Standard Control Scheme Research Articles

Event-Triggered Backstepping Control of Fractional-Order Chaotic Systems with Dead Zone Via Disturbance Observer

This paper comes up with an adaptive event-triggered tracking control scheme for a kind of fractional-order strict feedback nonlinear systems with actuator dead-zone inputs and uncertain external disturbances. The presented strategy can overcome the defect in standard backstepping control scheme which leads to the “explosion of complexity” problem of virtual signals, and strengthen the transmission efficiency by an event-triggered scheme. An error compensation mechanism is presented for promoting the tracking accuracy and reducing the effect filter error. The designed controller guarantees that the tracking error converges into a small neighborhood near the origin, and all closed-loop signals are bounded. Moreover, Zeno behavior will be avoided. The numerical simulation is given to verify the accuracy and effectiveness of the designed technique.

Event-Triggered Adaptive Neural Network Tracking Control for Uncertain Systems With Unknown Input Saturation Based on Command Filters.

This brief presents a modified event-triggered command filter backstepping tracking control scheme for a class of uncertain nonlinear systems with unknown input saturation based on the adaptive neural network (NN) technique. First, the virtual control functions are reconstructed to address the uncertainties in subsystems by using command filters. A piecewise continuous function is employed to deal with the unknown input saturation problem. Next, an event-triggered tracking controller is developed by utilizing the adaptive NN technique. Compared with standard NN control schemes based on multiple-function-approximators, our controller only requires a single NN. The closed-loop system stability is analyzed based on the Lyapunov stability theorem, and it is shown that the Zeno behavior is also avoided under the designed event-triggering mechanism. Simulation studies are performed to validate the effectiveness of our controller.

Synchronization of complex networks with PID pinning controller

Recently, synchronization of complex networks has been studied deeply; however, most existing works take the standard proportional control strategy approach to pinning controllers, which produces a steady-state error and has a significant time of fluctuation. In order to solve this problem, we propose a new method using the Proportional-Integral-Differential (PID) control to design a pinning control method for synchronization of complex networks. Based on Lyapunov theory, the stability of the network is proved, and sufficient synchronization conditions are obtained. Finally, examples are given to evaluate the effectiveness of the proposed PID pinning method and compare it to the traditional proportional pinning controller and adaptive pinning controller.

Dual-threshold control architecture for improved thermal and power management in open-cathode direct methanol fuel cell

A new dual-threshold cascade control scheme is proposed for water management in direct methanol fuel cells. The control architecture is constructed with the goal of adjusting the liquid-level of a methanol solution in an internal storage tank, where this control structure is based on a set point constrained by upper and lower thresholds. The logic of the algorithm is driven by a noninverting and an inverting Schmitt triggers comprising a switching function that sets the master controller on manual mode for periods of time during which the operating temperature of the electricity-generating fuel cell’s stack can be fixed at an optimal value. Automatic control is implemented when either threshold is reached, triggering a level recovery action where the temperature is manipulated to promote or inhibit cathodic evaporative water losses, depending on the existing perturbations. Manual mode is reinstated when a chosen mid liquid-level target is reached. The strategy seeks to avoid water flooding and dry-out states in the device, while ensuring constant power and current production during periods of manual operation. Simulation studies show that the new control method is immune to severe perturbations, which stands in contrast to a standard cascade control scheme that causes significant degradation and unstable electrical performance in the cell under analogous conditions. Unlike the standard cascade control where the operating temperature is seldom close to the optimal operating point, the improved dual-threshold logic is superior such that the desired temperature of operation can be realized during the entire manual mode, enhancing the performance of the variables that critically depend on temperature. The new method realizes a successful power transition from 30 to 40W, demonstrating its ability to meet changes in load demands, where this transfer is implemented by altering the operational temperature from 45°C to 60°C at a constant voltage. The new strategy can be incorporated into a higher-level optimization control layer, where different temperature operating points can be chosen based on user-defined performance criteria. The study focuses on a case of two different points of the polarization curve corresponding to a power of 40W. While the operating point at a current of 2.51A and temperature of 57.5°C has a higher voltage efficiency of 10%, the second point operating at 2.79A and 50°C shows a boost by 7.6% in fuel efficiency over the first point. Hence, a user can choose the optimal operating condition based on specific performance metrics while simultaneously meeting the load demands. Few recommendations are presented to advance this work in the future.

