Automatic Control Scheme Research Articles

Preliminary clinical experience of robot-assisted surgery in treatment with genioplasty

Genioplasty is the main way to treat diseases such as chin asymmetry, dysplasia and overdevelopment, which involve the three-dimensional direction abnormalities of the chin. Since this kind of surgery mainly uses intraoral incisions, the narrow surgical field of intraoral incisions and the surrounding important neurovascular tissues make it easy for complications, to occur during the osteotomy process, which results in greater surgical risks. The first craniofacial-plastic surgical robot (CPSR-I) system is developed to complete the precise positioning and improve the surgeon's force perception ability. The Kalman filtering method is adopted to reduce the interference of sensor signal noise. An adaptive fuzzy control system, which has strong robustness and adaptability to the environment, is designed to improve the stability of robot-assisted surgical operations. To solve the problem of the depth perception, we propose an automatic bone drilling control strategy that combines position and force conditions to ensure that the robot can automatically stop when the bone is penetrated. On the basis of model surgery and animal experiments, preliminary experiments were carried out clinically. Based on the early results of 6 patients, the robot-assisted approach appears to be a safe and effective strategy for genioplasty.

Comparison of an integrated short-cut biological nitrogen removal process with magnetic coagulation treating swine wastewater and food waste digestate

An integration of two processes, magnetic coagulation (MC) and short-cut biological nitrogen removal (SBNR), coupled with a sequencing batch membrane bioreactor (SMBR) controlled by an automatic real-time control strategy (RTC), was developed to treat different characteristics of high strength wastewater. The treatment efficiency and microbial community-diversity of the proposed method was evaluated and investigated using swine wastewater and food waste (FW) digestate. The MC showed high removal of TSS (89.1 ± 1.5%, 92.21 ± 1.8%), turbidity (90.58 ± 2.1%, 95.1 ± 2.1%), TP (88.5 ± 1.9%, 92.1 ± 1.5%), phosphate (87.76 ± 1.6%, 91.22 ± 1.5%), and SMBR achieved stable and excellent removal of COD (96.05 ± 0.2%, 97.39 ± 0.2%), TN (97.30 ± 0.3%, 97.44 ± 0.3%) andNH4+-N (99.07 ± 0.2%, 98.54 ± 0.2%) for swine wastewater and FW digestate, respectively. The effluent COD andNH4+-N concentrations were found to meet their discharge standards. The microbial community comparison showed similar diversity and richness, and genus Diaphorobacter and Thaurea were dominant in denitritation, and Nitrosomonas was dominant in nitritation treating both swine wastewater and FW digestate.

Open Information Architecture for Seamless Integration of Renewable Energy Sources

Electric power systems are currently confronted with a fundamental paradigm change related to its planning and operation, mainly caused by the massive integration of renewables. To allow higher penetration of them within existing grid infrastructures, the “smart grid” makes more efficient use of existing resources by integrating appropriate information technologies. Utilising the benefits of such smart grids, it is necessary to develop new automation architectures and control strategies, as well as corresponding information and communication solutions. This makes it possible to effectively use and manage a large amount of dispersed generators and to utilise their “smart” capabilities. The scalability and openness of automation systems currently used by energy utilities have to be improved significantly for handling a high amount of distributed generators. This will be needed to meet the challenges of missing common and open interfaces, as well as the large number of different protocols. In the work at hand, these shortcomings have been tackled by a conceptual solution for open and interoperable information exchange and engineering of automation applications. The approach is characterised by remote controllable services, a generic communication concept, and a formal application modelling method for distributed energy resource components. Additionally, the specification of an access management scheme for distributed energy resources, taking into account different user roles in the smart grid, allowed for a fine-grained distinction of access rights for use cases and actors. As a concrete result of this work, a generic and open communication underlay for smart grid components was developed, providing a flexible and adaptable infrastructure and supporting future smart grid requirements and roll-out. A proof-of-concept validation of the remote controllable service concept based on this infrastructure has been conducted in appropriate laboratory environments to confirm the main benefits of this approach.

