Recent cyberattacks against industrial control systems highlight the criticality of preventing future attacks from disrupting plants economically or, more critically, from impacting plant safety. This work develops a nonlinear systems framework for u...
The purpose of this study was to develop an integrated control strategy for multiscale crystallization processes. An image analysis method using a deep learning neural network is used to measure the fine-scale information of the crystallization proce...
Biological systems regulate motion and suppress unwanted vibrations through learning, adaptation, and predictive control under uncertainty. Inspired by these principles, Bayesian system identification has emerged as a powerful framework for modeling...
Nonlinear model predictive control (NMPC) delivers high tracking accuracy for industrial processes but requires solving a nonlinear program at each sampling instant, limiting its applicability under tight real-time constraints. The Koopman operator p...
This paper presents a simulation-based feasibility study of nonlinear model predictive control (NMPC) for process water quality management in the closed-loop water circuit of the Aktogai copper concentrator. Repeated water recirculation leads to the...
Machine learning has attracted extensive interest in the process engineering field, due to the capability of modeling complex nonlinear process behavior. This work presents a method for combining neural network models with first-principles models in...
Nonlinear technological processes are difficult to control because of strong nonlinear dynamics, parameter uncertainties, transport delays, and external disturbances, which can degrade the performance of conventional control strategies. This study pr...
This work focuses on developing and applying Encrypted Lyapunov-based Model Predictive Control (LMPC) in a nonlinear chemical process network for Ethylbenzene production. The network, governed by a nonlinear dynamic model, comprises two continuously...
In recent years, microreactors have attracted increasing attention as next-generation chemical reactors, enabling rapid and highly efficient reactions, while requiring precise control against temperature variations. In this paper, a research platform...
In this article, we introduce a novel framework for the design of multi set-point nonlinear explicit controllers for process systems engineering problems where the set-points are treated as uncertain parameters simultaneously with the initial state o...
This study develops a data-driven Gaussian process-based model predictive control (GP-MPC) method for an interacting nonlinear gas–liquid system. Three independent Gaussian process models with Matérn 5/2 kernels are trained to predict th...
The Wiener model, composed of a linear dynamical block and a nonlinear static one connected in series, is frequently used for prediction in Model Predictive Control (MPC) algorithms. The parallel structure is an extension of the classical Wiener mode...
In this paper, a technique that integrates methods of dynamic economic optimization and real-time control by including economic model predictive control and closed-loop predictive control has been developed, using a two-layer structure. The upper lay...
In this paper, a particle filter design scheme for a robust nonlinear control system of uncertain heat exchange process against noise and communication time delay is presented. The particle filter employs a cluster of particles and associated weights...
Temperature control in a continuous stirred tank reactor (CSTR) poses significant challenges due to the process’s inherent nonlinearities and uncertain parameters. This study proposes an innovative solution by developing an adaptive nonlinear p...
Subspace predictive control (SPC) is a widely recognized data-driven methodology known for its reliability and convenience. However, effectively applying SPC to complex industrial process systems remains a challenging endeavor. To address this, this...
A method for the advanced construction of the dynamic matrix for Model Predictive Control (MPC) algorithms with linearization is proposed in the paper. It extends numerically efficient fuzzy algorithms utilizing skillful linearization. The algorithms...
For some classes of control plants (e.g., large time delay or inverse response), the PID controllers may offer unsatisfactory results; on the other hand, a Model Predictive Control (MPC) algorithm based on a linear model may offer insufficient contro...
Inverse fuzzy model control (IFMC) is an attractive strategy for regulating nonlinear thermal processes, where complex heat-transfer dynamics, disturbances, and actuator constraints challenge conventional control approaches. However, traditional dire...
One of the challenges for chemical processes today, from a safety and profit standpoint, is the potential that cyberattacks could be performed on components of process control systems. Safety issues could be catastrophic; however, because the nonline...
Process operational safety plays an important role in designing control systems for chemical processes. Motivated by this, in this work, we develop a process Safeness Index-based economic model predictive control system for a broad class of stochasti...
This paper presents the development and investigation of an intelligent control system for a high-frequency ozone generator integrated into an IoT-based and telecommunication environment. A cyber-physical nonlinear mathematical model combining the el...
Performance of integrated production scheduling and advanced process control with disturbances is summarized and reviewed with four progressive stages of scheduling and control integration and responsiveness to disturbances: open-loop segregated sche...
A novel formulation for combined scheduling and control of multi-product, continuous chemical processes is introduced in which nonlinear model predictive control (NMPC) and noncyclic continuous-time scheduling are efficiently combined. A decompositio...
Since industrial control systems are usually integrated with numerous physical devices, the security of control systems plays an important role in safe operation of industrial chemical processes. However, due to the use of a large number of control a...
