Suitable Controller Research Articles

System Design of a Customized Solar Photovoltaic Power System for a Microcontroller-based Weather Station in Minna, Nigeria

In atmospheric and meteorological studies, missing values in acquired research data possess serious challenge on the integrity of research output emanating from their use. Missing values or readings may arise due to temporal or permanent failure in power supply to the measuring weather station. In this work, effort was made to address the fundamental issue of remote power at a standalone solar powered weather stations by professionally applying solar photovoltaic power system installation technique and the requisite knowledge of local parameters of the installed location to design a customize power supply pack for the weather station. The protocol involves an elaborate electrical load assessment and analysis of the weather station, followed by an independent component wise design formulation leading to selection of suitable solar module, battery, charge controller and voltage regulator along with their outdoor installation design. It was recommended that the design be validated using solar photovoltaic software followed by physical implementation which should be done in line with the system and installation design.

Enhanced grinding process of a cement ball mill through a generalised predictive controller integrated with a CARIMA model

Cement ball mills in the finishing stage of the cement industries consume the highest energy in the cement manufacturing stage. Therefore, suitable controllers that result in good productivity and product quality with reduced energy consumption are required for the cement ball mill grinding process to increase the profit margins. In this study, generalised predictive controllers (GPC)have been designed for the cement ball mill grinding operation using the model obtained from the step response data taken from the industrially recognized simulator. The servo and regulatory responses are analysed with and without constraints by implementing the designed GPC under the closed loop. The error metrics for GPC and conventional controllers are also analysed. The designed GPC for the cement ball mill grinding process outperforms the traditional controller in error metrics.

Power controller design for electrolysis systems with DC/DC interface supporting fast dynamic operation: A model-based and experimental study

The ability of the electrolysis system, powered by fluctuating and intermittent renewable sources, to rapidly and accurately track power signals is crucial for energy management in renewable power-to-hydrogen (ReP2H) plants and for providing grid frequency regulation as a virtual power plant (VPP). However, there is a considerable lag (several seconds or even more than 20 s) between the changes of the stack power compared with the stack current, mainly due to the existence of the electric double-layer (EDL) effect. This characteristic hinders the further application of electrolysis systems as flexible loads in power grids. By designing a suitable power controller in the power-electronics interface directly connected to the stack to replace the traditional current controller, it is expected to improve the fast dynamic response of the stack power. This paper proposes a unified electrical equivalent circuit for alkaline water electrolysis (AWE) systems and proton exchange membrane (PEM) electrolysis system with detailed parallel Buck type DC/DC interface, which considers the EDL effect and nonlinear behaviors of electrolysis systems and is suitable for controller design. A power controller design and robust parameter tuning method without excessive current overshoot based on frequency-domain analysis is proposed. The accuracy and effectiveness of the proposed model and method are verified by the experimental test on the 2 N m3/h(10 kW) AWE system and the 1 N m3/h(5 kW) PEM electrolysis system. With the proposed power controller, the AWE and PEM electrolysis system can change the stack power within 0.266s and 0.21s respectively, meeting the requirements of energy management and frequency regulation. Additionally, the temperature stability and the sensitivity of the proposed method to parameter fluctuations in the stack and DC/DC interface are analyzed.

Empowering Fuel Cell Electric Vehicles Towards Sustainable Transportation: An Analytical Assessment, Emerging Energy Management, Key Issues, and Future Research Opportunities

