Conventional Control Approach Research Articles

Experimental study on the double-skyhook controls of semi-active suspension with variable inertance and damping

As a conventional control approach, the skyhook damping control algorithm can respond to changes in road circumstances but not to those in load circumstances. The skyhook inertance control algorithm, a newly developed control approach, may adapt to changes in load circumstances rather than in road circumstances. The recent introduction of double-skyhook control algorithms has allowed vehicle suspension systems to accomplish both load and road adaptation. This paper presents an experimental investigation contrasting the performance of the double-skyhook configuration to that of the traditional skyhook damper configuration and the skyhook inerter configuration. By integrating the double-skyhook control strategy with semi-active devices with variable inertance and damping, the paper proposes two strategies for coordinating skyhook inertance and skyhook damping control. A controller using the double-skyhook control strategy is designed to employ the Development to Production rapid control prototyping platform. The controller mode is adjusted using MotoTune software to implement control methods. Three tests were conducted under various road conditions, load conditions, and impact conditions for semi-active suspension. Test results indicate that the double-skyhook configuration considerably enhances driving comfort compared to the single-skyhook configuration. Moreover, the greater the deterioration of road conditions and the decrease in load, the more pronounced the enhancement.

A Novel Cooperative Control Strategy for Three-Degree-of-Freedom Pneumatic Parallel Mechanism

The three-degree-of-freedom (3-DoF) parallel mechanism (PM) is widely used due to its simple structure and ability to avoid coupling problems commonly found in high-DoF PMs. The conventional control approach is usually independent control for each branch of the mechanism using a PID controller, without considering the consistency among branches. This paper proposes a novel cooperative control strategy for the 3-DoF PM to achieve both synchronized and differential motion. A pneumatic actuated test rig was constructed to validate the effectiveness of the cooperative controller. The results demonstrate our control approach outperforms the PID controller. Our self-designed platform is functional and intuitive, which can be regarded as a control scheme test bench for a 3-DoF PM.

Exploring the Potential of Remote Sensing to Facilitate Integrated Weed Management in Smallholder Farms: A Scoping Review

In light of a growing population and climate change compounding existing pressures on the agri-food system, there is a growing need to diversify agri-food systems and optimize the productivity and diversity of smallholder farming systems to enhance food and nutrition security under climate change. In this context, improving weed management takes on added significance, since weeds are among the primary factors contributing to crop yield losses for smallholder farmers. Adopting remote-sensing-based approaches to facilitate precision agricultural applications such as integrated weed management (IWM) has emerged as a potentially more effective alternative to conventional weed control approaches. However, given their unique socio-economic circumstances, there remains limited knowledge and understanding of how these technological advancements can be best utilized within smallholder farm settings. As such, this study used a systematic scoping review and attribute analysis to analyze 53 peer-reviewed articles from Scopus to gain further insight into remote-sensing-based IWM approaches and identify which are potentially best suited for smallholder farm applications. The findings of this review revealed that unmanned aerial vehicles (UAVs) are the most frequently utilized remote sensing platform for IWM applications and are also well suited for mapping and monitoring weeds within spatially heterogeneous areas such as smallholder farms. Despite the potential of these technologies for IWM, several obstacles to their operationalization within smallholder farm settings must be overcome, and careful consideration must be given on how best to maximize their potential before investing in these technologies.

Evaluating Reinforcement Learning Algorithms in Residential Energy Saving and Comfort Management

The challenge of maintaining optimal comfort in residents while minimizing energy consumption has long been a focal point for researchers and practitioners. As technology advances, reinforcement learning (RL)—a branch of machine learning where algorithms learn by interacting with the environment—has emerged as a prominent solution to this challenge. However, the modern literature exhibits a plethora of RL methodologies, rendering the selection of the most suitable one a significant challenge. This work focuses on evaluating various RL methodologies for saving energy while maintaining adequate comfort levels in a residential setting. Five prominent RL algorithms—Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Deep Q-Network (DQN), Advantage Actor-Critic (A2C), and Soft Actor-Critic (SAC)—are being thoroughly compared towards a baseline conventional control approach, exhibiting their potential to improve energy use while ensuring a comfortable living environment. The integrated comparison between the different RL methodologies emphasizes the subtle strengths and weaknesses of each algorithm, indicating that the best selection relies heavily on particular energy and comfort objectives.

