A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

Soft Computing Techniques and Their Applications in Intel-ligent Industrial Control Systems: A Survey

Soft computing involves a series of methods that are compatible with imprecise information and complex human cognition. In the face of industrial control problems, soft computing techniques show strong intelligence, robustness and cost-effectiveness. This study dedicates to providing a survey on soft computing techniques and their applications in industrial control systems. The methodologies of soft computing are mainly classified in terms of fuzzy logic, neural computing, and genetic algorithms. The challenges surrounding modern industrial control systems are summarized based on the difficulties in information acquisition, the difficulties in modeling control rules, the difficulties in control system optimization, and the requirements for robustness. Then, this study reviews soft-computing-related achievements that have been developed to tackle these challenges. Afterwards, we present a retrospect of practical industrial control applications in the fields including transportation, intelligent machines, process industry as well as energy engineering. Finally, future research directions are discussed from different perspectives. This study demonstrates that soft computing methods can endow industry control processes with many merits, thus having great application potential. It is hoped that this survey can serve as a reference and provide convenience for scholars and practitioners in the fields of industrial control and computer science

Designing an occupancy flow-based controller for airport terminals

One of the most cost-effective ways to save energy in commercial buildings is through designing a dedicated controller for adjusting environmental set-points according occupancy flow. This paper presents the design of a fuzzy rule-based supervisory controller for reducing energy consumptions while simultaneously providing comfort for passengers in a large airport terminal building. The inputs to the controller are the time schedule of the arrival and departure of passenger planes as well as the expected number of passengers, zone global illuminance (daylight) and external temperature. The outputs from the controller are optimised temperature, airflow and lighting set-point profiles for the building. The supervisory controller was designed based on expert knowledge in MATLAB/Simulink, and then validated using simulation studies. The simulation results demonstrate significant potential for energy savings in the controller's ability to maintain comfort by adjusting set-points according to the flow of passengers. Practical application : The systematic approach adopted here, including the use of artificial intelligence to design supervisory controllers, can be extended to other large buildings which have variable but predictable occupancy patterns like the restricted area of the airport terminal building

Fuzzy-logic-based control, filtering, and fault detection for networked systems: A Survey

This paper is concerned with the overview of the recent progress in fuzzy-logic-based filtering, control, and fault detection problems. First, the network technologies are introduced, the networked control systems are categorized from the aspects of fieldbuses and industrial Ethernets, the necessity of utilizing the fuzzy logic is justified, and the network-induced phenomena are discussed. Then, the fuzzy logic control strategies are reviewed in great detail. Special attention is given to the thorough examination on the latest results for fuzzy PID control, fuzzy adaptive control, and fuzzy tracking control problems. Furthermore, recent advances on the fuzzy-logic-based filtering and fault detection problems are reviewed. Finally, conclusions are given and some possible future research directions are pointed out, for example, topics on two-dimensional networked systems, wireless networked control systems, Quality-of-Service (QoS) of networked systems, and fuzzy access control in open networked systems.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374039, 61473163, and 61374127, the Hujiang Foundation of China under Grants C14002 andD15009, the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

An Optimized Type-2 Self-Organizing Fuzzy Logic Controller Applied in Anesthesia for Propofol Dosing to Regulate BIS

During general anesthesia, anesthesiologists who provide anesthetic dosage traditionally play a fundamental role to regulate Bispectral Index (BIS). However, in this paper, an optimized type-2 Self-Organizing Fuzzy Logic Controller (SOFLC) is designed for Target Controlled Infusion (TCI) pump related to propofol dosing guided by BIS, to realize automatic control of general anesthesia. The type-2 SOFLC combines a type-2 fuzzy logic controller with a self-organizing (SO) mechanism to facilitate online training while able to contend with operational uncertainties. A novel data driven Surrogate Model (SM) and Genetic Programming (GP) based strategy is introduced for optimizing the type-2 SOFLC parameters offline to handle inter-patient variability. A pharmacological model is built for simulation in which different optimization strategies are tested and compared. Simulation results are presented to demonstrate the applicability of our approach and show that the proposed optimization strategy can achieve better control performance in terms of steady state error and robustness