Overvoltage Suppression Strategy of LCC-HVDC Delivery System Based on Hydropower Phase Control Participation

In a high-voltage direct current (HVDC) transmission system, commutation failure at the receiving end may lead to transient overvoltage at the sending end converter bus of the weak alterative current (AC) system. Firstly, the principle calculation method of overvoltage generation at the sending end after commutation failure is analyzed. Combined with the output characteristics of the hydroelectric excitation system, a coordinated control strategy for hydroelectric and DC systems is proposed. Since the voltage and current values at the DC outlet of the rectifier side change first after a fault occurs at the receiving end, the relationship equation between DC voltage and AC bus voltage is derived and it is used as an input signal to construct additional excitation control for hydropower stations. The proposed strategy is verified by establishing a simulation hydrogen–wind–solar model bundled via a DC sending system in PSCAD/EMTDC. The simulation results illustrate that the transient overvoltage suppression rates are all more than 35%, and the maximum is 38.53%. The proposed strategy can reduce the overvoltage by 0.126 p.u. compared with the International Council on Large Electric Systems (CIGRE) standard control strategy.

A Composite Super-Twisting Sliding Mode Approach for Platform Motion Suppression and Power Regulation of Floating Offshore Wind Turbine

The floating platform motion of an offshore wind turbine system can exacerbate output power fluctuations and increase fatigue loads. This paper proposes a new scheme based on a fast second-order sliding mode (SOSM) control and an adaptive super-twisting extended state observer to suppress the platform motion and power fluctuation. Firstly, an affine nonlinear model of the floating wind turbine pitch system is constructed. Then, a fast SOSM pitch control law is adopted to adjust the blade pitch angle, and a new adaptive super-twisting extended state observer is constructed to achieve total disturbance observation. Finally, simulations are conducted under two cases of wind and wave conditions based on FAST (fatigue, aerodynamics, structures, and turbulence) and MATLAB/Simulink. Compared with the traditional proportional integral (PI) control scheme and standard super-twisting control scheme, the platform roll under the proposed scheme is reduced by 13% and 4%, and pitch is reduced by 16% and 3% in Case 1. Correspondingly, the roll is reduced by 9% and 15%, and pitch is reduced by 7% and 1% in Case 2. For the tower top pitch and yaw moment, load reductions of 7% and 3% or more are achievable compared with those under the PI control scheme. It is indicated that the proposed scheme is more effective in suppressing floating platform motion, stabilizing output power of the wind turbine system, and reducing tower loads.

A field study on the impacts of cold island effect on operating performances of air source heat pump array for space heating

Connecting multiple air source heat pump units in parallel to form an air source heat pump array (ASHP-A) is an important heating approach for central heating in small areas. At a low wind speed, the colder air stream exhausted by outdoor coils of ASHP units cannot effectively mixed with the ambient air and repeatedly return to the outdoor coil for heat exchange, resulting in the local air temperature inside the ASHP-A lower and lower and forming the cold island effect (CIE). CIE may seriously deteriorate the heating efficiency of ASHP units in buildings. However, the impacts of CIE on the operating performances of an ASHP-A were insufficiently studied in existing studies but necessary for developing a reasonable installation standard and efficient control strategy. Therefore, to reveal the impacts of CIE on the operating performances of ASHP units in ASHP-A, the characteristics of CIE, and its impacts on the operating performances of the field ASHP-A were experimentally carried out. The results demonstrated that CIE significantly existed in the field ASHP-A and resulted in frequent defrosting operations and lower heating performances. The defrosting frequency of Unit 1 having a more severe CIE was increased by 243.5 %. The averaged output heating capacity and COP in Unit 1 declined by 27 % and 27.7 %, and these for Unit 2 by 13.6 % and 14.8 %, respectively. The frosting-defrosting loss coefficient for Unit 1 was increased by 519 % relative to Unit 2. This study is valuable to promote the development of installation standards by taking the CIE into consideration for improving heating efficiency of ASHP-A in buildings.

Quadrotor trajectory tracking based on backstepping control and radial basis function neural networks

In this paper we introduce a novel method for solving the trajectory tracking problem for a quadrotor system based on backstepping control and radial basis function (RBF) neural networks. Backstepping controllers in quadrotor control are designed so as to guarantee Lyapunov stability of the closed-loop system based on a dynamic model of the quadrotor, derived using first principles equations. Such a model is prone to modeling errors due to various factors, including uncertainties, time varying quadrotor parameters and external disturbances acting on the vehicle, thus compromising the controller performance. To remedy this situation, we couple the controller with a radial basis function (RBF) neural network trained with the fuzzy means (FM) algorithm, which allows the proposed RBF – backstepping framework to take into account unmodeled dynamics with increased accuracy, thus improving the tracking performance. The resulting method is evaluated on a detailed quadrotor model over trajectories with distinct geometrical characteristics, and compared with a backstepping controller using a different neural network architecture and a standard backstepping control scheme. The proposed control method produces very competitive results, outperforming its rival in terms of trajectory tracking performance.

