Changes In Plant Parameters Research Articles

Performance improvement of DC servo motor using sliding mode controller

<p>Sliding mode control has emerged as a valuable technique for enhancing dynamic response in various fields, including load frequency regulation and remote vehicle applications. While the widely adopted PID controller has proven effective for optimizing control tasks in industries, sliding mode control offers distinct advantages. By controlling the slope of the dynamical trends of state variable behavior, it enables rapid dynamic response with minimal or no overshoot, as well as negligible steady-state error. The robustness of sliding mode control, which makes it highly resilient to changes in plant parameters and outside disturbances, is one of its main advantages. A digital computer simulation was run using Simulink in the MATLAB software, concentrating on a position control system using an armature voltage-controlled D.C. servo motor to assess how well it performed. To learn more about the operation of sliding mode control, several control laws were used and state trajectories were examined. When compared to the conventional tuned PID control, the findings and discussion conclusively show sliding mode control to be more successful. The sliding mode technique has exceptional effectiveness, including enhanced dynamic response, less overshoot, and almost no steady-state error. Furthermore, its robust nature ensures consistent operation even in the face of parameter fluctuations and external disturbances. This study underscores the immense potential of sliding mode control as a powerful alternative to conventional control methods. Its ability to enhance system performance, coupled with its inherent robustness, makes it a compelling choice for various industrial applications where precise control and resilient operation are crucial.</p>

Build a DC to AC Inverter Circuit with IOT Based Load Monitoring

Abstract: The boost inverter or boost DC-AC converter is a new voltage source inverter (VSI) that is suggested in this research. The novel inverter topology's key feature is that, depending on the instantaneous duty cycle, it produces an AC output voltage that is greater than the DC input voltage. This characteristic is not present in the conventional VSI, which consistently generates an AC output instantaneous voltage lower than the DC input voltage. A sliding mode controller is suggested to optimize the boost inverter dynamics while guaranteeing proper functioning under any working circumstance. The primary benefit of sliding mode control over traditional control strategies is its resistance to changes in plant parameters, which results in invariant dynamics and steady-state response in in the best scenario. This study covers operation, analysis, control technique, and experimental outcomes. When an AC voltage greater than the DC link voltage is required, the new inverter is designed to be employed in UPS and AC driver systems without the requirement for a second power conversion stage

An interpretable convolutional neural network for nuclear power plant abnormal events

When an abnormal situation occurs in a nuclear power plant (NPP), operators must properly diagnose the event among hundreds of possible abnormal events. To do so, they monitor changes in plant parameters and confirm the correct abnormal operating procedure when the parameters match the entry conditions described in that procedure. In this process, operators are burdened with a lot of information. The purpose of this study is to optimize the number of main parameters to be monitored for abnormal state diagnosis in NPPs by clarifying the classification process with a deep learning model. To increase the transparency of the trained convolutional neural network model in the diagnosis of 10 different NPP states, we applied three explanation techniques: saliency mapping, guided gradient-weighted class activation mapping, and deep learning important features + Shapley values. These techniques can highlight the particular input parameters that are the most influential to the classification. Each transparency result confirmed that the parameters selected by these techniques can be a key rationale in NPP abnormal state diagnosis. By averaging the results of the two methods with the highest transparency performance, it was possible to intuitively classify all 10 NPP states with only 6 optimized monitoring parameters.

Deep Learning for Robust Adaptive Inverse Control of Nonlinear Dynamic Systems: Improved Settling Time with an Autoencoder.

An adaptive deep neural network is used in an inverse system identification setting to approximate the inverse of a nonlinear plant with the aim of constituting the plant controller by copying to the latter the weights and architecture of the converging deep neural network. This deep learning (DL) approach to the adaptive inverse control (AIC) problem is shown to outperform the adaptive filtering techniques and algorithms normally used in adaptive control, especially when in nonlinear plants. The deeper the controller, the better the inverse function approximation, provided that the nonlinear plant has an inverse and that this inverse can be approximated. Simulation results prove the feasibility of this DL-based adaptive inverse control scheme. The DL-based AIC system is robust to nonlinear plant parameter changes in that the plant output reassumes the value of the reference signal considerably faster than with the adaptive filter counterpart of the deep neural network. The settling and rise times of the step response are shown to improve in the DL-based AIC system.