Data-Driven Resilient Automatic Generation Control Against False Data Injection Attacks

With the advancement of communication technologies and the development of the smart grid, today's physical power systems present an ever-growing dependency on cyber resources. It increases cyber vulnerabilities, causing safety and system stability concerns. In this article, a data-driven resilient automatic generation control (AGC) scheme is proposed under a false data injection attack (FDIA). The key idea is to identify the relationship between AGC signals and system operational conditions. This is achieved by the proposed regression-based FDIA signal predictions, including sequence-to-point prediction and the long short-term memory network-based prediction. It allows us to reconstruct the AGC control signals without being affected by FDIAs and to attenuate attacks in the closed control loop, thus alleviating the impact of FDIA on system performance. Numerical results carried out on the benchmark systems validate the effectiveness of the proposed method.

A novel robust event‐triggered fault tolerant automatic steering control approach of autonomous land vehicles under in‐vehicle network delay

AbstractThis paper proposes a novel robust event‐triggered fault tolerant automatic steering control strategy for autonomous land vehicles to achieve path tracking and vehicle lateral motion control under in‐vehicle network delay. From the practical point of view, the parameter uncertainties, time delay, and actuator fault are simultaneously introduced to make the designed controller robust to more extensive and challenging driving conditions. A novel polytope reduction and norm‐bounded uncertainty reduction method is used to effectively handle the time‐varying velocity and tire cornering stiffness uncertainties. Then, the uncertain vehicle model can be reconstructed as an uncertain network control system model with time delay and actuator fault. Due to the inevitable time delay and actuator fault, a new cubic absolute‐value Lyapunov function is developed to guarantee the asymptotical stability of the closed control system with the H∞ performance, and the modified project‐based adaptive law is applied to strengthen the fault tolerant ability. Furthermore, a progressive event‐triggered mechanism is proposed for the collaborative design of robust fault tolerant automatic steering controller, which can signally improve the communication resource utilization of the limited bandwidth in‐vehicle network. Finally, the performance comparisons of different controllers are presented. These results prove that the proposed controller can simultaneously guarantee the path tacking performance and lateral dynamics stability, and save the communication resource of the in‐vehicle network.

Automatic control of the methionine extraction process from food enterprises wastewater

The process of methionine extracting from food enterprises wastewater by ion exchange sorption was studied in work. The optimal conditions for conducting the process of sorption and desorption of methionine on polycondensation cation exchanger KU-5 were determined. The maximum coefficient of ion-exchange equilibrium from aqueous salt solutions in a strongly acidic medium (pH 2) at the sodium sulfate concentration in the equilibrium solution of 2.1 mg-Eq/ml was determined by the analysis of the isotherms of methionine sorption in the sodium form of the KU-5 cation exchanger. Methionine desorption was carried out with a 1% NaOH solution, which allowed crystalline methionine being extracted from the eluate at pH 6.5 at room temperature, this crystalline methionine meeting the requirements of regulatory documentation. An automatic control scheme where the adsorber operates in a closed cycle: sorption–acidic wastewater displacement–desorption and activation of cation exchanger–regenerating solution displacement–sorption, was proposed to optimize the process. The method allows additional measurement of the modes and changing the equipment parameters depending on the rational parameters of the sorption/desorption. This approach allows complete extraction of the target component from wastewater, as well as increases the sorbent use completeness and energy and resource-saving at the enterprise.

Research on Multi-mode Operation Control Strategy of Feeder Flexible Interconnection in Distribution Network Based on Flexible Multi-State Switch

Aiming at the problems of multi-mode operation and switching, unbalanced feeder load and heavy load of main transformer in flexible interconnection system of distribution network based on Flexible Multi-State Switch(FMSS), a multi-mode operation control strategy of feeder interconnection in distribution network based on Virtual Synchronous Generator (VSG) is proposed,which includes load balancing control strategy and heavy load main transformer automatic control strategy. Firstly, according to the feeder and main transformer load status, the operation mode of system is divided.Then, through the analysis power transmission and switching logic of different operating mode, active power control instructions of FMSS under multi-mode operation are obtained. So that multi-mode stable operation and free switching of the system, the feeder load balancing and main transformer heavy load automatic regulation are realized. In addition, there is no need for control strategy switching. Simulation verifies the rationality and effectiveness of the proposed control strategy.

