Design Of Control Strategies Research Articles

Development of new framework for order abatement and control design strategy

Development of new framework for order abatement and control design strategy

Counter-rotating synchronization theory and experiment of tri-rotor actuated with dual-frequency considering adaptive global sliding mode control strategy

In petroleum drilling engineering, self-synchronous vibration screen with single-frequency actuation is difficult to implement the scheduled synchronous behavior caused by its invariance and instability of dynamic characteristics, which usually leads to reduction of the production efficiency. Hence, to solve the problem mentioned above, a dual-frequency counter-rotating synchronization control method among three exciters is presented by combining adjacent cross-coupling control (ACCC) structure with global sliding mode control (GSMC) theory. Firstly, motion differential equation of the vibration system in each direction is derived according to mathematical model of the tri-rotor vibration machine. Secondly, considering exponential reaching law and adaptive control algorithm, the adjacent cross-coupling controller and the vector controller of each induction motor are investigated to achieve the stable zero phase difference synchronization between double-frequency rotors. Subsequently, Runge-Kutta method is introduced to establish the electromechanical coupling control model and prove validity of the control theory. Meanwhile, it is also discussed that the rotors cannot obtain an ideal self-synchronization state since velocity of high-frequency motor is not strictly equal to twice that of low-frequency motor. Finally, based on the experimental test scheme, an experimental prototype related to controlled multi-rotor synchronization vibration system is designed independently, which further verifies the validity of theory and computer simulation. Research shows that the desirable synchronous state of the vibration system can be accurately controlled via the designed control strategy, and the control system can implement diversity of the dynamical characteristics, due to the existence of strong robustness.

Distributed cooperative control for global power sharing management of interconnected microgrids in flexible multi-terminal DC connection scheme

Flexible multi-terminal direct current (MTDC) connection scheme is an ideal way to integrate multiple adjacent microgrids and form interconnected microgrids (IMGs). However, under the traditional control mode, MTDC will lead to frequency/voltage decoupling among all the microgrids. Moreover, improper control scheme design of bidirectional interlinking converters (BICs) in MTDC will make global active/reactive power sharing management impossible, which is considered as the significant task in IMGs. Therefore, a novel distributed cooperative control strategy is designed in this article. In the proposed control strategy, frequency/voltage regulations and local active/reactive power sharing management are achieved through distributed secondary control within each MG. Accurate global active/reactive power sharing management is realized by coordinating the power exchange instructions of each BIC in a distributed manner. The control dynamics of these two levels are designed to be decoupled. The stability of the proposed control strategy is demonstrated by the Lyapunov method, and its effectiveness is verified by simulating a test IMG system in MATLAB/Simulink.

Control of DC-DC boost converter in discontinuous conduction mode feeding a constant power load

This article presents a new control strategy for a DC-DC boost converter used to supply a constant power load. The designed controller allows regulates the converter output voltage to a required reference value. By designing the controller based on the reduced order averaged model of the converter and using a nonlinear control technique based on feedback linearization, a simple-to-implement and stable controller for both operating modes of the converter (continuous and discontinuous conduction modes respectively) is obtained. The designed control strategy is validated through simulation and experimental results, demonstrating good dynamic performance and stability in both conduction modes, under variations in reference values and significant load changes. In addition, a comparison of the performance and implementation costs between the control strategy proposed in this paper and two strategies proposed by other authors is included.

Distributed resilient adaptive consensus tracking control of nonlinear multi-agent systems dealing with deception attacks via K-filters approach

It poses technical difficulty to achieve distributed consensus tracking control with input quantizations and deception attacks for nonlinear multi-agent systems subject to mismatched parametric uncertainties via backstepping design. The underlying problem becomes even more complicated because i) the system contains mismatched uncertainties; ii) the output becomes unavailable after attacks, making the conventional recursive control design strategy using traditional error surfaces unsuitable; iii) since only the compromised output signal is available, the impacts of nonlinear items related to unknown attack weights become difficult to be explicitly addressed in the existing available output feedback control results. In this paper, we present a solution to circumvent these obstacles by constructing a set of new K-filters using compromised output only, which, together with a coordinate transformation involving the attacked output and filter variables, allow a new form of backstepping based distributed resilient adaptive control protocol to be developed. Meanwhile, an extra compensation term is incorporated into the real controller to handle the effect of quantization on the system stability. It is shown that, with proper choices of Lyapunov functions, the global uniform boundedness of all the closed-loop signals and the asymptotically output consensus tracking can be achieved. A practice example is provided to verify the benefits of our scheme.

