Control Performance Research Articles

Event-triggered adaptive neural prescribed performance admittance control for constrained robotic systems without velocity measurements

In this paper, an event-triggered adaptive neural prescribed performance admittance control (ETANPPAC) scheme is proposed to control the constrained robotic systems without velocity sensors. To ensure compliance during human–robot interaction, the reference trajectory is obtained by reshaping the desired trajectory for the robotic systems based on the admittance relationship, where a saturation function is used to constrain the reference trajectory, avoiding excessive contact forces that could render the trajectory inexecutable. Moreover, a barrier Lyapunov function is used to constrain the tracking errors for prescribed performance, where a velocity observer and a radial basis function neural network are designed to estimate the velocity and the uncertainty of the robotic systems, respectively, to enhance control performance. To reduce the communication burden, an event-triggered mechanism is introduced and the Zeno behavior is avoided with the event-triggered condition. The stability of the whole control scheme is analyzed by the Lyapunov function. Simulation and experimental tests demonstrate that the proposed ETANPPAC scheme can track the desired trajectory well under constraints and reduce the communication burden, thereby achieving better efficiency for controlling the robotic systems compared with similar control schemes.

Design and research of fuzzy PID-based liquid pump performance testing platform

Abstract The pump is a crucial component of agricultural sprayer machinery, directly impacting its operational quality. This paper designs a performance testing platform for pumps. Utilizing a brushless DC motor as the power source for the pump, a closed-loop mathematical model for the brushless DC motor is established. Simulation experiments and indoor experimental tests are conducted. Simulation results indicate that the motor control system exhibits fast response, no overshooting, and excellent dynamic and static performance. Experimental tests on the platform show that the error range of motor speed is less than 0.3%, achieving excellent control performance. The constructed pump performance testing system achieves precise measurement of pump flow rates at different speeds, demonstrating outstanding performance.

Efficacy of powered air-purifying respirators (PAPRs) for source control of simulated respiratory aerosols

Loose-fitting powered air purifying respirators (PAPRs) are a popular alternative to the use of filtering facepiece respirators for healthcare workers. Although PAPRs protect the wearer from aerosol particles, their ability to block infectious aerosol particles exhaled by the wearer from being released into the environment (called source control) is unclear. The source control performance of four PAPRs with loose-fitting facepieces were tested using a manikin that exhales aerosol particles. The PAPRs were tested by themselves and in combination with a face-worn product intended to provide source control (either a surgical mask or an N95® filtering facepiece respirator.) RESULTS: Two PAPR facepieces with filtration panels significantly reduced the release of exhaled aerosols into the environment, while three facepieces without such panels did not. Wearing a surgical mask or respirator under the facepiece significantly improved the source control performance. Most PAPR facepieces do not block aerosols exhaled by the wearer. Facepieces designed to filter exhaled particles can prevent aerosols from being released into the environment. Wearing a surgical mask or a filtering facepiece respirator under the facepiece can also provide source control, but PAPRs are not typically certified for use with masks and respirators.

Enhancing voltage stability through wavelet-fuzzy control of hydrogen flow in OC-PEM fuel cell

Open cathode proton exchange membrane fuel cells (OC-PEM fuel cells) serve as electricity generators, utilizing hydrogen as an input source. While effective for fixed loads like residential applications, challenges arise in dealing with output voltage fluctuations caused by rapid load changes. These fluctuations not only impact fuel cell performance but also introduce instability in the supplied power. To solve this issue, the study proposes an innovative hydrogen flow control system employing a feedforward wavelet- fuzzy method. The primary goal of this control system is to enhance fuzzy control performance using wavelets, mitigating signal fluctuations and achieving optimal stability in fuel cell output voltage under constant load conditions. Wavelet functions act as filters on the fuzzy control input, minimizing fluctuations and refining the entire process. Additionally, a feedforward system is incorporated to maintain hydrogen flow at the set point value. The proposed control system is implemented on a validated model using experimental data. Performance analysis reveals that the proposed method effectively stabilizes voltage by accelerating the recovery time from disturbances.