Discovery of Resident Behavior Patterns Using Machine Learning Techniques and IoT Paradigm

In recent years, technological paradigms such as Internet of Things (IoT) and machine learning have become very important due to the benefit that their application represents in various areas of knowledge. It is interesting to note that implementing these two technologies promotes more and better automatic control systems that adjust to each user’s particular preferences in the home automation area. This work presents Smart Home Control, an intelligent platform that offers fully customized automatic control schemes for a home’s domotic devices by obtaining residents’ behavior patterns and applying machine learning to the records of state changes of each device connected to the platform. The platform uses machine learning algorithm C4.5 and the Weka API to identify the behavior patterns necessary to build home devices’ configuration rules. Besides, an experimental case study that validates the platform’s effectiveness is presented, where behavior patterns of smart homes residents were identified according to the IoT devices usage history. The discovery of behavior patterns is essential to improve the automatic configuration schemes of personalization according to the residents’ history of device use.

Model predictive control in pharmaceutical continuous manufacturing: A review from a user’s perspective

Pharmaceutical continuous manufacturing is considered as an emerging technology by the regulatory agencies, which have defined a framework guided by an effective quality risk management. With the understanding of process dynamics and the appropriate control strategy, pharmaceutical continuous manufacturing is able to tackle the Quality-by-Design paradigm that paves the way to the future smart manufacturing described by Quality-by-Control. The introduction of soft sensors seems to be a helpful tool to reach smart manufacturing. In fact, soft sensors have the ability to keep the quality attributes of the final drug product as close as possible to their references set by regulatory agencies and to mitigate the undesired events by potentially discard out of specification products. Within this review, challenges related to implementing these technologies are discussed. Then, automation control strategies for pharmaceutical continuous manufacturing are presented and discussed: current control tools such as the proportional integral derivative controllers are compared to advanced control techniques like model predictive control, which holds promise to be an advanced automation concept for pharmaceutical continuous manufacturing. Finally, industrial applications of model predictive control in pharmaceutical continuous manufacturing are outlined. Simulations studies as well as real implementation on pharmaceutical plant are gathered from the control of one single operation unit such as the tablet press to the control of a full direct compaction line. Model predictive control is a key to enable the industrial revolution or Industry 4.0.

Hemodynamic and behavioral changes in older adults during cognitively demanding dual tasks.

IntroductionExecutive functions play a fundamental role in walking by integrating information from cognitive‐motor pathways. Subtle changes in brain and behavior may help identify older adults who are more susceptible to executive function deficits with advancing age due to prefrontal cortex deterioration. This study aims to examine how older adults mitigate executive demands while walking during cognitively demanding tasks.MethodsTwenty healthy older adults (M = 71.8 years, SD = 6.4) performed simple reaction time (SRT), go/no‐go (GNG), n‐back (NBK), and double number sequence (DNS) cognitive tasks of increasing difficulty while walking (i.e., dual task). Functional near infra‐red spectroscopy (fNIRS) was used to measure the hemodynamic response (i.e., oxy‐ [HbO2] and deoxyhemoglobin [HbR]) changes in the prefrontal cortex (PFC) during dual and single tasks (i.e., walking alone). In addition, performance was measured using gait speed (m/s), response time (s), and accuracy (% correct).ResultsUsing repeated measures ANOVAs, neural findings demonstrated a main effect of task such that ∆HbO2 (p = .047) and ∆HbR (p = .040) decreased between single and dual tasks. An interaction between task and cognitive difficulty (p = .014) revealed that gait speed decreased in the DNS between single and dual tasks. A main effect of task in response time indicated that the SRT response time was faster than all other difficulty levels (p < .001). Accuracy performance declined between single and dual tasks (p = .028) and across difficulty levels (p < .001) but was not significantly different between the NBK and DNS.ConclusionFindings suggest that a healthy older adult sample might mitigate executive demands using an automatic locomotor control strategy such that shifting conscious attention away from walking during the dual tasks resulted in decreased ∆HbO2 and ∆HbR. However, decreased prefrontal activation was inefficient at maintaining response time and accuracy performance and may be differently affected by increasing cognitive demands.