First Report on the Molecular Detection and Genetic Characterization of Toxoplasma gondii From Donkeys in Kenya.

The present study was conducted to determine the presence of Toxoplasma gondii in donkeys by molecular tests and genetic diversity analysis of the obtained DNA samples from central Kenya. A total of 363 blood samples were collected from donkeys in Meru and Kirinyaga Counties, and 96 samples that were previously seropositive for T. gondii using indirect ELISA were subjected to nested PCR based on the amplification of the internal transcribed spacer 1 (ITS-1) gene followed by DNA sequencing and phylogenetic analysis. Genotyping was performed on 15 selected positive samples using multilocus nested polymerase chain reaction restriction fragment length polymorphism (Mn-PCR-RFLP) with eight genetic markers ('SAG 2, 5'SAG 2, Alt. SAG 2, SAG 3, GRA 6, C29-2, BTUB and L358). Toxoplasma gondii DNA was detected in 36.5% (35/96) of the blood samples. The sequences obtained exhibited 98.2-99.5% homology with those deposited in GenBank. Phylogenetic analysis demonstrated that the obtained sequences are conserved and clustered with those of infecting animals from other regions of the world. Eighteen distinct T. gondii haplotypes were identified to be circulating in donkeys from central Kenya. The T. gondii DNA samples exhibited high haplotype diversity (Hd: 0.915) and limited genetic diversity (π = 0.01027). PCR-RFLP of T. gondii DNA-positive samples revealed three different genetic combinations that consisted of alleles I, II and III, indicating the dissemination of atypical genotypes. This study demonstrated that T. gondii is widespread in donkeys from Kenya and could be a possible source of infection in humans. These findings are important for designing control strategies for this parasite to improve the livestock sector, which is one of the main sources of livelihood for farmers in Kenya.

Feedback Control Design Strategy for Stabilization of Delayed Descriptor Fractional Neutral Systems with Order 0 < ϱ < 1 in the Presence of Time-Varying Parametric Uncertainty

Descriptor systems are more complex than normal systems, which are modeled by differential equations. This paper derives stability and stabilization criteria for uncertain fractional descriptor systems with neutral-type delay. Through the Lyapunov–Krasovskii functional approach, conditions subject to time-varying delay and parametric uncertainty are formulated as linear matrix inequalities. Based on the established criteria, static state- and output-feedback control laws are designed to ensure regularity and impulse-free properties, together with robust stability of the closed-loop system under permissible uncertainties. Numerical examples illustrate the effectiveness of the control methods and show that the results depend on the range of variation in the delays and on the fractional order, leading to stability analysis results that are less conservative than those reported in the literature.

Design of adaptive cruise control strategy for EREV considering driving behavior

Design of adaptive cruise control strategy for EREV considering driving behavior

Model Predictive Control for Level Control of a Conical Tank

Conical tanks have a high application rate in industrial processes, especially in colloidal mills, chemical processes, and food processing. The use of conical tanks presents significant benefits because they contribute to sedimentation and reduce the accumulation of impurities compared to conventional cylindrical tanks. However, level control of a conical tank due to its shape requires advanced strategies to guarantee efficient control. In this research, a model predictive control (MPC) method was designed and implemented for the level control of a conical tank on a laboratory scale. To evaluate the performance of the controller, it was compared with a traditional proportional–integral (PI) controller, and the rise time, settling time, overshoot, and error in the steady state were analyzed when different set point changes were tested. In addition, the system was subjected to disturbances, and the MPC demonstrated better performance in a transient state, as well as smooth and stable action controls that allowed for an increase in the useful life of the actuator. In addition, an interactive graphical interface was developed that allowed a dynamic response in a real plant to be experienced; this provides an academic tool for designing control strategies before implementation in a real process.