Variable Rate Seeding in Precision Agriculture: Recent Advances and Future Perspectives

The main objective of this study was to analyze variable rate seeding (VRS) methods and critically evaluate their suitability and effectiveness for the challenges under field conditions. A search was performed using scientific databases and portals by identifying for analysis and evaluation 92 VRS methodologies, their impact and economic benefits depending on the main parameters of the soil and environment. The results of the review identified that VRS could adapt the appropriate seeding rate for each field zone, which was based on site-specific data layers of soil texture, ECa, pH and yield maps. Then, remotely detected images or other data which identify yield-limiting factors were identified. The site-specific sowing method (with a variable sowing rate for each field area) allows the optimization of crop density to obtain the best agronomic and economic results. Various proximal and remote sensor systems, contact and contactless equipment, mapping and VRS modeling technologies are currently used to determine soil and crop variability. VRS depends on the field characteristics’ sowing equipment capabilities, the planned harvest, soil productivity and machine technology interactions with the environment. When forecasting the effective payback of a VRS over the desired period, the farm size should on average be at least 150 ha. In future studies, to achieve the best solutions and optimal methods, it is important to test, evaluate and put into practice the latest methodologies on farms, to perform complex assessments of changes in sensor, soil, plant and environmental parameters.

Online learning based neural network adaptive controller for efficient power tracking of PWR type reactor with unknown internal dynamics

An online learning neural network (NN) based adaptive scheme has been proposed for tracking the power level of higher order point kinetic pressurized water reactor (PWR) with unknown dynamics under local and global load following conditions and emergency conditions. The PWR type of nuclear reactors are linear parameter varying (LPV) systems whose parameters vary with power, ageing effects and changes in nuclear core reactivity with fuel burn up. The drastic changes in plant parameters should be accommodated in a reactor control system through on-line identification to ensures safe operation for the power plant which demands for adaptive control system. To track the demand changes which happens during the load following and emergency conditions considered, the PID controller parameters needs to be varied. However, the proposed NN controller is based on feedback linearisation of nonlinear discrete time system with unknown internal dynamics by using a multilayer neural network acting as function approximator. Online weight tuning algorithm based on a modified delta rule and projection algorithm are used for the update of the weights of the proposed neural network controller along with conventional PID controller. Simulation studies with the proposed controller on a non linear PWR core shows that the proposed algorithm exhibits improved performance in terms of lesser ISE/ IAE/ ITAE over using PID controller under load following conditions. This new proposed power level controller has self learning capability and it needs the measurement of all the state variables and the desired trajectory.

Optimal Quick-Response Variable Structure Control for Highly Efficient Single-Phase Sine-Wave Inverters

This paper puts forward an optimal quick-response variable structure control with a single-phase sine-wave inverter application, which keeps harmonic distortion as low as possible under various conditions of loading. Our proposed solution gives an improvement in architecture in which a quick-response variable structure control (QRVSC) is combined with a brain storm optimization (BSO) algorithm. Notwithstanding the intrinsic resilience of a typical VSC with respect to changes in plant parameters and loading disruptions, the system state convergence towards zero normally proceeds at an infinitely long-time asymptotically, and chattering behavior frequently takes place. The QRVSC for ensuring speedy limited-time convergence with the system state to the balancing point is devised, whilst the BSO will be employed to appropriately regulate the parametric gains in the QRVSC for the elimination of chattering phenomena. From the mix of both a QRVSC together with a BSO, a low total harmonic distortion (THD) as well as a high dynamic response across different types of loading is generated by a closed-loop inverter. The proposed solution is implemented on a practicable single-phase sine-wave inverter under the control of a TI DSP (Texas Instruments Digital Signal Processor). It has experimentally shown the simulation findings as well as the mathematical theoretical analysis, displaying that both quick transient reaction as well as stable performance could be obtained. The proposed solution successfully inhibits voltage harmonics in compliance with IEEE 519-2014’s stringent standard of limiting THD values to less than 5%.