Application of a New Fusion of Flower Pollinated With Pathfinder Algorithm for AGC of Multi-Source Interconnected Power System

As the world's population grows and energy demand increases, it is necessary to increase the scale of the electrical system, which is more complicated. Consequently, adopting automatic generation control (AGC) scheme to meet the demand becomes inevitable. In this article, the fusion of flower pollinated algorithm (FPA) and pathfinder algorithm (PFA), named hereafter as hFPAPFA, is proposed to achieve maximum control efficiency by combining the exploitation of FPA with the exploration capacity of PFA. The proposed hFPAPFA is meant to regulate two unequal multi-area interconnected power system with different generating units such as thermal, hydro, wind power and diesel plants. The proposed control scheme aims to achieve this by using the new algorithm to optimize the fractional-order set-point weighted PID (FOSWPID) parameters under time domain-based fitness functions namely, integral time square error (ITSE) and integral time absolute error (ITAE) while simultaneously minimizing the power losses. Employing the same interconnected power systems, a comparative study with some recent approaches in renowned journals is conducted. The performance of the proposed method is observed under diverse load conditions scenarios. Moreover, three nonlinearities including boiler dynamics, the governor dead band (GDB) and generation rate constraints (GRC) are further integrated into the system from a pragmatic context. Finally, sensitivity tests involving various parameter changes and the introduction of random step load perturbations are carried out. From the results, the proposed approach outperformed other approaches under different load condition scenarios, incorporation of nonlinearities and random load perturbation, demonstrating the proposed technique's efficacy and reliability.

Design and evaluation of advanced automatic control strategies in a total nitrogen removal activated sludge plant

AbstractAdvanced control strategies are designed for an activated sludge‐based wastewater treatment plant (WWTP) consisting of two anoxic, three aerobic reactors and secondary settler by considering the influent data from an existing WWTP. Four different control strategies (PI/FPI/MPC/FLC) are designed and a systematic procedure is developed to implement these control strategies. The plant performance is evaluated using operational cost index (OCI) and effluent quality index (EQI) and the controller performance is assessed based on integral of squared error. The EQI and OCI for open loop are reported as 4469 and 12 743 respectively. Whereas, when advanced controllers are implemented, a significant decrease of 19.89% and 5.24% in EQI and OCI is observed for MPC and a decrease of 20.9% and 4.63% in EQI and OCI is observed for fuzzy controller. The outcomes of this research help the wastewater utilities to adopt advanced control strategies for enhanced operation and decision‐making.

Graphic RCRA‐PIDA tuning based on maximum sensitivity for automatic generation control of thermal and hydro power systems

The frequency control is the mandatory task in a modern power system because of load demand variation and the integration of renewable energy sources (RESs). The main objective of automatic generation control (AGC) scheme is to maintain the balance between generation and load demand. This study proposes a graphical robust characteristic ratio assignment (RCRA) approach for PIDA controller tuning based on maximum sensitivity ( M s ) for AGC. The basic feature of this graphical approach is that characteristic ratios ( γ i ) in RCRA scheme are calculated in terms of M s . The important part of this design is that the analytical expressions for coefficient gains of PIDA controller are derived in terms of γ i and system parameters. Here, the non-reheat steam-turbine and hydro-turbine based single and multi-area power systems are considered for validation of the proposed control scheme. The critical issue in controller design for a hydro-turbine based power system is the non-minimum phase behaviour. The robustness and performance of RCRA-PIDA controller are examined under system non-linearities, parametric uncertainty and disturbances. Further, the performance of proposed approach is evaluated in the presence of RESs. Finally, it is observed that the proposed control approach performs better in comparison to the recently published control schemes.

