This work thoroughly compares the efficiency of Long Short-Term Memory Networks (LSTMs) and Gated Recurrent Unit (GRU) neural networks as models of the dynamical processes used in Model Predictive Control (MPC). Two simulated industrial processes were considered: a polymerisation reactor and a neutralisation (pH) process. First, MPC prediction equations for both types of models were derived. Next, the efficiency of the LSTM and GRU models was compared for a number of model configurations. The influence of the order of dynamics and the number of neurons on the model accuracy was analysed. Finally, the efficiency of the considered models when used in MPC was assessed. The influence of the model structure on different control quality indicators and the calculation time was discussed. It was found that the GRU network, although it had a lower number of parameters than the LSTM one, may be successfully used in MPC without any significant deterioration of control quality.

This work has two objectives. Firstly, it describes a novel physics-informed hybrid neural network (PIHNN) model based on the long short-term memory (LSTM) neural network. The presented model structure combines the first-principle process description and data-driven neural sub-models using a specialized data fusion block that relies on fuzzy logic. The second objective of this work is to detail a computationally efficient model predictive control (MPC) algorithm that employs the PIHNN model. The validity of the presented modeling and MPC approaches is demonstrated for a simulated polymerization reactor. It is shown that the PIHNN structure gives very good modeling results, while the MPC controller results in excellent control quality.

This work is concerned with an original ball-on-plate laboratory process. First, a simplified process model based on state–space process description is derived. Next, a fast state–space MPC algorithm is discussed. Its main advantage is computational simplicity: the manipulated variables are found on-line using explicit formulas with parameters calculated off-line; no real-time optimization is necessary. Software and hardware implementation details of the considered MPC algorithm using the STM32 microcontroller are presented. Tuning of the fast MPC algorithm is discussed. To show the efficacy of the MPC algorithm, it is compared with the classical PID and LQR controllers.

Moving-object tracking using a pan–tilt camera setup is quite a well-known task in robotics. However, the presented research addresses specific properties of the tracked object and introduces novel features to the pan–tilt camera control strategy. Pan–tilt camera control does not operate in an isolated environment. It is a part of the visual servoing system with specific goals. The system has to fulfill certain purposes, which affect its configuration and functionality. The pan–tilt system aims at keeping the visually tracked object within the middle of the image. At the same time, the overall visual servoing efficiently recognizes and tracks the object enabling its grasping by the robot arm. It uses a predictive strategy utilizing specific second-order linear models for pan and tilt joints. Model predictive control (MPC) introduces into the system the ability to predict camera operation over the specific horizon according to the predefined tracking goals. As the system anticipates future positions over the horizon of operation, the setpoint prediction of the future tracked system positions is required. Visual object recognition and tracking system use particular strategies for preparing online tracked object extrapolation over MPC horizon. Therefore, the pan–tilt camera system is intrinsically coupled to camera-based recognition and tracking. Predictive pan–tilt positioning keeps the tracked system in the middle of the image, while the visual system extrapolation improves the tracking performance. The proposed approach is thoroughly tested in the dedicated Gazebo-based robot simulator. Finally, the system is implemented and validated on the Velma robot. The results and their comparison with other control strategies confirm the initial assumptions, allowing further visual servoing system development.

This work is concerned with the vulnerability of a network industrial control system to cyber-attacks, which is a critical issue nowadays. This is because an attack on a controlled process can damage or destroy it. These attacks use long short-term memory (LSTM) neural networks, which model dynamical processes. This means that the attacker may not know the physical nature of the process; an LSTM network is sufficient to mislead the process operator. Our experimental studies were conducted in an industrial control network containing a magnetic levitation process. The model training, evaluation, and structure selection are described. The chosen LSTM network very well mimicked the considered process. Finally, based on the obtained results, we formulated possible protection methods against the considered types of cyber-attack.