An adaptive fuzzy coordinated control strategy for hybrid electric vehicles considering clutch wear and engine temperature variation

AbstractAn improved method of clutch coordinated control based on the Kalman filter was proposed to solve the problem that the existing mode switching strategy of hybrid electric vehicles could not adapt to engine temperature changes and clutch wear. First, taking advantage of the relationship between the torque transmitted by the clutch and the starting resistance of the engine, combined with the characteristics of the clutch, the clutch wear was roughly calculated. Accordingly, the control strategy of the clutch in the existing mode switching was improved to adapt to the clutch wear. The adaptive control strategy proposed for clutch wear included the fuzzy control module of the initial engagement pressure, the fuzzy inference module of the clutch engaging pressure change, the clutch wear estimation module and so on. Second, the Kalman filter was used to process the results to improve the estimation accuracy of clutch wear. The engine starting resistance related to starting speed and temperature was modeled to enhance the adaptability of the control strategy to engine temperature. Finally, the designed control strategy was verified in simulation. The results show that the improved control strategy can complete the mode switching when the engine temperature is variable and the clutch is worn. The maximum impact degree increased from 5 m/s3 without wear to 8.5 m/s3 with wear, but it is still less than the index limit, and it can be considered that the proposed strategy can achieve the desired control effect. The fuzzy control algorithm proposed enhances the vehicle's ride comfort during mode switching from pure electric driving to hybrid driving.

MFRL-BI: Design of a model-free reinforcement learning process control scheme by using Bayesian inference

Design of the process control scheme is critical for quality assurance to reduce variations in manufacturing systems. Taking semiconductor manufacturing as an example, extensive literature focuses on control optimization based on certain process models (usually linear models), which are obtained by experiments before a manufacturing process starts. However, in real applications, pre-defined models may not be accurate, especially for a complex manufacturing system. To tackle model inaccuracy, we propose a Model-Free Reinforcement Learning (MFRL) approach to conduct experiments and optimize control simultaneously according to real-time data. Specifically, we design a novel MFRL control scheme by updating the distribution of disturbances using Bayesian inference to reduce their large variations during manufacturing processes. As a result, the proposed MFRL controller is demonstrated to perform well in a nonlinear chemical mechanical planarization process when the process model is unknown. Theoretical properties are guaranteed when disturbances are additive. The numerical studies also demonstrate the efficiency of our methodology.

Iterative learning resilient consensus of uncertain nonlinear multi-agent systems vulnerable to false data injection attacks

This work investigates the iterative learning resilient consensus of uncertain nonlinear high-order multi-agent systems (MASs) against false data injection (FDI) attacks. First, in order to reduce the impact of FDI attacks on the nonlinear MASs, a novel coordinate transformation technique is proposed, which is composed of the states after being attacked, and the Nussbaum gain technique is adopted to address the problem of unknown attack gains resulting from FDI attacks. Then, by employing compromised state variables, a fuzzy adaptive iterative learning resilient control method is presented based on the composite energy function, where unknown nonlinear functions are handled using fuzzy logic systems. The presented approach can guarantee that the outputs of all followers precisely track the leader in a limited time interval while ensuring that all closed-loop signals are bounded. Finally, a numerical simulation is provided to demonstrate the efficacy of the designed control strategy.