Opinion: Agricultural cybernetics, what it is, why needs it and what needs to do

This article presents the author’s opinion on potential benefits and challenges of applying cybernetics theory to agriculture, termed agricultural cybernetics. While farming shares similarities with a control system and could benefit from system modeling and control theory, its inherent characteristics pose opportunities to expand existing theories. Such obstacles are induced by the complexities involved in crop production, including the impact of climate, soil, water, fertilizer, farming operations, and the unique communication and control mechanisms within these elements, thus, requires a transdisciplinary approach and further research to remove. This opinion paper particularly underlines the need for creating task-specific models and incorporating farmers' management strategies into these models. Additionally, the author introduces a concept of “Repertoire,” a unique component that distinguishes agricultural cybernetics from conventional control approaches, that acts as a knowledge base of farming strategies under specified conditions. Overall, this article aims to stimulate research interests in agricultural cybernetics and provide insights into benefits and challenges in applying cybernetics theory to agriculture.

Artificial intelligence in parasitic disease control: A paradigm shift in health care.

Parasitic diseases, including malaria, leishmaniasis, and trypanosomiasis, continue to plague populations worldwide, particularly in resource-limited settings and disproportionately affecting vulnerable populations. It has limited the use of conventional health-care delivery and disease control approaches and necessitated exploring innovative strategies. In this direction, artificial intelligence (AI) has emerged as a transformative tool with immense promise in parasitic disease control, offering the potential for enhanced diagnostics, precision drug discovery, predictive modeling, and personalized treatment. Predictive AI algorithms have assisted in understanding parasite transmission patterns and outbreaks by analyzing vast amounts of epidemiological data, environmental factors, and population demographics. This has strengthened public health interventions, resource allocation, and outbreak preparedness strategies, enabling proactive measures to mitigate disease spread. In diagnostics, AI-enabled accurate and rapid identification of parasites by analyzing microscopic images. This capability is particularly valuable in remote regions with limited access to diagnostic facilities. AI-driven computational methods have also assisted in drug discovery for parasitic diseases by identifying novel drug targets and predicting the efficacy and safety of potential drug candidates. This approach has streamlined drug development, leading to more effective and targeted therapies. This article reviews these current developments and their transformative impacts on the health-care sector. It also assessed the hurdles that require attention before these transformations can be realized in real-life scenarios.

Realizing asynchronous finite-time robust tracking control of switched flight vehicles by using nonfragile deep reinforcement learning.

In this study, a novel nonfragile deep reinforcement learning (DRL) method was proposed to realize the finite-time control of switched unmanned flight vehicles. Control accuracy, robustness, and intelligence were enhanced in the proposed control scheme by combining conventional robust control and DRL characteristics. In the proposed control strategy, the tracking controller consists of a dynamics-based controller and a learning-based controller. The conventional robust control approach for the nominal system was used for realizing a dynamics-based baseline tracking controller. The learning-based controller based on DRL was developed to compensate model uncertainties and enhance transient control accuracy. The multiple Lyapunov function approach and mode-dependent average dwell time approach were combined to analyze the finite-time stability of flight vehicles with asynchronous switching. The linear matrix inequalities technique was used to determine the solutions of dynamics-based controllers. Online optimization was formulated as a Markov decision process. The adaptive deep deterministic policy gradient algorithm was adopted to improve efficiency and convergence. In this algorithm, the actor-critic structure was used and adaptive hyperparameters were introduced. Unlike the conventional DRL algorithm, nonfragile control theory and adaptive reward function were used in the proposed algorithm to achieve excellent stability and training efficiency. We demonstrated the effectiveness of the presented algorithm through comparative simulations.