Fuel cell electric vehicles (FCEVs) have received significant attention in recent times due to various advantageous features, such as high energy efficiency, zero emissions, and extended driving range. However, FCEVs have some drawbacks, including high production costs; limited hydrogen refueling infrastructure; and the complexity of converters, controllers, and method execution. To address these challenges, smart energy management involving appropriate converters, controllers, intelligent algorithms, and optimizations is essential for enhancing the effectiveness of FCEVs towards sustainable transportation. Therefore, this paper presents emerging energy management strategies for FCEVs to improve energy efficiency, system reliability, and overall performance. In this context, a comprehensive analytical assessment is conducted to examine several factors, including research trends, types of publications, citation analysis, keyword occurrences, collaborations, influential authors, and the countries conducting research in this area. Moreover, emerging energy management schemes are investigated, with a focus on intelligent algorithms, optimization techniques, and control strategies, highlighting contributions, key findings, issues, and research gaps. Furthermore, the state-of-the-art research domains of FCEVs are thoroughly discussed in order to explore various research domains, relevant outcomes, and existing challenges. Additionally, this paper addresses open issues and challenges and offers valuable future research opportunities for advancing FCEVs, emphasizing the importance of suitable algorithms, controllers, and optimization techniques to enhance their performance. The outcomes and key findings of this review will be helpful for researchers and automotive engineers in developing advanced methods, control schemes, and optimization strategies for FCEVs towards greener transportation.

Adaptive MPC path-tracking controller based on reinforcement learning and preview-based PID controller

Path-tracking control is a crucial process for autonomous vehicles, ensuring that the vehicle drives safely along the reference path, and the suitable controller parameters ensure the accuracy and stability of this process. To enhance the adaptability of traditional path-tracking controller parameters, this paper proposes an adaptive model predictive control (MPC) controller based on a preview-based PID controller and deep deterministic policy gradient (DDPG) algorithm to achieve adaptive tuning of the controller parameters. Starting with the design of the dynamics tracking error model of the vehicle and the MPC controller. Based on the actor-critic reinforcement learning architecture, the DDPG agent is designed to tune the prediction horizon and weight matrix of the MPC controller. A preview-based PID controller is proposed to improve the efficiency and stability of reinforcement learning and compensate for the error in vehicle modeling. The improved algorithm performance is verified through the simulation scenarios of high-speed lane changing and accelerated overtaking scenarios constructed by MATLAB/Simulink. The results show that the improved algorithm significantly improves the adaptive ability of the traditional MPC controller to time-varying conditions and achieves higher tracking accuracy and stability.

Data-driven study/optimization of a solar power and cooling generation system in a transient operation mode and proposing a novel multi-turbine modification concept to reduce the sun's intermittent effect

This study explores the integration of solar energy via parabolic trough collectors (PTC) with the Goswami cycle, focusing on evaluating life cycle costs (LCC) and energy performance in both steady-state and transient conditions. A suitable system configuration of a single-turbine Goswami cycle is designed for integration with PTC. Then, in the system's steady-state, a parametric study is performed based on the change in the design components. Subsequently, a multi-objective optimization method using energy and lifetime cost objectives are defined to identify the best system performance. This optimization technique utilizes artificial neural network (ANN) and the grey wolf optimizer (GWO) algorithm. Accordingly, the transient performance of the system is investigated during its optimal operating point using the TRNSYS software. Suitable controllers are considered for regulating power production and cooling system. Annual transient performance graphs of the systems are obtained. Following these investigations, a novel multi-turbine system is proposed. This configuration aims to enhance system reliability and diminish the reliance on storage devices by optimizing performance. The proper configuration of the five-turbine system with the required controllers is also provided. Hence, the method of dividing the turbine into similar turbines with lower capacity is a new concept proposed in this study. In the optimal state, it is observed that for 100 kW of net power output, 161 kW of effective cooling load can be achieved. At this configuration, the system's LCC amounts to 2.91 M. Moreover, during transient operation, an average power of 90 kW is attainable with a cooling load of 120 kW. The findings indicate that modifying the system from single turbine to five-turbines incurs an additional cost of approximately 9.2 % and reduces power output by 6 %. However, this modification significantly mitigates the transient impact of solar fluctuations.

Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks.