Nonsingular fixed-time K-filter output-feedback control for high-order nonlinear multi-agent systems

This paper investigates the problem of nonsingular fixed-time control for high-order nonlinear multi-agent systems with unmeasurable states and disturbances. To address the unmeasurable states, a K-filter is introduced to reconstruct the system with the input and output only and to facilitate the construction of the observer. To provide an approximation for the unknown nonlinear functions, the fuzzy logic system technique is applied. A novel tracking controller is proposed to ensure that all signals remain bounded for the closed-loop system and that the tracking error converges into a small and adjustable set within a fixed time by utilising the adaptive backstepping recursive technology and the adding power integrator technique. Meanwhile, the singular problems arising from the fixed-time stability criterion can be also avoided. Finally, a simulation experiment verifies the effectiveness of the designed control scheme.

Vehicle dynamics analysis and adaptive control scheme design under coupled slope for trajectory tracking of four-wheel independent drive autonomous vehicles

Trajectory tracking is the core function of autonomous vehicle motion control. On variable road slopes, vehicles will encounter extra forces due to gravity, distinct from driving on flat roads. These forces will cause significant deviations in trajectory tracking and even instability, especially for four-wheel independent drive autonomous vehicles (4WIDAVs) with closely coupled longitudinal-lateral dynamics. To address above issues, first, a modified dynamics model considering coupled slopes is built, and the slope effect on vehicle motion and stability is analysed. Secondly, treating coupled slopes as augmented states, a square root cubature Kalman filter (SRCKF) is designed for real-time slope estimation. Then, a slope-adaptive model separate predictive trajectory tracking control strategy is proposed, decoupling the slope-adaptive tracking system into longitudinal and lateral subsystems. Predictive control is performed separately to ensure real-time performance, and prediction information is exchanged between subsystems to preserve dynamics coupled characteristics, enhancing tracking accuracy. Finally, the effectiveness of the proposed scheme is validated by simulations and real-vehicle experiment under the varying slope condition. Results show that the proposed scheme improves tracking accuracy by 17.59% and reduces computation time by 55.02% compared to existing scheme in simulations, and in experiment, the tracking error is reduced by 17.89%.

Factors associated with the spatiotemporal distribution of dog rabies in Tunisia.

Despite continuous efforts of veterinary services to control rabies in dogs since 1982, rabies remains a cause of death in Tunisia, with more than five reported human cases in 2022. As little is known on the determinants of transmission of rabies in dogs, better understand which factors contribute to its spatial heterogeneity in Tunisia is critical for developing bespoke mitigation activities. In this context, we developed Bayesian Poisson mixed-effect spatio-temporal model upon all cases of rabid dogs reported in each delegation during the period from 2019 to 2021. The best fitting model highlighted the association between the risk of rabies and the mean average monthly temperature, the density of markets and the density of dogs in delegations. Interestingly, no relationship was found between intensity of vaccination in dogs and the risk of rabies. Our results provided insights into the spatio-temporal dynamics of dog rabies transmission and highlighted specific geographic locations where the risk of infection was high despite correction for associated explanatory variables. Such an improved understanding represent key information to design bespoke, cost-efficient, rabies prevention and control strategies to support veterinary services activities and policymaking.

Smart ArM: a customizable and versatile robotic arm prosthesis platform for Cybathlon and research

BackgroundIn the last decade, notable progress in mechatronics paved the way for a new generation of arm prostheses, expanding motor capabilities thanks to their multiple active joints. Yet, the design of control schemes for these advanced devices still poses a challenge, especially with the limited availability of command signals for higher levels of arm impairment. When addressing this challenge, current commercial devices lack versatility and customizing options to be employed as test-beds for developing novel control schemes. As a consequence, researchers resort to using lab-specific experimental apparatuses on which to deploy their innovations, such as virtual reality setups or mock prosthetic devices worn by unimpaired participants.MethodsTo meet this need for a test-bed, we developed the Smart Arm platform, a human-like, multi-articulated robotic arm that can be worn as a trans-humeral arm prosthesis. The design process followed three principles: provide a reprogrammable embedded system allowing in-depth customization of control schemes, favor easy-to-buy parts rather than custom-made components, and guarantee compatibility with industrial standards in prosthetics.ResultsThe Smart ArM platform includes motorized elbow and wrist joints while being compatible with commercial prosthetic hands. Its software and electronic architecture can be easily adapted to build devices with a wide variety of sensors and actuators. This platform was employed in several experiments studying arm prosthesis control and sensory feedback. We also report our participation in Cybathlon, where our pilot with forearm agenesia successfully drives the Smart Arm prosthesis to perform activities of daily living requiring both strength and dexterity.ConclusionThese application scenarios illustrate the versatility and adaptability of the proposed platform, for research purposes as well as outside the lab. The Smart Arm platform offers a test-bed for experimenting with prosthetic control laws and command signals, suitable for running tests in lifelike settings where impaired participants wear it as a prosthetic device. In this way, we aim at bridging a critical gap in the field of upper limb prosthetics: the need for realistic, ecological test conditions to assess the actual benefit of a technological innovation for the end-users.