Smart Contract-Based Access Control Framework for Internet of Things Devices

The Internet of Things (IoT) has recently attracted much interest from researchers due to its diverse IoT applications. However, IoT systems encounter additional security and privacy threats. Developing an efficient IoT system is challenging because of its sophisticated network topology. Effective access control is required to ensure user privacy in the Internet of Things. Traditional access control methods are inappropriate for IoT systems because most conventional access control approaches are designed for centralized systems. This paper proposes a decentralized access control framework based on smart contracts with three parts: initialization, an access control protocol, and an inspection. Smart contracts are used in the proposed framework to store access control policies safely on the blockchain. The framework also penalizes users for attempting unauthorized access to the IoT resources. The smart contract was developed using Remix and deployed on the Ropsten Ethereum testnet. We analyze the performance of the smart contract-based access policies based on the gas consumption of blockchain transactions. Further, we analyze the system’s security, usability, scalability, and interoperability performance.

A learning observer-based control strategy for PMSM with position sensor fault in railway

In this paper, control strategy for PMSM with position sensor fault in railway is investigated. Conventional control approaches rely on accurate measurement feedback signal by position sensor. However, position sensor of PMSM in trains is prone to failure. To solve such a problem, a learning observer-based control strategy is proposed. Firstly, a novel learning observer is proposed in order to achieve high-precision estimation of electromotive force. Secondly, a combined PD-SMC controller is designed to accelerate speed response. Estimation of position and speed can be deduced from EMFs estimation, the estimated speed information can be employed by proposed PD-SMC controller. The effectiveness and validity of proposed learning observer and PD-SMC controller are verified by simulations and experiments.

Distributed intelligence for consensus-based frequency control of multi-microgrid network with energy storage system

Multi-Microgrids (MMGs) provide a highly promising approach for accommodating diverse distributed energy resources and improving the overall system performance. This enhanced performance encompasses aspects such as reliability, resilience, flexibility, and energy efficiency. As MMGs become more prevalent, the conventional centralized control approach struggles to adequately address the coordinated operation of system. Therefore, this article proposes a leader-follower-based distributed control strategy (DCS) for coordinated frequency control of numerous heterogeneous and geographically dispersed DERs in MMG network. The DCS employs a robust nonlinear fractional-order proportional integral derivative controller for frequency control of MMG. To optimize the parameters of the controller, an adaptive hybrid chaotic atom search-particle swarm optimization (ACAS-PSO)-based distributed intelligent technique is proposed. Initially, the proposed control strategy efficacy has been tested against various single/multidimensional benchmark functions. Then, it is applied to optimize the parameters of the controller for frequency regulation of MMG system by minimizing the integral absolute error (IAE). The findings obtained illustrate that the proposed ACAS-PSO control technique has an IAE improvement of 42.21 % to 65.3 % in MG 1, 55 % to 96 % in MG 2 and 42.2 % to 84.5 % in MG 3 compared to state-of-the-art. Moreover, the proposed method enhances the frequency response in terms of settling time by 23.4 % to 68.7 % in MG 1, 45.2 % to 68.3 % in MG 2 and 36.35 % to 48 % in MG 3, and the control effort is enhanced by 31.2 % to 87.6 % in MG 1, 31.3 % to 87.05 % in MG 2 and 31.25 % to 73.2 %, in MG 3 compared to other techniques presented. In addition, the robustness of the proposed DCS is validated against plug-and-play ability, communication failure, and delays. Further, the proffered controller is validated by implementing in real-time digital simulation using OPAL-RT.