Discrete‐time modified repetitive sliding mode control for uncertain linear systems

SummaryThis article presents a design strategy and stability analysis of modified repetitive sliding mode controller for uncertain linear systems. A modified repetitive controller is adopted to simultaneously track and reject periodic signals. A discrete‐time sliding mode controller is combined to compensate the slow response of repetitive control and to provide robustness against plant parameters uncertainties. Stability analysis is provided to prove boundedness of the proposed control law and the convergence of sliding function and the tracking error. Comparative simulation results demonstrate that the proposed method is able to accurately track reference signal and to reject disturbance with fast transient response. The results also indicate that the closed‐loop system remains stable in the presence of plant parameter changes.

Driving factors influencing the rhizobacteriome community structure of plants adapted to multiple climatic stressors in edaphic savannas

The natural variation of multiple abiotic stresses in hyper-seasonal edaphic savanna provides a unique opportunity to study the rhizobacteriome community structure of plants adapted to climate change-like conditions in the humid tropics. In this study, we evaluated changes in soil, plant and rhizobacteriome community structure parameters across seasons (wet and dry) in two edaphic savannas (SV-1 and SV-5) using four dominant plant species. We then examined relationships between rhizobacteriome community structure and soil properties, plant biomass, and conventional and novel root traits. We further hypothesized that plants adapted to the Aripo Savanna had a core rhizobacteriome, which was specific to plant species and related to root foraging traits. Our results showed that cation exchange capacity (CEC) and the concentration of micronutrients (Fe, Cu and B) were the only soil factors that differed across savanna and season, respectively. Plant biomass traits were generally higher in the dry season, with a higher allocation to root growth in SV-5. Root traits were more plastic in SV-5, and network length-distribution was the only root trait which showed a consistent pattern of lower values in the dry season for three of the dominant plant species. Rhizobacterial community compositions were dominated by Proteobacteria and Acidobacteria, as well as WPS-2, which is dominant in extreme environments. We identified a shared core rhizobacteriome across plant species and savannas. Cation exchange capacity was a major driver of rhizobacterial community assemblies across savannas. Savanna-specific drivers of rhizobacterial community assemblies included CEC and Fe for SV-1, and CEC, TDS, NH4+, NO3−, Mn, K, and network length-distribution for SV-5. Plant factors on the microbiome were minimal, and host selectivity was mediated by the seasonal changes. We conclude that edaphoclimatic factors (soil and season) are the key determinants influencing rhizobacteriome community structure in multiple stressed-environments, which are ecologically similar to the Aripo Savanna.

Control of the temperature in a petroleum refinery heating furnace based on a robust modified Smith predictor

The robust control of the crude oil outlet temperature uniformity in a heating furnace of a petroleum refinery is addressed. A reliable dynamic model of the nominal process has been attained using a system identification procedure based on real-time data. This procedure yields a second order model with a dominant time-delay. A PI controller embedded in a modified Smith predictor structure is therefore proposed. Sensitivity and robustness properties of this control system are analytically obtained. Based on that, a tuning procedure is developed for this control system which has lower sensitivity to disturbances than the standard Smith predictor while guaranteeing the system stability when plant parameters change. Simulations are carried out of the proposed control system and other well-known linear advanced process control systems. The comparison of the obtained results shows the superior performance of our control system in most cases, both in rejecting a variety of disturbances and in maintaining the closed-loop stability when the process parameters change.