Exposure of blue crab (Callinectes sapidus) to modified clay treatment of Karenia brevis as a bloom control strategy

Harmful algal blooms (HABs) of the toxic marine dinoflagellate Karenia brevis, commonly called red tides, are an ongoing threat to human health and marine ecosystems in Florida. Clay flocculation is a standard control strategy for marine HABs in China and Korea and is currently being assessed for use in the United States. We evaluated the effects of a PAC-modified clay called Modified Clay II on mortality, eyestalk reflexes, and righting reflexes of 48 adult blue crabs (Callinectes sapidus). Crabs were exposed to clay alone (0.5 g L − 1), untreated K. brevis (1 × 106 cells L − 1), or a combination of K. brevis and clay for eight days. Clay treatment reduced cell concentrations in the water column by 95% after 24 h. We detected no significant differences in mortality, righting reflexes, or eyestalk reflexes between treatments. Our results indicate that the clay alone is not harmful to adult crabs at typical treatment concentrations within the measured time frame, and that treatment of K. brevis with this clay appears to have a negligible impact on crab mortality and the reflex variables we measured. These results suggest that Modified Clay II may be a viable option to treat K. brevis blooms without impacting adult blue crab populations. Additional controlled experiments and field tests are needed to further evaluate the impact of clay on natural benthic communities.

Distributed economic predictive control of integrated energy systems for enhanced synergy and grid response: A decomposition and cooperation strategy

The close integration of increasing operating units into an integrated energy system (IES) results in complex interconnections between these units. The strong dynamic interactions create barriers to designing a successful distributed coordinated controller to achieve synergy between all the units and unlock the potential for grid response. To address these challenges, we introduce a directed graph representation of IESs using an augmented Jacobian matrix to depict their underlying dynamics topology. By utilizing this representation, a generic subsystem decomposition method is proposed to partition the entire IES vertically based on the dynamic time scale and horizontally based on the closeness of interconnections between the operating units. Exploiting the decomposed subsystems, we develop a cooperative distributed economic model predictive control (DEMPC) with multiple global objectives that regulate the generated power at the grid’s requests and satisfy the customers cooling and system economic requirements. In the DEMPC, multiple local decision-making agents cooperate sequentially and iteratively to leverage the potential across all the units for system-wide dynamic synergy. Furthermore, we discuss how subsystem decomposition impacts the design of distributed cooperation schemes for IESs and provide a control-oriented basic guideline on the optimal decomposition of complex energy systems. Extensive simulations demonstrate that the control strategies with different levels of decomposition and collaboration will lead to marked differences in the overall performance of the IES. The standard control scheme based on the proposed subsystem configuration outperforms the empirical decomposition-based control benchmark by about 20%. The DEMPC architecture further improves the overall performance of the IES by about 55% compared to the benchmark.

Application of deep learning techniques to minimize the cost of operation of a hybrid solar-biomass system in a multi-family building

Concerns related to climate change put renewable energy at the centre of most of the policies aimed at achieving a deep decarbonisation of the building sector. The combined use of two or more renewable energy sources in the same energy system can lead to an increase in the total share of renewable energy and in the flexibility of the system. In this direction, the SolBio-Rev project aims to develop an innovative system that uses solar thermal collectors and a biomass boiler to meet energy demand in buildings in different climatic regions. An advanced control that used deep reinforcement learning techniques was considered in this paper to find an optimal control strategy for a specific SolBio-Rev system installed in a standard multi-family residential building located in Madrid. The advanced control was developed to minimize the total cost of operation of the system. The results indicated that the advanced control strategy achieved a cost reduction of 35% in winter, compared to a standard rule-based control strategy. However, the improved control was not able to produce a significant cost reduction in summer.