Wireless sensor networks (WSNs) are usually composed of tens or hundreds of nodes powered by batteries that need efficient resource management to achieve the WSN's goals. One of the techniques used to manage WSN resources is clustering, where nodes are grouped into clusters around a cluster head (CH), which must be chosen carefully. In this article, a new centralized clustering algorithm is presented based on a Type-1 fuzzy logic controller that infers the probability of each node becoming a CH. The main novelty presented is that the fuzzy logic controller employs three different knowledge bases (KBs) during the lifetime of the WSN. The first KB is used from the beginning to the instant when the first node depletes its battery, the second KB is then applied from that moment to the instant when half of the nodes are dead, and the last KB is loaded from that point until the last node runs out of power. These three KBs are obtained from the original KB designed by the authors after an optimization process. It is based on a particle swarm optimization algorithm that maximizes the lifetime of the WSN in the three periods by adjusting each rule in the KBs through the assignment of a weight value ranging from 0 to 1. This optimization process is used to obtain better results in complex systems where the number of variables or rules could make them unaffordable. The results of the presented optimized approach significantly improved upon those from other authors with similar methods. Finally, the paper presents an analysis of why some rule weights change more than others, in order to design more suitable controllers in the future.

A Novel Mixed Finite/Infinite Dimensional Port–Hamiltonian Model of a Mechanical Ventilator

Mechanical ventilation is a life-saving treatment for critically ill patients who are struggling to breathe independently due to injury or disease. Globally, per year, there has always been a large number of individuals who have required mechanical ventilation. The COVID-19 pandemic brought to light the significance of mechanical ventilation, which played a significant role in sustaining COVID-19-infected critically ill patients who could not breathe on their own. The pandemic drew the attention of the world to the shortage of ventilators globally. Some of the challenges to providing an adequate number of ventilators include: increased demand for ventilators, supply chain disruptions, manufacturing constraints, distribution inequalities, financial constraints, maintenance and logistics difficulties, training and expertise shortages, and the lack of design and development of affordable mechanical ventilators that satisfy the stipulated requirements. This research work presents the formulation of a detailed Port–Hamiltonian model of a mechanical ventilator integrated with the human respiratory system. The interconnection and coupling conditions for the various subsystems within the mechanical ventilator and the coupling between the mechanical ventilator and the human respiratory system are also presented. Structure-preserving discretization is provided alongside numerical simulations and results. The obtained results are found to be comparable to results presented in the literature. Future work will include the design of suitable controllers for the system.

POWER QUALITY ENHANCEMENT USING GWO OPTIMIZED ANFIS CONTROLLER BASED STATCOM IN MG

In power systems, voltage stability and power quality are major problems. Hence, this work formulated a novel flexible control algorithm for STATCOM in order to mitigate power quality issues in micro grids. Further to improve the performance of the STATCOM, an intelligent optimised ANFIS controller has been incorporated. The suggested technique has two parts; GWO is employed in the first stage to learn the ANFIS parameters. The performance of the suggested ANFIS-GWO approach is assessed in the second stage. Thus, the efficacy of the proposed controller is examined under different case studies using both simulation and experimental results. From the results, it was found that when the system is subjected to load changes, the dynamic performance of the stem is improved and the dc link voltage remains constant. As the proposed controller achieves less fluctuation, it can be considered as suitable controller for micro-grids.

Identification of heat exchanger process through Hammerstein and polynomial models

Process industries extensively use heat exchangers in their operations, playing a crucial role in various sectors by facilitating efficient heat transfer, conserving energy, and reducing operational costs. This paper focuses on identifying and validating the system model, with the primary task of designing the controller involving the determination of the mathematical representation of the system. The First Order Plus Dead Time (FODT) model, derived from basic principles, is used to represent the system. To analyze the system’s behavior and construct a suitable controller, model development is essential, achieved by constructing principles models using energy balance equations to identify the heat exchanger process. Data collected from the model are utilized in the identification process, with the temperature at the outlet of the hot air blower being the controlled variable in this investigation. The research aims to determine the mathematical model based on time versus temperature data acquired from the Heat Exchanger. Various system identification methods, such as Hammerstein Wiener (HW), Auto Regressive with Exogenous Input (ARX), Box-Jenkins (BJ), Output-Error (OE), and Auto Regressive Moving Average with Exogenous Input (ARMAX) models, are implemented for the heat exchanger. The models obtained undergo validation, and the best-fit model closest to the physical system is considered for controller design. After conducting the analysis, it was found that the Output-Error (OE) model outperforms other models in terms of achieving the best fit.