An Adaptive Inertia and Damping Control Strategy Based on Enhanced Virtual Synchronous Generator Model

In modern converter-dominated power systems, total inertia is very variable and depends on the share of power generated by renewable-based converter-interfaced generation (CIG) at each specific moment. As a result, the limits required by the grid codes on the rate of change of frequency and its nadir or zenith during disturbances become challenging to achieve with conventional control approaches. Therefore, the transition to a novel control strategy of CIG with a grid-forming power converter is relevant. For this purpose, a control algorithm based on a virtual synchronous generator (VSG) is used, which simulates the properties and capabilities of a conventional synchronous generation. However, due to continuously changing operating conditions in converter-dominated power systems, the virtual inertia formed by VSG must be adaptive. At the same time, the efficiency of adaptive algorithms strongly depends on the used VSG structure. In this connection, this paper proposes an enhanced VSG structure for which the transfer function of the active power control loop was formed. With the help of it, the advantages over the conventional VSG structure were proven, which are necessary for the effective adaptive control of the VSG parameters. Then, the analysis of the impact of the VSG parameters on the dynamic response using the transient characteristics in the time domain was performed. Based on the results obtained, adaptive algorithms for independent control of the virtual inertia and the parameters of the VSG damper winding were developed. The performed mathematical modeling confirmed the reliable and effective operation of the developed adaptive control algorithms and the enhanced VSG structure. The theoretical and experimental results obtained in this paper indicate the need for simultaneous development and improvement of both adaptive control algorithms and VSG structures used for this purpose.

Multi-objective control and optimization of a stand-alone photovoltaic power conversion system with battery storage energy management

This paper addresses the problem of controlling a stand-alone photovoltaic (PV) energy conversion system integrated with a battery energy storage system. The study focuses on a series association of PV panels, a DC/AC converter, a Li-ion battery, and a DC load. The intermittent nature of PV power and frequent variations in load demand decrease battery lifetime and its charging performance. To mitigate these issues, an efficient battery charge controller is proposed to instantaneously balance the PV power flow delivered to the DC load and the battery, ensuring optimal utilization of PV power and appropriate battery charging. Based on available solar power, battery state of charge (SOC), and DC load demand The controller adapts to three charging modes, namely, maximum power point tracking (MPPT) charging mode, constant current (CC) charging mode, and constant voltage (CV) charging mode. Additionally, a novel energy management algorithm is designed to ensure battery safety and determine the system’s mode of operation, considering weather conditions and load demand variations. Interestingly, no solar irradiation or battery SOC sensors are required for the implementation of the control system. Nonlinear and robust controllers are developed to provide the necessary control input laws for the management algorithm. The robustness and stability of the system are verified using the Lyapunov theory. Furthermore, the paper quantifies the performance of the proposed strategy through a comparative analysis using integral of absolute error (IAE) indices against two conventional control approaches. Simulation results validate the effectiveness of the proposed controller strategy, demonstrating its high performance and ability to meet the specified objectives. This work presents an innovative approach to enhance the efficiency and reliability of stand-alone PV energy conversion systems with battery storage, offering promising prospects for sustainable energy applications.

Real-world implementation and evaluation of a Model Predictive Control framework in an office space

In this work, Model Predictive Control (MPC) is experimentally implemented on a Heating, Ventilation and Air Conditioning (HVAC) system of a large-scale office space. As controller model, a physics-based Modelica building model is calibrated based on historic data pursuing an iterative, nonlinear optimization approach. For the calibration period with a horizon of seven weeks, the calibrated model exhibits a high model accuracy with a Root Mean Square Error (RMSE) of 0.49 °C between the measured and estimated room temperature. The MPC toolchain includes modules for state estimation and forecasts of disturbances quantities (such as outdoor temperature, solar radiation including calculation of the direct and diffuse fraction, supply temperatures and occupancy). The MPC execution comprises the operation of heating based on radiators and floor heating (via regulation of valve openings) as well as shading of three Venetian blind systems (via regulation of vertical position and slat inclination angle). The experiment is conducted during a heating period with a duration of three weeks from October 21 to November 11, 2022. The heating actuators are controlled considering their typical dynamics and take into account the night setback during unoccupied office periods. User acceptance of the automated shading control is included through additional cost function terms for the shading operation. The field test reveals the predictive control capabilities of the proposed MPC toolchain in a real-life scenario, demonstrating energy-efficient building operation and total average discomfort of 0.53 Kh/d. The MPC formulation provides flexibility regarding adjustability of the control towards energy efficiency, thermal comfort, daylight transmission and non-oscillating shading control. Finally, the disturbance forecast accuracies for outdoor temperature and the solar radiation quantities are evaluated and the MPC control performance is compared against a conventional control approach.