Iso-m Based Adaptive Fractional Order Control With Application to a Soft Robotic Neck

This article proposes an adaptive fractional feedback control using recursive least squares algorithm for plant identification and a recent real-time method (iso-m) for fractional controller tuning. The combination of both methods allows keeping the same original performance specifications invariant, combining adaptability and robustness in a single scheme. Thanks to the robust controller, the system performance is maintained around a specified operating point, and due to the adaptive scheme, this operating point is adjusted depending on plant changes. Extensive experimentation of the proposal is carried out in a real platform with non-linear time varying properties, a soft robotic neck made of 3D printer soft materials. The experiments proposed consist in the neck inclination control using tilt sensors installed on the tip. According to expectations, an invariant performance despite plant parameter changes was observed throughout the experiments. The good results obtained in the proposed test platform suggest that the benefits of this control scheme are suitable for other nonlinear time varying applications.

Robust control of a hydraulic cylinder using an observer-based sliding mode control: Theoretical development and experimental validation

This paper focuses on the design of an observer-based sliding mode controller (SMC) for nonlinear hydraulic differential cylinder systems affected by uncertainties. These uncertainties include modeling errors, external disturbances, and measurement noise. The aim is to ensure suitable tracking performance and robustness against unknown inputs. The task of system state and unknown input estimation is performed using high gain Proportional-Integral-Observer (PIO). Input-output exact feedback linearization is used to linearize the system model to be applicable for linear PIO structure. Estimation of system states is directly used in calculation of sliding surface. The SMC is utilized for input-output linearized system to provide a desired performance in the presence of uncertainties and to compensate the effects of external disturbances, plant parameter changes, unmodeled dynamics, measurement noise, etc. Parameter selection of SMC is elaborately considered by defining a novel performance/energy criteria. Stability of closed-loop system is established using Lyapunov method. Furthermore, a complete robustness evaluation considering different levels of measurement noise, modeling errors, and external disturbances is investigated. Experimental results validate the advantages of introduced combined approach in comparison with standard SMC and P-controller approaches. Consequently, integration of state and unknown input estimations into the sliding mode structure leads to appropriate disturbance attenuation and increases the system performance robustness.

A study of the treatment of high-salt chromium-containing wastewater by the photocatalysis-constructed wetland combination method.

In this study, iron ore slag as the photocatalyst was introduced into a constructed wetland simulation system. A comparative experiment of the constructed wetland method and photocatalysis-constructed wetland combination method that treats the high-salt chromium-containing wastewater was carried out. The best hydraulic retention time (HRT) of the photocatalysis-constructed wetland combination system was studied. The effects of these two methods on biochemical oxygen demand (BOD5), chemical oxygen demand (COD) removal and Cr(VI) reduction rate of the high-salt chromium-containing wastewater were analysed after 14 periods. The results showed that under the optimal HRT of 4 hours, the COD and BOD5 of the wastewater reduced by 47% and 31%, and the reduction rate of Cr(VI) was 83% separately in the constructed wetland system. The COD and BOD5 of the wastewater reduced by 83% and 42%, and the reduction rate of Cr(VI) was 96% separately in the photocatalysis-constructed wetland combination method system. At the same time, the changes in plant parameters under these two systems were studied, and the results showed that the addition of photocatalyst and hydrogen peroxide to constructed wetlands did not affect the normal indicators of plant growth. The results showed that the photocatalysis-constructed wetland combination method not only reduced the treatment time greatly, but also improved the quality of the treated wastewater significantly.

Assessment of common plant parameters as biomarkers of air pollution.

