Real-Time Adaptive Machine-Learning-Based Predictive Control of Nonlinear Processes

Fractional Order Distributed Model Predictive Control of Fast and Strong Interacting Systems

Fast and strong interacting systems are hard to control from both performance and control effort points of view. Moreover, multiple objective functions or objectives with various identifiers of varying weights can hold unfeasible solutions at times. A novel cost objective function is proposed here to overcome both feasibility set limitations and computational burdens. An application example is used to illustrate its added value, which is a fast and strong interacting multivariable system: a landscape office lighting regulatory problem. New lighting technology and an intelligent control system have been produced to improve control accuracy and reduce power consumption. While optimizing the hardware of the lighting system, the energy consumption can be further reduced by applying advanced control strategy in the lighting system. This paper designed a fractional order distributed model predictive control (FOMPC) scheme to realize the reference tracking and stability control of multiple illuminations at the same time. In order to test the efficiency of the control strategy, an experiment was carried out on the lighting setup based on the dSPACE control system. The FOMPC scheme was analyzed through simulation and lighting experiments based on the dSPACE control system. Through a comparison with the mode predictive control (MPC) scheme, the superiority of the FOMPC scheme for the dynamic behavior and control performance of multiple lighting systems was verified. The research results provide a basis for multiple lighting control and its application.

A Survey on Learning-Based Model Predictive Control: Toward Path Tracking Control of Mobile Platforms

The learning-based model predictive control (LB-MPC) is an effective and critical method to solve the path tracking problem in mobile platforms under uncertain disturbances. It is well known that the machine learning (ML) methods use the historical and real-time measurement data to build data-driven prediction models. The model predictive control (MPC) provides an integrated solution for control systems with interactive variables, complex dynamics, and various constraints. The LB-MPC combines the advantages of ML and MPC. In this work, the LB-MPC technique is summarized, and the application of path tracking control in mobile platforms is discussed by considering three aspects, namely, learning and optimizing the prediction model, the controller design, and the controller output under uncertain disturbances. Furthermore, some research challenges faced by LB-MPC for path tracking control in mobile platforms are discussed.

Steam Turbine Rotor Stress Control through Nonlinear Model Predictive Control

The current flexibility of the energy market requires operating steam turbines that have challenging operation requirements such as variable steam conditions and higher number of startups. This article proposes an advanced control system based on the Nonlinear Model Predictive Control (NMPC) technique, which allows to speed up the start-up of steam turbines and increase the energy produced while maintaining rotor stress as a constraint variable. A soft sensor for the online calculation of rotor stress is presented together with the steam turbine control logic. Then, we present how the computational cost of the controller was contained by reducing the order of the formulation of the optimization problem, adjusting the scheduling of the optimizer routine, and tuning the parameters of the controller itself. The performance of the control system has been compared with respect to the PI Controller architecture fed by the soft sensor results and with standard pre-calculated curves. The control architecture was evaluated in a simulation exploiting actual data from a Concentrated Solar Power Plant. The NMPC technique shows an increase in performance, with respect to the custom PI control application, and encouraging results.

Integration and Optimal Control of MicroCSP with Building HVAC Systems: Review and Future Directions

Heating, ventilation, and air-conditioning (HVAC) systems are omnipresent in modern buildings and are responsible for a considerable share of consumed energy and the electricity bill in buildings. On the other hand, solar energy is abundant and could be used to support the building HVAC system through cogeneration of electricity and heat. Micro-scale concentrated solar power (MicroCSP) is a propitious solution for such applications that can be integrated into the building HVAC system to optimally provide both electricity and heat, on-demand via application of optimal control techniques. The use of thermal energy storage (TES) in MicroCSP adds dispatching capabilities to the MicroCSP energy production that will assist in optimal energy management in buildings. This work presents a review of the existing contributions on the combination of MicroCSP and HVAC systems in buildings and how it compares to other thermal-assisted HVAC applications. Different topologies and architectures for the integration of MicroCSP and building HVAC systems are proposed, and the components of standard MicroCSP systems with their control-oriented models are explained. Furthermore, this paper details the different control strategies to optimally manage the energy flow, both electrical and thermal, from the solar field to the building HVAC system to minimize energy consumption and/or operational cost.

Responsive Economic Model Predictive Control for Next-Generation Manufacturing

There is an increasing push to make automated systems capable of carrying out tasks which humans perform, such as driving, speech recognition, and anomaly detection. Automated systems, therefore, are increasingly required to respond to unexpected conditions. Two types of unexpected conditions of relevance in the chemical process industries are anomalous conditions and the responses of operators and engineers to controller behavior. Enhancing responsiveness of an advanced control design known as economic model predictive control (EMPC) (which uses predictions of future process behavior to determine an economically optimal manner in which to operate a process) to unexpected conditions of these types would advance the move toward artificial intelligence properties for this controller beyond those which it has today and would provide new thoughts on interpretability and verification for the controller. This work provides theoretical studies which relate nonlinear systems considerations for EMPC to these higher-level concepts using two ideas for EMPC formulations motivated by specific situations related to self-modification of a control design after human perceptions of the process response are received and to controller handling of anomalies.