Reduction of DC Capacitor Size in Three-Phase Input/Single-Phase Output Power Cells of Multi-Cell Converters through Resonant and Predictive Control: A Characterization of Its Impact on the Operating Region

Cascaded H-bridge drives require using a significant-size capacitor on each cell to deal with the oscillatory power generated by the H-bridge inverter in the DC-link. This results in a bulky cell with reduced reliability due to the circulating second harmonic current through the DC-link capacitors. In this article, a control strategy based on a finite control set model predictive control and a proportional-resonant controller is proposed to compensate for the oscillatory power required by the H-bridge inverter through the cell’s input rectifier. With the proposed strategy, a DC-link second harmonic free operation is achieved, allowing for the possibility of reducing the capacitor size and, in consequence, the cell dimensions. The feasibility of the proposed control scheme is verified by experimental results in one cell of a cascade H-bridge inverter achieving an operation with a capacitance 141 times smaller than required by conventional control approaches for the same voltage ripple.

An AI framework integrating physics-informed neural network with predictive control for energy-efficient food production in the built environment

Relieving the stress from energy demand is critical for encouraging the application of built environment in agriculture, which is the most energy-intensive food-production sector. In this study, to enhance energy efficiency and crop growth optimization for the built environment, we present a novel artificial intelligence (AI)-based control framework which combines a physics-informed neural network and data-driven robust model predictive control. The physics-informed neural network is constructed for accurately predicting the built environment’s indoor climate and crop states based on control inputs, including controls of temperature, humidity, CO2, irrigation, and fertilization. Data-driven robust model predictive control uses machine learning techniques to account for unpredictable changes in weather conditions to make the best control decisions for actuators. Two examples of the implementation of the proposed AI-based control framework in tomato cultivation environments in Ithaca, New York and Tucson, Arizona are shown to showcase its effectiveness in varying climates. The results show our proposed AI-based control framework can maintain the ideal cultivation environment and, on average, lower cost by 46.4% compared to the conventional control approaches.

Occupant-oriented demand response with multi-zone thermal building control

In future energy systems with high shares of renewable energy sources, the electricity demand of buildings has to react to the fluctuating electricity generation in view of stability. As buildings consume one-third of global energy and almost half of this energy accounts for Heating, Ventilation, and Air Conditioning (HVAC) systems, HVAC are suitable for shifting their electricity consumption in time. To this end, intelligent control strategies are necessary as the conventional control of HVAC is not optimized for the actual demand of occupants and the current situation in the electricity grid. In this paper, we present the novel multi-zone controller Price Storage Control (PSC) that not only considers room-individual Occupants’ Thermal Satisfaction (OTS), but also the available energy storage, and energy prices. The main feature of PSC is that it does not need a building model or forecasts of future demands to derive the control actions for multiple rooms in a building. For comparison, we use an ideal, error-free Model Predictive Control (MPC), a heuristic control approach from the literature (PC), and a conventional hysteresis-based two-point control as upper and lower benchmarks. We evaluate the four controllers in a multi-zone environment for heating a building in winter and consider two different scenarios that differ in how much the permitted temperatures vary. In addition, we compare the impact of model parameters with high and low thermal capacitance. The results show that PSC strongly outperforms the conventional control approach and PC in both scenarios and for both parameters concerning the electricity costs and OTS. For high capacitance, it leads to 22% costs reduction while the ideal MPC achieves cost reductions of more than 39%. Considering that PSC does not need any building model or forecast, as opposed to MPC, the results support the suitability of our developed control strategy for controlling HVAC systems in future energy systems.