Evaluation of the effectiveness of a multi-sectional facade with Venetian blinds and roller shades with automated shading control strategies

Daylighting plays an important role in building design. To maintain visual comfort and provide outdoor views, designers can use multi-sectional facades to extend daylit areas by daylight-redirecting devices and minimize undesired sunlight by movable shading devices. Beyond using specially designed light-redirecting systems, a combination of common shading devices – roller shades and blinds – may be used to meet aesthetic and cost requirements. However, the evaluation and simulation of multi-sectional facades are challenging, especially when the modeling involves complex fenestration systems and automated control strategies. In this study, we evaluate the effectiveness of multi-sectional facades with a roller shade and blind combination. Two control strategies are designed for two different multi-sectional facades, with a workflow developed for the evaluation. This workflow applies Radiance’s three-phase method for each shading state to model movable shades. External scripts are adopted to describe control algorithms and to generate annual results and daylight metrics. The workflow facilitates simulations of daylighting conditions for spaces equipped with both movable shading devices and daylight-redirecting devices under any customized control algorithms. The results demonstrate that the combinations of roller shades, blinds, and proper control algorithms greatly increase daylight near a rear wall compared to cases with only controlled roller shades, and do not cause glare. The combination with fixed blinds and controlled roller shades at the top section and another controlled roller shade at the middle section works well for rear spaces with south-facing or west-facing facades in both Taipei and New York.

Upgrading the Smartness of Retrofitting Packages towards Energy-Efficient Residential Buildings in Cold Climate Countries: Two Case Studies

Improving the energy efficiency of existing buildings by implementing building automation control strategies (BACS) besides building envelope and energy system retrofitting has been recommended by the Energy Performance of Buildings Directive (EPBD) 2018. This paper investigated this recommendation by conducting a simulation-based optimization to explore cost-effective retrofitting combinations of building envelope, energy systems and BACS measures in-line with automation standard EN 15232. Two cases (i.e., a typical single-family house and apartment block) were modeled and simulated using IDA Indoor Climate and Energy (IDA-ICE). The built-in optimization tool, GenOpt, was used to minimize energy consumption as the single objective function. The associated difference in life cycle cost, compared to the reference design, was calculated for each optimization iteration. Thermal comfort of the optimized solutions was assessed to verify the thermal comfort acceptability. Installing an air source heat pump had a greater energy-saving potential than reducing heat losses through the building envelope. Implementing BACS achieved cost-effective energy savings up to 24%. Energy savings up to 57% were estimated when BACS was combined with the other retrofitting measures. Particularly for compact buildings, where the potential of reducing heat losses through the envelope is limited, the impact of BACS increased. BACS also improved the thermal comfort.

Invasive Weed Optimized Area Centralized 2 Degree of Freedom Combined PID Controller Scheme for Automatic Generation Control

Frequency disturbances caused by load perturbations of multi area interconnected power system are reduced by using proper control schemes provided via secondary control loop along with primary control of generation plants. These secondary controllers provide simultaneous control signal to individual machines of a particular area when the control scheme is area centralized. This area centralized mechanism provides quick control action with less computational burden since control problem dimensions gets reduced even the area consists multiple plants/machines. Such centralized new cascade control scheme is proposed in this paper to supervise the secondary control mechanism and it is implemented with parallel connection of 2-Degree of Freedom Proportional-Integral-Derivative (2-DOF PID) controller combined with regular PID controller. The performance of this new controller is studied on multi area multi machine interconnected power system with participation factor concept and later its relative performance in terms of dynamic and steady state specifications are compared with conventional PID and 2-DOF PID controllers. These control parameters are tuned by Invasive Weed Optimization (IWO) algorithm to achieve better system outputs. Case studies presented in this paper show the advantages of proposed control scheme.