Investigations of BLDC motor speed characteristics via THD under conventional and advanced hybrid controllers

This project investigates brushless direct current (BLDC) motor speed control through total harmonic distortion (THD) analysis, employing proportional integral (PI), fuzzy logic (FLC), adaptive neuro-fuzzy inference system (ANFIS), and an innovative hybrid ANFIS-PD/PI controller. Considering the vital role of BLDC motors in precision-dependent industries like robotics, electric vehicles, and industrial automation, our primary focus is on understanding BLDC motor operation and recognizing THD's significance as a performance metric. Controllers are meticulously implemented in real-time, fine-tuned, and optimized to achieve desired speed characteristics, incorporating considerations like response time, accuracy, and energy efficiency. The project's core involves THD analysis, quantifying harmonic content in the BLDC motor's speed waveform. This facilitates a comprehensive comparative evaluation of controller performance, assessing their capability to maintain speed stability and influence power quality. The discussion covers the merits and limitations of each controller, with a special emphasis on the hybrid ANFIS-PD/PI controller, seamlessly blending ANFIS adaptability with PD/PI control stability. Results illustrate the hybrid controller's excellence in optimizing BLDC motor speed control, demonstrating superior performance in speed accuracy, disturbance rejection, and THD reduction. These findings drive advancements in motor control technology, providing practical guidance for selecting controllers tailored to specific application requirements. Simulation results can be analyzed using MATLAB/Simulink 2018a Software.

Enhancing PI controller performance in grid-connected hybrid power systems

The optimal operation of a microgrid was buildup of both uncontrollable (solar, wind) and controllable (batteries, diesel generators) electrical energy sources are enclosed in this paper. By replacing controllers, the variations in wavering power supply caused by load fluctuations are managed. The objective of the research paper is to optimize these controller gain settings for effective use of electrical energy. In this paper integral time square error principle is combined along with the Cuckoo search algorithm (CSA) and particle swarm algorithm (PSA) to obtain the accurate, precise and appropriate results. It enhances the microgrid's steady-state sensitive responsiveness in comparison to trial-and-error techniques, assuring a stable supply of electricity to the load.

Exploring the impact of temporally contiguous action effect on action control performance in typical development and attention-deficit/hyperactivity disorder.

Previous work shows a reinforcing impact of action effect on behavior, independent of other reinforces such as positive outcomes or task success. Action-effect temporal contiguity plays an important role in such a reinforcing effect, possibly indicating a motor-based evaluation of their causal relationship. In the present study, we aimed to negate the reinforcing impact of an immediate action effect with task success by designing a task where red and green circle stimuli rapidly descended on the screen. Participants were instructed to respond only when a specific sequence of colored stimuli matched a predefined response rule. The temporal contiguity between the response and a perceptual effect was manipulated. We initially hypothesized an increased action tendency resulting in higher false alarm rates in the immediate (compared to 400 ms lag) action-effect condition. We also expected this pattern to be more pronounced in attention-deficit/hyperactivity disorder compared to typically developing individuals. Contrary to our expectations, results from three experiments showed a consistent pattern of a lower false alarm rate in the immediate compared to the 400 ms lag effect condition across both attention-deficit/hyperactivity disorder and typically developing groups. Additionally, while action-effect temporal contiguity did not significantly alter the overall rate of misses, we observed earlier improvements in both misses and false alarms in the immediate condition during the first blocks. Possible explanations for the complex impact of action effect on action tendency and action control are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Active control vibrations of aircraft wings under dynamic loading: Introducing PSO-GWO algorithm to predict dynamical information

This study presents an innovative approach to mitigate vibrations induced by external shock on composite structures through the application of an intelligent controller. Leveraging the first-order shear deformation panel theory, a sophisticated controller scheme is developed, integrating methodologies such as the differential quadrature approach and Laplace transform. Furthermore, deep neural network (DNN) and support vector regression (SVR) techniques are employed to enhance prediction accuracy and control efficiency. Additionally, two optimized hybrid models are proposed, incorporating Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) algorithms, to further refine the controller's performance. The proposed methodology aims to address the challenges associated with vibrations in composite structures by providing a comprehensive and adaptive control solution. By utilizing advanced optimization algorithms and machine learning techniques, the controller can effectively adapt to dynamic changes in external shock conditions, thereby minimizing vibrations and ensuring structural integrity. The integration of ANN and SVR enhances the controller's predictive capabilities, enabling it to anticipate and respond to varying shock scenarios with precision. Through theoretical analysis and numerical simulations, the effectiveness of the proposed intelligent controller is demonstrated in reducing vibrations and enhancing the structural stability of composite systems. The optimized hybrid models, employing PSO and GWO algorithms, further improve the controller's performance by fine-tuning its parameters for optimal control efficiency. Overall, this research contributes to the development of robust control strategies for mitigating vibrations in composite structures subjected to external shock, with potential applications in aerospace, automotive, and civil engineering industries.