Air pollution is a very serious current environmental issue of human society. Large parts of countries, especially the densely populated cities, having high vehicular movement, industries, and factories, are worst affected. Biomarkers are changes in plant parameters that help in easy assessment of the environmental quality of an area at a certain time. Plants can react to different environmental stresses with the most evident responses shown by the leaves. In the present work, we have studied changes in biochemical parameters in the leaves of a mango plant (Mangifera indica), a very common plant in West Bengal, India, which were collected from four different locations in the city of Kolkata which has a high concentration of air pollutants and one control area from a rural region having a low concentration of air pollutants. It was observed that leaves which were exposed to high amounts of harmful air pollutants showed higher accumulation of molecules such as phenol, proline, malondialdehyde, and cellulose with lower amounts of chlorophyll. From this, we can observe that common environmental stress such as air pollution leads to a change in the synthesis of bioactive molecules to resist the effect of stress on the plant. Thus, from these data, it can be concluded that biochemical parameters can serve as efficient biomarkers of the air quality of an area.

IMPROVING OF ELECTROMECHANICAL STABILIZATION SYSTEMS ACCURACY

Aim. Improving of accuracy parameters and reducing of sensitivity to changes of plant parameters for nonlinear robust tank main armament guidance and stabilization electromechanical systems based on synchronous motor with permanent magnets and vector control. Methodology. The method of multiobjective synthesis of nonlinear robust control by nonlinear tank main armament stabilization electromechanical system taking into account the elastic oscillations of the tank gun barrel as a discrete-continuous plant and with parametric uncertainty based on the multiobjective optimization. The target vector of robust control choice by solving the corresponding multicriterion nonlinear programming problem in which the calculation of the vectors of the objective function and constraints is algorithmic and associated with synthesis of nonlinear robust controllers and modeling of the synthesized system for various modes of operation of the system, with different input signals and for various values of the plant parameters. Synthesis of nonlinear robust controllers and non-linear robust observers reduces to solving the system of Hamilton-Jacobi-Isaacs equations. Results. The results of the synthesis of a nonlinear robust tank main armament guidance and stabilization electromechanical systems are presented. Comparison of the dynamic characteristics of the synthesized tank main armament stabilization electromechanical systems showed that the use of synthesized nonlinear robust controllers allowed to improve the accuracy parameters and reduce the sensitivity of the system to changes of plant parameters in comparison with the existing system. Originality. For the first time carried out the multiobjective synthesis of nonlinear robust tank main armament stabilization electromechanical systems. Practical value. Practical recommendations are given on reasonable choice of the gain matrix for the nonlinear feedbacks of the regulator and the nonlinear observer of the tank main armament stabilization electromechanical systems, which allows improving the dynamic characteristics and reducing the sensitivity of the system to plant parameters changing in comparison with the existing system.

Changes in Optical Plant Parameters with Respect to the Degree of Soil Salinity in Arid Lands

We have carried out a bioindication of the soil–plant system of North Dagestan using optical plant parameters based on ecological and physiological indicating features. The high soil salinity and manifestation of aridization features indicate an urgent need in soil bioindication. We have revealed indicator plants, the fluorescent response of which makes it possible to reveal and diagnose soil degradation features in a timely way.

Suggestions for plant parameters to monitor potential CO2 leakage from carbon capture and storage (CCS) sites

AbstractPotential CO2 leakage from carbon capture and storage (CCS) facilities should be monitored and managed carefully. Biomonitoring technologies for CO2 leakage using plants could be very effective, especially in areas near transport pipelines, because of the extensive areas that they can cover. A greenhouse study was conducted to investigate whether early changes in plant parameters could be an effective indicator to detect leaked CO2. Corn (Zea mays), which was reported to be a CO2‐tolerant species, was selected to identify specific indicators of CO2 leakage. The mean soil CO2 concentration for soil treated with CO2 was 20–40%, which is similar to the concentration that could occur near transport pipelines through slow‐insidious seepage. In the soil treated with CO2, the number of yellow leaves at the canopy level was significantly increased from the sixth day of CO2 injection compared to the control. The chlorophyll content in green leaves was significantly reduced on the eighth day from CO2 injection. The soil water content in the treated soil was increased from the sixth day of injection due to reduced root adsorption. The results of this study implied that early changes in parameters of CO2‐tolerant species such as canopy‐level discoloration and chlorophyll content could be used as CO2‐specific indicators for the detection of soil CO2 leakage. We suggested that canopy‐level monitoring is a promising, useful technique for soil CO2 leakage in extensive areas, such as near CO2 transport pipelines. In addition, rhizosphere‐level parameters, such as soil water content, could be good subsidiary indicators together with the large‐scale collection of baseline data and statistical analysis. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd.