Investigation of the Impact of SSSC-Based FLC on the Stability of Power Systems Connected to Wind Farms

The integration of renewable energy sources into power systems has increased significantly in recent years. Among various types of renewable energy, the use of wind energy is growing rapidly due to its low operating cost, wide distribution worldwide, and no greenhouse gas emissions. However, power systems integrated with wind energy may face stability and reliability issues due to the intermittent nature of wind power. Therefore, in power systems connected to wind farms, it is usually required to use some compensators such as static synchronous series compensator (SSSC) to increase the system performance under abnormal conditions. On the other hand, for an SSSC to be effective in improving the system performance, it must be equipped with a suitable controller. In this paper, a fuzzy logic controller (FLC) is used for the SSSC because of its advantages over conventional controllers. Extensive research has been conducted in power systems with wind turbines in which SSSC or FLC has been used; however, their simultaneous application in such systems has received less attention. Therefore, this article aims to fill this gap. The proposed method is implemented on two power systems and the simulation results are analyzed. In both systems, the dynamic behavior of three different wind farms is examined. In the first and second wind farms, either a squirrel cage induction generator (SCIG) or doubly-fed induction generator (DFIG) are used, whereas in the third one which is a combined wind farm (CWF), an equal number of SCIG and DFIG are employed. In wind farms with SCIG or DFIG, an SSSC is also utilized. Furthermore, an FLC is employed for the SSSC to improve its efficacy. A proportional integral (PI) controller is also considered for the SSSC, and its results are compared with FLC results. The simulation results confirm the superiority of FLC over PI controller.

Model Simplification for Asymmetric Marine Vehicles in Horizontal Motion—Verification of Selected Tracking Control Algorithms

This paper addresses a trajectory tracking control algorithm for underactuated marine vehicles moving horizontally in which the current in the North–East–Down frame is constant. This algorithm is a modification of a control scheme based on the input-output feedback linearization method, for which the application condition was that the vehicle was symmetric with respect to the left and right sides. The proposed control scheme can be applied to a fully asymmetric model, and, therefore, the geometric center can be different from the center of mass in both the longitudinal and lateral directions. A velocity transformation to generalized vehicle equations of motion was used to develop a suitable controller. Theoretical considerations were supported by simulation tests performed for a model with 3 degrees of freedom, in which the performance of the proposed algorithm was compared with that of the original algorithm and the selected control scheme based on a combination of backstepping and integral sliding mode control approaches.

Adaptive energy management strategy for solar energy harvesting IoT nodes by evolutionary fuzzy rules

This study explores the integration of genetic programming (GP) and fuzzy logic to enhance control strategies for Internet of Things (IoT) nodes across varied locations. It is introduced a novel methodology for designing a fuzzy-based energy management controller that autonomously determines the most suitable controller structure and inputs. This approach is evaluated using a solar harvesting IoT model that leverages historical solar irradiance data, highlighting the methodology’s potential for diverse geographical applications and compatibility with low-performance microcontrollers. The findings demonstrate that the integration of GP with designed fitness function enables the dynamic learning and adaptation of control strategies, optimizing system behavior based on historical data. The experimental model showcases an ability to efficiently use historical datasets to derive optimal control strategies, with the fitness metric indicating consistent improvement throughout the learning phase. The results indicate that useful control strategies learned at a certain location may outperform a locally-trained control strategies and can be successfully re-applied in other locations.

