digital thread, and self-organizing production systems.