IMPROVING OF ELECTROMECHANICAL SERVO SYSTEMS ACCURACY

Aim. Improving of accuracy parameters and reducing of sensitivity to changes of plant parameters of nonlinear robust electromechanical servo systems of guidance and stabilization of lightly armored vehicle weapons based on multiobjective synthesis. Methodology. The method of multicriterion synthesis of nonlinear robust controllers for controlling by nonlinear multimass electromechanical servo systems with parametric uncertainty based on the choice of the target vector of robust control by solving the corresponding multicriterion nonlinear programming problem in which the calculation of the vectors of the objective function and constraints is algorithmic and associated with synthesis of nonlinear robust controllers and modeling of the synthesized system for various modes of operation of the system, with different input signals and for various values of the plant parameters. Synthesis of nonlinear robust controllers and non-linear robust observers reduces to solving the system of Hamilton-Jacobi-Isaacs equations. Results. The results of the synthesis of a nonlinear robust electromechanical servo system for the guidance and stabilization of lightly armored vehicle weapons are presented. Comparison of the dynamic characteristics of the synthesized servo electromechanical system showed that the use of synthesized nonlinear robust controllers allowed to improve the accuracy parameters and reduce the sensitivity of the system to changes of plant parameters in comparison with the existing system. Originality. For the first time carried out the multiobjective synthesis of nonlinear robust electromechanical servo systems of guidance and stabilization of lightly armored vehicle weapons. Practical value. Practical recommendations are given on reasonable choice of the gain matrix for the nonlinear feedbacks of the regulator and the nonlinear observer of the servo electromechanical system, which allows improving the dynamic characteristics and reducing the sensitivity of the system to plant parameters changing in comparison with the existing system.

Sensitivity of SAR Tomography to the Phenological Cycle of Agricultural Crops at X-, C-, and L-band

Understanding the impact of soil and plant parameter changes in agriculture on Synthetic Aperture Radar (SAR) measurements is of great interest when it comes to monitor the temporal evolution of agricultural crops by means of SAR. In this regard, specific transitions between phenological stages in corn, barley, and wheat have been identified associated to certain dielectric and geometric changes, based on a time series of fully polarimetric multibaseline SAR data and in situ measurements. The data have been acquired in the frame of DLR's CROPEX campaign on six dates between May and July in 2014. The experiments reported in this paper address the sensitivity of X-, C-, and L-band to phenological transitions exploiting the availability of multiple baselines on each acquisition date. The application of tomographic techniques enables the estimation of the three-dimensional (3-D) backscatter distribution and the separation of ground and volume scattering components. Tomographic parameters have been derived at different frequencies, namely the center of mass of the profiles of the total and of the volume-only 3-D backscatter, and the ground and volume powers. Their sensitivity and ability to detect changes occurring on the ground and in the vegetation volume have been evaluated focusing on the added value provided by the 3-D resolution at the different frequencies and polarizations available.

Robust Predictive Sliding Mode Control for Multiterminal HVDC Grids

In this paper, a robust nonlinear control scheme is proposed for voltage source converter based multiterminal HVDC grids, based on the predictive sliding mode control (PSMC). This control is a hybrid of the multivariable nonlinear model predictive control and sliding mode control. The particle swarm optimization method is applied to obtain the optimal control parameters. This control fulfills the objectives of regulating terminal dc voltage, active and reactive power, improved dynamic performance, and superior robust control operation even with plant parameter changes and external measurement disturbances. The performance of the proposed PSMC scheme is compared with the optimal synergetic control and optimal PI. The results confirm the superior transient performance and robustness of the PSMC scheme.