A Comparison of Ryu and Pox Controllers: A Parallel Implementation

Software Defined Network (SDN) network controllers have limitations in handling large volumes of data generated by switches, which can slow down their performance. Using parallel programming methods such as threading, multiprocessing, and MPI aims to improve the performance of the controller in handling a large number of switches. By considering factors such as memory usage, CPU consumption, and execution time. The test results show that although RYU outperforms POX in terms of faster execution time and lower CPU utilization rate, POX shows its prowess by exhibiting less memory usage despite higher CPU utilization rate than RYU. The use of the parallel approach proves advantageous as both controllers exhibit enhanced efficiency levels. Ultimately, RYU's impressive speed and superior resource optimization capabilities may prove to be more strategic than POX over time. Taking into account the specific needs and prerequisites of a given system, this research provides insights in selecting the most suitable controller to handle large-scale switches with optimal efficiency.

Finite-time fuzzy cooperative control for multi-AUV systems under cyber-attacks with hybrid unknown nonlinearities

In this paper, we investigate the fuzzy cooperative control and collision-free problems of discrete-time multiple autonomous underwater vehicles (AUVs) systems subject to unknown nonlinearities and constrained communication under cyber-attacks. A cooperative controller composed of three components is proposed for each AUV follower. The first component maneuvers the position and velocity for tracking desired signals by the feedback information. The repulsion part protects the AUV from collision with environmental obstacles or other vehicles. The remaining nonlinearities compensation term is employed to compensate for the system’s nonlinearities. Notably, the fuzzy logic systems (FLSs) approximate the hybrid unknown term constituted by external disturbances, unmodeled dynamics and deceptive signals. Then, the unknown terms identification and cooperative control problems are transformed into an error system stability problem. Inequality conditions for finite-time bounded error signals are presented through stability analysis. Also, it is theoretically proven that the cooperative control objective with collision avoidance can be accomplished by using suitable controller parameters. Lastly, the simulation results demonstrate the effectiveness of the complete control framework. Comparisons of various scenarios also affirm the significance of the FLSs and repulsion control for the multi-AUV cooperative control system.

Optimization of PEMFC pressure control using fractional PI/D controller with non-integer order: design and experimental evaluation

The fuel-based proton exchange membrane (PEM) fuel cell is a promising technology for clean energy production owing to the several advantages including high efficiency (around 80% theoretical), quiet in operation, and almost zero emission as compared to conventional internal combustion engine. Only hydrogen and oxygen are supplied at the anode and cathode, respectively to generate power and water is produced as by product. However, it suffers to achieve its maximum theoretical efficiency due to lack of flow/pressure management of hydrogen and oxygen in the PEMFC stack which also causes flooding within the cell and reduce the performance of the catalyst and reduces the efficiency. The higher efficiency can be achieved with the proper control of the hydrogen and oxygen inlet flow rate and pressure at the PEMFC. Since it’s crucial to maintaining a consistent supply of exponential pressure, the main focus of this work is pressure regulation at the PEMFC cathode side. A fractional PI/D controller is designed to operate the PEMFC system more realistically. There are three primary objectives of this research work. In the first step, monitoring the PEMFC operating pressure to find out the suitable fractional PI-D controller for a given resilience level, which has the lowest Integration Absolute Error (IAE) to disturbances. The robustness level and/or threshold peak is considered as a tuning parameter for the evaluation. Second, compare the best IAE performance of the fractional PI-D controller with that of simple SIMC rules, where a certain level of resilience is achieved by varying the SIMC tuning variable. Through this comparison, the effectiveness of the recommended controller in achieving the optimal plant performance is evaluated. Thirdly, design a non-integer order PEMFC plant with a fractional controller using MATLAB software and compare the results with existing models. This comparison provides insight into the practical performance of the proposed controller. The results shows that the developed fractional PI/D controller is able to control the pressure very efficiently at the PEMFC cathode side. The findings further emphasise on the important to consider the resilience and robustness levels at the time of developing control systems for PEMFCs. The efficacy of the suggested unique technique is further confirmed by contrasting the suggested controller with the developed models.