Optimal Control Strategy for a Marine Current Farm Integrated with a Hybrid PV System/Offshore Wind/Battery Energy Storage System

This paper suggests an automated control technique constructed on the Multi-Objective Particle Swarm Optimization to enhance the operation of a wind farm, a marine power plant and a photovoltaic array with a battery energy storing system. due to changes in PV / wind / tide, and to boost the efficiency of offshore wind farms and marine power stations connected to the battery-powered storage system, with a view to smoothing power production, the aim of projected automatic control strategy is to minimize power fluctuations and voltage variations. The battery energy storage network was used with an optimized demand response strategy based on the real-time pricing model to improve stability and power efficiency, reduce the power fluctuations and variations in bus voltage and address renewable energy generation instability. The multi-objective particle swarm optimization-based energy management programming model would be used to minimize running costs, pollutant emissions, increase the demand response benefits of micro grid operators and, at the same time, meet the load demand constraints from customers of all sorts, such as domestic, commercial and industrial users.

On-line reset control of a commercial-scale opposed multi-burner coal-water slurry gasification system using dynamic reduced-order model

The entrained flows from four opposed burners enhance the mixing in a coal-water slurry (CWS) gasifier, which increases the extent of carbon conversion. Moreover, the operation of the opposed multi-burner (OMB) gasifier is more flexible because one couple of the opposed burners is allowed to be shut off for a certain period as long as the other couple is still running. Thus, the operation lifetime of the gasification system is determined by the ability to return the shut-off burners to operation, referred to as online reset control (ORC). However, the ORC is manual, which increases the operation uncertainty. In this study, we demonstrate an automatic control strategy using a dynamic reduced-order model (ROM). A reactor network model (RNM) of the gasifier, which considers several sub-models, is developed based on a flow field analysis and particle and gas residence time distributions (RTDs). The dynamic ROM and simulated manual ORC are verified using industrial data (5.8 MPa, 1500 metric tons per day (TPD)). An automatic control scheme for the ORC is proposed. The dynamic ROM is validated to be reliable when operating with the commercial-scale OMB CWS gasification system, as well as with its manual ORC process. The automatic ORC is promising for industrial gasification because the fluctuation ranges of the operation temperature and pressure of the gasifier are acceptable, which indicates that the reliable dynamic ROM can simulate a closed-loop control for a complex reactor.

Deflection simulation for a needle adjusted by the insertion orientation angle and axial rotation during insertion in the muscle-contained double-layered tissue.

During the process of percutaneous puncture, a medical needle with a beveled tip is usually inserted into a multilayered soft tissue, which contains a muscle layer, before it reaches the target. The robot-assisted needle insertion technology will help brachytherapists to perform high-accuracy seed implantation. To guide the automatic needle insertion, a mechanics-based model to simulate needle deflection caused by the asymmetric cutting force during insertion in the muscle-contained double-layered tissue is developed. The model is driven by the anisotropic and inhomogeneous interaction forces, which are updated off-line according to the tip's position and pose and are independent of the real-time data feedback. The parameterized insertion orientation angle and 180° axial rotation are introduced in the simulating process as the control inputs of the needle to adjust the needle deflection. Simulations and experiments confirm that the cooperative control of the insertion orientation angle and 180° axial rotation can steer the needle towards to any desired target in the reachable region of the tip. The results show that the proposed approach can successfully predict the needle deflection in the muscle-contained double-layered tissue. This work will be useful for a needle automatic control strategy design. Graphical abstract.

A New Coupling Structure and Position Detection Method for Segmented Control Dynamic Wireless Power Transfer Systems

In this letter, a new coupling structure for dynamic wireless power transfer (DWPT) systems is proposed. Bipolar coils are symmetrically placed on the transmitter unipolar coils, resulting in natural decoupling between the bipolar coils and the unipolar coils. This special structure can mitigate the self-couplings between the adjacent unipolar transmitter coils and hence facilitate the design of the compensation circuit. Another remarkable advantage of this design is that it can lead to a stable mutual coupling between the transmitter array and the receiver when the receiver moves along the transmitter, making it a natural fit for DWPT applications. Furthermore, to reduce the electromagnetic interference and power loss, an automatic segmented control scheme is implemented, and a position detection method by monitoring the primary current is developed. The feasibility of the proposed coupling structure and the position detection method are verified on a laboratory prototype with 72-V output voltage. The experimental results show that the power fluctuation is within ±2.5%, and system efficiency is around 90%. (This letter is accompanied by a video demonstrating the experimental test).