Discrete-time prediction based event-triggered controller design: An application to networked multi-area power system with time delays

This work proposes a solution for power system stability by utilizing prediction-based event-triggered control (ETC) in the discrete-time domain. The proposed control method can handle a large sampling period, and the event-triggered mechanism (ETM) is applied in both controller and actuator loops to reduce the network’s computational and communication burden. Input and output (I/O) quantizers are used to avoid the quantization error that arises due to sampling and are also included in the stability analysis. The proposed control strategy is evaluated under various load scenarios using three-area interconnected power systems. The results demonstrate that the proposed approach saves 25.5%, 22%, and 23.5% of channel bandwidth in each area, as compared to the conventional time-triggered control approach. A comparative study shows that the proposed work outperforms recently reported works in terms of better event triggering number, average inter-event time, and performance indices. The effectiveness of the proposed control schemes is further validated by considering uncertainty in system parameters and typical power system nonlinearities. The study also illustrates the integration of renewable energy resources (RERs) and electric vehicles (EVs). The closed-loop system stability is proved theoretically using uniform ultimate boundedness and validated through simulations in MATLAB R2018a.

INDUSTRIAL ASSEMBLY LINE OPERATION

The idea of this paper is to present basic components of a flexible assembly system (FAS), discuss it characteristics, classify the systems and describe an application. This equipment offers professional training according to the industrial reality, simulating a real assembly process and including different technologies from Industry 4.0. The FAS consists of an automated flexible assembly cell with five different stages: assembly, handling, quality inspection, transfer and warehouse. It is the ideal answer to research and training in industrial automation. The main advantage is that it has fully modularity in keeping with industrial reality. The technologies included in its different assembly modules, as well as the assembly process with several variants, allow the user to develop the professional skills required by advanced automation. In this work the conventional control approach is presented with programmable logic controllers (PLC) and SCADA level.

On the Benefits and Limitations of Modulated Damping With Passive Motor Control

Abstract Conventional emulated damping, in which velocity feedback is employed in combination with motor current control to emulate damping, is used in a number of mechatronic applications. As known in the field, although such damping should be strictly passive, several implementation factors render this emulation nonpassive and subject to instability, especially with increasing damping ratios. This paper describes an alternative implementation of emulated damping that employs passive motor control, which does not draw energy from a battery or any other power source. The method is specifically described in the context of a brushless direct-current (BLDC) motor; an unique controller architecture is used along with a particular MOSFET switching scheme which employs only a subset of the standard BLDC motor driver and does not require electronic commutation. This paper employs analytical and experimental means to compare damping control with the described passive motor approach, relative to using a conventional BLDC motor control approach. Stability considerations for each scheme are discussed. Benchtop testing demonstrates the advantage of the passive control scheme in terms of providing smooth behavior, enhancing control robustness, preventing energy leaks, and providing accurate behavior. The special cases of unilateral and asymmetric behaviors (i.e., different damping command for each direction of rotation) are also considered.

Integrated vehicle control using adaptive control allocation

SummaryThe focus of this paper is an integrated, fault‐tolerant vehicle control algorithm for the overall stability of ground vehicles. The proposed scheme comprises a high‐level controller that creates a virtual control input and a low‐level adaptive control allocator that distributes the virtual control effort among redundant actuators. The proposed control framework distinguishes itself from earlier results in the literature by its ability to blend active suspension, steering and traction control channels, in the presence of uncertainties and time‐varying dynamics, without the need for fault identification. The control structure is validated in the simulation environment using a fourteen‐degree‐of‐freedom non‐linear vehicle model. The integrated controller is compared to the case of a conventional control approach where each control problem is solved separately. Our results show that, compared to the conventional approach, the proposed method ensures that the vehicle follows driver inputs with up to % higher longitudinal maneuver velocity, despite the presence of actuator failures and slippery road conditions. Furthermore, to demonstrate the benefit of integrating active suspension control to the overall control scheme, we replaced the suspension control of the proposed approach with an independent suspension control system for comparison purposes. We then showed that the integrated case provided % lower roll angle deviation, and % lower pitch angle deviation, in the presence of actuator effectiveness loss and adverse road conditions.