Controller design of coordinated control problems over finite fields via fully actuated approach

This paper discusses coordinated control problems for systems over finite fields via fully actuated approach. Novel types of consensus and synchronization, which are irrelevant to initial states, are proposed. For different coordinated control problems, suitable controllers are designed, which are able to drive the system to the corresponding trajectory in finite steps. Besides, the conclusions are applied to stabilization, and the convergence time is analyzed. As an application, a camera network example is given to illustrate the results.

Research on AUV dynamic obstacle avoidance planning based on rolling speed obstacle method

In this paper, aiming at the problem of poor path planning and obstacle avoidance effect of autonomous underwater vehicle (AUV) in a dynamic environment, a feasible rolling speed obstacle method is proposed. This method combines the rolling window method with the speed obstacle method, and designs a suitable three-dimensional model predictive controller based on the rolling window method under a hybrid obstacle avoidance structure, and achieves stable tracking of the reference path by optimizing the objective function. A three-dimensional collision cone and speed obstacle cone model is constructed while the window is rolling. If the collision avoidance condition is met, the critical collision point is calculated, and the AUV is guided to avoid obstacles safely by tracking the critical collision point; if collision avoidance ends, guide AUV trajectory recovery. The final simulation and experimental results show that the performance of the rolling speed obstacle method in avoiding dynamic obstacles is 30% higher than the rolling window method and 40% higher than the speed obstacle method. The method used in this paper can effectively improve the dynamic obstacle avoidance ability of AUV in real-time path planning.

Novel LMI-Based Boundary Stabilization of Stochastic Delayed Reaction-Diffusion Cohen–Grossberg BAM Neural Networks with Impulsive Effects

The stabilization problem of stochastic delayed reaction-diffusion Cohen–Grossberg BAM neural networks (SDRDCGBAMNNs) with impulsive effects and boundary control is studied in this paper. By using suitable boundary controllers, Lyapunov–Krasovskii functional, linear matrix inequalities and average impulsive interval method, new sufficient criteria are found to ensure that the SDRDCGBAMNNs achieve boundary stabilization in finite-time. Based on these criteria, the effects of impulsive and boundary controllers on finite-time stability are analyzed. Numerical simulations are performed to demonstrate the feasibility of the theoretical results.

UETOPSIS: A Data-Driven Intelligence Approach to Security Decisions for Edge Computing in Smart Cities

Despite considerable technological advances for smart cities, they still face problems such as instability of cloud server connection, insecurity during data transmission, and slight deficiencies in TCP/IP network architecture. To address such issues, we propose a data-driven intelligence approach to security decisions under Named Data Networking (NDN) architecture for edge computing, taking into consideration factors that impact device entry in smart cities, such as device performance, load, Bluetooth signal strength, and scan frequency. Despite existing techniques for Order Preference by Similarity to Ideal Solution (TOPSIS)-based on entropy weights methods are improved and applied, there exist unstable decision results. Due to this, we propose a technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-based on utility function and entropy weights, named UETOPSIS, where the corresponding utility function is applied according to the influence of each attribute on the decision, ensuring the stability of the ranking of decision results. We rely on an entropy-based weights mechanism to select a suitable master controller for the design of the multi-control protocol in the smart city system, and utilize a utility function to calculate the attribute values and then combine the normalized attribute values of utility numbers, starting by analyzing the main work of the controllers. Lastly, a prototype is developed for performance evaluation purposes. Experimental evaluation and analysis show that the proposed work has better authenticity and reliability than existing works and can reduce the workload of edge computing devices when forwarding data, with stability 24.7% higher than TOPSIS, significantly improving the performance and stability of system fault tolerance and reliability in smart cities, as the second-ranked controller can efficiently take over the work when a central controller fails or damaged.