In this paper, the theory of RPC (remote parameter control) iteration process of linear situation without and with iteration coefficient as well as nonlinear situation with coefficient is analyzed. The influence of iteration coefficient on iterative...
In this study, we present a general form of nonlinear two-time-scale systems, where singular perturbation analysis is used to separate the dynamics of the slow and fast subsystems. Machine learning techniques are utilized to approximate the dynamics...
In recent years, intelligent control of complex thermodynamic systems has gained increasing attention due to global demands for higher energy efficiency and reduced environmental impact in industrial settings. This study explores the integration of q...
In this paper, a novel data-driven approach for the development of soft sensors (SSs) for multi-step-ahead prediction of industrial process variables is proposed. This method is based on the recent developments in Koopman operator theory and dynamic...
Authors of this paper take under investigation the optimization of biological processes during the wastewater treatment in sequencing batch reactor (SBR) plant. A designed optimizing supervisory controller generates the dissolved oxygen (DO) trajecto...
Accurate trajectory tracking is a paramount objective when a mobile robot must perform complicated tasks. In high-speed movements, time delays appear when reaching the desired position and orientation, as well as overshoots in the changes of orientat...
Thermal processes with prolonged and variable delays pose considerable difficulties due to unpredictable system dynamics and external disturbances, often resulting in diminished control effectiveness. This work presents a hybrid control strategy that...
The ultraviolet water disinfection process has numerous advantages in comparison with other water treatments. This study deals with this process’s optimal control and presents all its stages, from problem statement to analysis of results. We consider...
Ultra-wideband (UWB) positioning provides cost-effective localization for quadrotors in global navigation satellite system (GNSS)-denied environments, but geometry-dependent, heavy-tailed errors challenge state estimation and safety-critical control....
In this work, an optimal state feedback control strategy is proposed for non-linear, distributed-parameter processes. For different values of a given parameter susceptible to upsets, the strategy involves off-line computation of a repository of optim...
In this paper, two identification methods are proposed for a ground robotic system. A Gaussian process regression (GPR) model is presented and adopted for a system identification framework. Its performance and features were compared with a wavelet-ba...
Recurrent neural networks (RNNs) have been widely used to model nonlinear dynamic systems using time-series data. While the training error of neural networks can be rendered sufficiently small in many cases, there is a lack of a general framework to...
This paper introduces a novel output constraint satisfaction mechanism that can be used to supplement controllers employing a control law. This mechanism is dedicated to control systems of nonlinear processes, with this additional feature. It utilize...
In this paper, a robust setpoint tracking disturbance rejection and aggressiveness (RTD-A) controller is designed and developed to control the liquid level of a conical tank process. Meta-heuristic algorithms like grey wolf optimization and the genet...
This work presents a non-linear mathematical model of a machining system subjected to a non-ideal vibration source. Computer simulations have shown chaotic behavior for specific parameters of the proposed mathematical model. The chaotic behavior is p...
The monoethanolamine (MEA)-based post-combustion CO2 capture plant must operate flexibly under the variation of the power plant load and the desired CO2 capture rate. However, in the presence of process nonlinearity, conventional linear control strat...
This paper addresses the problem of fault-tolerant stabilization of nonlinear processes subject to input constraints, control actuator faults and limited sensor–controller communication. A fault-tolerant Lyapunov-based model predictive control (MPC)...
Non-minimum-phase behavior introduces an additional limitation in predictive process control because directly inverting a right-half-plane zero can result in an unstable controller. This study develops a Stable-Inverse Fractional-Order Predictive PID...
Efficient control schemes for ill-conditioned systems, such as the high-purity distillation column, can be challenging and costly to design and implement. In this paper, we propose a distributed control scheme that utilizes well-designed excitation s...
In this paper, an approach for the tuning of a model-based non-linear predictive control (NMPC) is presented. The proposed control uses the pattern search optimization algorithm (PSM), which is applied to the pH non-linear control in the alkalinizati...
Optimization techniques are typically used to improve economic performance of batch processes, while meeting product and environmental specifications and safety constraints. Offline methods suffer from the parameters of the model being inaccurate, wh...
In Model Predictive Control (MPC) algorithms, control signals are generated after solving optimization problems. If the model used for prediction is linear then the optimization problem is a standard, easy to solve, quadratic programming problem with...
This paper presents a non-linear model predictive control approach for offset-free tracking and the rejection of piece-wise constant disturbances. The approach involves augmenting the system’s state vector with the integral of the tracking erro...
The paper proposes a new method for the synthesis of spatial motion control systems of the AUV-leader and a group of AUV-followers during their cooperative movement in a desired formation. This system allows the provision of an accurate positioning o...
The integration of new technologies in Industry 4.0 has modernised agriculture, fostering the concept of smart agriculture (Agriculture 4.0). Higher education institutions are incorporating digital technologies into agricultural curricula, equipping...
of 47