Sensitivity of Lillgrund Wind Farm Power Performance to Turbine Controller

The impact of the choice of the individual wind turbine controller on the power production of a wind farm is studied by means of large eddy simulations (LES) combined with aero-servo-elastic actuator disc representations of the rotors. The investigation includes both two typical controllers for pitch-regulated variable speed wind turbines and a simplified control strategy based on tabulated rotor speed and pitch as a function of the average velocity at the rotor. The simulations show that the power output of the individual wind turbines is strongly affected by choosing either a standard or simplified control strategy when they operate in the deep wake of upstream turbines. On the other hand, the sensitivity to control strategy is less significant for wind directions where the effective stream-wise distance between the turbines is larger.

Extending ramp metering control to mixed autonomy traffic flow with varying degrees of automation

The emergence of automated vehicles may have significant impacts on traffic flow. While many studies suggest that fully automated vehicles can improve traffic flow by changing their macroscopic characteristics at different market penetration rates, recent studies show that partially automated vehicles (e.g., adaptive cruise control vehicles) commercially available on the market may negatively impact the traffic flow. With this in mind, existing traffic control strategies, such as ramp metering, may require further modification due to the change of macroscopic traffic flow characteristics in the presence of mixed autonomy traffic. However, the extent to which the ramp metering control algorithm will have to change in response to mixed autonomy traffic is still an open question. In this study, we first formulate the fundamental diagram analytically for mixed autonomy traffic, which is dependent on the market penetration rates of automated vehicles. Next, we model and simulate the composite traffic flow with a mixed autonomy macroscopic traffic flow model, and modify a standard ramp metering control strategy to optimize operations under the new flow conditions.The simulation results show that the total time spent is reduced by 4% compared to the scenario controlled with a standard ramp metering control strategy, suggesting a higher highway throughput is obtained at a merge section when leveraging the modified ramp metering controller. The results also show that the system-wide total delay is reduced by 11% compared to the scenario controlled with a standard ramp metering control strategy, implying the proposed ramp metering control strategy is efficient.

Synthesis of Voltage PID Controller to Improve INC- MPPT Algorithm for Cascade Regulation of KC200GT Panel-Based Solar System

This paper proposes the Maximum Power Point Tracking MPPT method based on the Incremental Conductance INC algorithm. The primary goal is to prevent the ripple problem that is occurred in the output responses of the solar panel system. This last includes the KC200GT panel system, while using it to supply a resistive load throughout the DC-DC boost converter. This goal is reached through respecting the two following steps. First, the INC algorithm modified so that it produces an optimal reference voltage rather than a duty cycle control, which is often utilized in the standard INC-MPPT control strategy for controlling the switch button of the DC-DC boost converter. Second, the solar system behavior is modeled by the linear small-signal model for the design of the voltage PID controller. At each sample, a voltage discrepancy is generated as a result of comparing the previous reference voltage to the one delivered by the KC200GT panel where the introduction of the voltage PID controller becoming indispensable. These two steps create the development of a novel strategy that is afterwards known as improved INC-MPPT. The performance assessments of the proposed strategy are carried out by simulation using MATLAB®/SIMULINK software, and the obtained results reveal the crucial importance of including the voltage PID controller to overcome the ripple issue, occurred when applying the standard INC-MPPT control strategy.

Smooth commutation of brushless DC motor by model‐free predictive current control without back‐EMF estimation

AbstractThis study presents a new predictive control approach for brushless DC (BLDC) motor to smooth its commutation process. The proposed predictive control method is applied to the BLDC motor's equivalent DC current. Therefore, this method utilises only one prediction equation, which means less calculation than the standard predictive control schemes. Here, the equivalent DC current equation is defined according to the model‐free control theory to remove the back‐EMF voltage estimation from the control system. In consequence, the proposed predictive DC current control (DC‐PCC) has a smooth commutation. Because of applying the model‐free theory, both commutation and non‐commutation periods of the BLDC motor have similar dynamic equations. Hence, one simple prediction equation can be utilised for both periods. Besides, the proposed method has less computational burden due to using only one prediction equation and removing the back‐EMF estimation process. To validate the proposed model‐free DC‐PCC, experimental tests are performed, and the results are discussed in this research.