Design of hospitals for the post-pandemic era: an international survey of design professionals on infection control design strategies

ABSTRACT The COVID-19 pandemic placed unpreceded stress on healthcare systems, highlighting their deficiencies and vulnerabilities. The risk of cross-infection among patients and healthcare workers increased significantly, hindering the overall healthcare process. Hospital design should meet the challenges that will be posed by future pandemics, especially infection control. This paper reports on the results of an online survey for healthcare design professionals and academics from across the globe. The questionnaire addressed how hospital design can contribute to the infection control during future pandemic situations. Respondents agreed that the benefits of designing hospitals to meet the needs of pandemic/emergency situations outweigh the additional cost, and that infection control should play a more pronounced role when designing hospitals for the coming era. Results indicate that the overall organization of hospitals should allow for the compartmentation of critical/isolation departments/units and the creation of separate circulation routes to suit this compartmentation during pandemic times. The circulation system of the emergency departments and Intensive Care Units (ICU) should allow for the separation of infectious patients from others during pandemic times. 20–30% of the ICU beds of tertiary level care hospitals should be designed as single patient rooms. 53% of these rooms should be easily convertible to Air-Borne-Infection Isolation (AII) rooms, 44% should have an additional space for adding anterooms during pandemics, and 55% of these rooms should have permanent storage spaces for Personal Protective Equipment. The inpatient units should also be designed to support infection control. AII rooms should be grouped in designated inpatient units.

Predefined-Time Consensus Tracking Control for Multiagent System With Channel Fading.

This article proposes a predefined-time leader-following consensus control scheme for a second-order multiagent system (MAS) whose communication network is subject to channel fading. New distributed observers are designed to achieve prescribed-time leader's states estimation under undirected graph and digraph over faded communication channel. Then, a new adaptive dynamic surface predefined-time control is developed for the elimination of the mismatched disturbances stemmed from estimation error and achieving practical predefined-time leader-following consensus. It is proved that the developed control approach achieves predefined-time consensus tracking. This article's contribution is to propose novel distributed observers to remove the influence of channel fading and estimate leader's states under undirected graph and digraph within prescribed time and develop a novel predefined-time control to achieve predefined-time consensus tracking over fading channel. A simulation example verifies that the designed control scheme is effective.

Design and application of multi-level grid agc collaborative control scheme adapting to distributed new energy control

Design and application of multi-level grid agc collaborative control scheme adapting to distributed new energy control

Nonsingular Fixed-Time Asymptotic Consensus for Nonlinear Nonstrict-Feedback MASs.

In this article, a fuzzy adaptive fixed-time asymptotic consistent control scheme is developed for a class of nonlinear multiagent systems (NMASs) with a nonstrict-feedback (NSF) structure. In the control process, a fixed-time consistency control method without control singularity is proposed by combining fuzzy logic systems (FLSs) with good approximation capability, fixed-time stability theory, and plus power integration techniques. Then, by using Barbalat's Lemma, the asymptotic stability of tracking errors and the boundedness of the controlled systems are successfully achieved, which means that the tracking errors can converge to zero in a fixed time. Finally, the effectiveness of the designed control scheme is demonstrated by a simulation example.