Body image dissatisfaction, nutritional status and weight control strategies among university undergraduates in Lagos: a descriptive cross-sectional study.

body image dissatisfaction has been associated with poor nutritional status and unhealthy weight management strategies. This study determined the prevalence and relationships between body image dissatisfaction, nutritional status, and weight management strategies among university undergraduate students in Lagos, Nigeria. a descriptive cross-sectional study employed a multi-stage sampling technique to select 865 undergraduates in Lagos. A pretested self-administered questionnaire was used to assess the variables. Stunkard figure rating scale was used to determine body image dissatisfaction. Body mass index (BMI) was calculated to determine nutritional status. A standard weight control strategy scale was adopted to determine weight management strategies. SPSS (version 23) was used for analysis and the association between variables was determined using Chi-square. The level of significance was set at P= <0.05. the prevalence of body image dissatisfaction was high (63.5%) but not associated with gender. The majority (65.1%) had normal BMI, 10.6% were overweight and 7.2% were obese. Majority of the respondents (93.3%) engaged in weight management practices with dietary control being the most employed strategy. The most commonly employed unhealthy practice is strict dieting (37.7%). Body image dissatisfaction was significantly associated with overweight/obesity (P=0.001) but not with weight management practices. Age and overweight/obesity were predictors of BID. prevalence of body image dissatisfaction, overweight and obesity, and unhealthy weight management strategies were high. Body image dissatisfaction was associated with obesity but not associated with weight management strategies. All undergraduates need health education on body image and appropriate weight management strategies.

Discrete spatiotemporal network synchronization based on adaptive control

This paper first introduces the basic concept of network synchronization and several common synchronization types. Secondly, aiming at the synchronization control problem of a class of discrete spatiotemporal networks, a standard synchronization control strategy and a synchronization controller are proposed based on Lyapunov stability theory. In order to further verify the effectiveness of the synchronization theory, the spatiotemporal network model is selected, the coupling matrix and the identification rate of unknown parameters are designed for numerical simulation. Finally, the simulation results show the feasibility of the theory.

A new fast nonlinear model predictive control of parallel manipulators: Design and experiments

High-speed parallel manipulators are characterized by fast sampling rates and challenges owing to the presence of constraints, high nonlinearities, uncertainties, and fast dynamics. The development of a reliable nonlinear model predictive control (NMPC) approach for this type of robotic systems is challenging because of the complex dynamics that involves high computational burden. To address this control problem, this paper proposes a new fast NMPC strategy applied to high-speed parallel robots. Based on (i) a parameterization technique, (ii) a fast gradient solver, (iii) a proportional integral derivative (PID) control term, (iv) a nonlinear dynamic model, and (v) an artificial neural network (ANN) model, two fast NMPC frameworks were developed to reduce the computational cost of the classical NMPC and address online implementation. In this study, we focus on improving the performance of the standard predictive control strategy outside of the range of its classical applicability by developing a fast NMPC controller for high-speed parallel kinematic manipulators (PKMs). Numerical simulations and real-time experimental results were presented and discussed to validate the relevance of the proposed controllers. Experiments were conducted on a four-degree-of-freedom (4-DOF) PKM called VELOCE developed in our laboratory. The results show that the proposed solution outperforms the original scheme in terms of real-time tracking performance.

Distributed Neural Network Observer for Submodule Capacitor Voltage Estimation in Modular Multilevel Converters

Modular multilevel converters (MMCs) have become one of the most popular power converters for medium/high-power transmission systems and motor drive applications. Standard control schemes for MMCs use a voltage measurement per submodule (SM) to balance the capacitor voltages and govern the MMC. Consequently, the control system requires a significant amount of sensors and the effective communication of sensitive data under relevant electromagnetic interference (EMI), impacting the reliability and cost of the MMC. This work presents a distributed neural network (DNN) observer inspired by a general predictor-corrector structure for estimating the capacitor voltages at each SM. The proposed observer predicts each SM capacitor voltage using a standard average model. Then, each prediction is corrected and denoised by a neural network of reduced computational complexity. As a result, the proposed observer reduces the number of required voltage sensors per arm to only one and filters the high-frequency noise without noticeable delay in the estimated SM capacitor voltages for both transient and steady-state operations. Experiments conducted in a three-phase MMC with 24 SMs confirm the effectiveness of the proposed DNN observer.

Command filtered adaptive fuzzy control of fractional-order nonlinear systems

The standard backstepping control scheme has inherently computational complexity explosion problem, and consequently, becomes prohibitive as the order increases. This paper contributes to command filtered adaptive fuzzy control (AFC) of fractional-order nonlinear systems (FNSs) by using fractional backstepping control method. To approximate the virtual input, a fractional-order command filter (FCF) is proposed. Filtering approximation errors that affect the performance of the controller during the filtering process are solved by an error compensation mechanism (ECM). The simulation results verify the validity of the theoretical results.