Zone Model Predictive Control with Ellipsoid Softing Target in Chemical Processes

A zone control algorithm is proposed that considers both economic performance indicators and control performance indicators. Unlike classic set point control, zone control expands the control target into a convex set. In this study, an ellipsoid is used as the control target, and the advantages of the ellipsoid target are explained in terms of overall stability and computational load. After defining the distance measurement function and appropriate terminal constraints, an objective function that considers both control performance and optimization performance is constructed. A theoretical analysis shows that the proposed control algorithm satisfies the Lyapunov stability criterion. The superiority of the ellipsoid control target in handling complex multivariable control tasks is also demonstrated. This method has significant potential value in practical industrial applications, helping to unleash the potential control performance and economic benefits of zone control systems. Finally, the feasibility and stability of the algorithm are verified through a typical chemical process simulation.

Active Vibration Control of Landing Gear System

The vibration of the aircraft when taxing, landing, and taking off is a great problem. Vibration induced due to the dynamic interaction of aircraft with the ground causes serious problems. Therefore, the vibration should be reduced or completely absorbed if possible by some external means. So, in this study active vibration control of an aircraft LG is investigated. The equation of motion of the aircraft LG system is obtained by Newton’s second law and simulated in MATLAB/Simulink environment. Two different controllers are fitted to the landing gear system. Proportional, integral derivative (PID) and Linear Quadratic Regulator (LQR) controllers are used in controlling active suspension structure. In the study, vertical displacement and acceleration of the fuselage are set as objective since it has harmful effects on the performance of the pilot’s capability. Simulation is run under B grade road and bump road for passive and active systems simultaneously. The performance of controllers is compared to one another and also with that of a passive system. From the results, it was obtained that under bump road excitation, LQR and PID controllers make 57.32% and 53.34% improvement in fuselage displacement, respectively. Similarly, under B Grade road, compared to passive systems LQR and PID controllers improve the vertical acceleration of the fuselage by 92.86% and 53.55% respectively.

Economic model predictive control for building HVAC system: A comparative analysis of model-based and data-driven approaches using the BOPTEST Framework

Model Predictive Control (MPC) has demonstrated its capability to significantly enhance energy efficiency while ensuring indoor comfort. This study focuses on the development and fair comparison of two Economic Model Predictive Control(EMPC) frameworks with the goal of optimizing demand response for utility cost reduction and ensuring thermal comfort in residential buildings. The first framework, known as RC-MPC, utilizes a reduced-order thermal Resistance–Capacitance network model to represent the thermal dynamics of the building. It formulates the optimization problem as a Linear Programming (LP) task, which is solved using CPLEX. The second framework, known as ANN-MPC, employs an Artificial Neural Network model capture the thermal behavior of the building, It addresses a non-convex optimization problem through projected stochastic gradient descent. To evaluate the performance of these frameworks, experiments are conducted using a single-zone dwelling within the BOPTEST framework. The results demonstrate that both approaches effectively reduce operational costs and enhance comfort, achieving comparable performance and outperforming the baseline Rule-Based Control (RBC) method. The discomfort reductions range from 30% to 95%, and energy expense savings range from 17% to 34%. Furthermore, the study examines the impact of various MPC hyperparameters. The findings highlight that a proper control horizon and formulating the objective function accurately are crucial for achieving optimal control performance. Additionally, tightening the comfort range is found to be important in managing the comfort violations, and the choice of solver can influence both the control performance and computational efficiency.

Improved Active Disturbance Rejection Control for Permanent Magnet Synchronous Motor

To improve the control performance of a permanent magnet synchronous motor (PMSM) under external disturbances, an improved active disturbance rejection control (IADRC) algorithm is proposed. Since the nonlinear function in the conventional ADRC algorithm is not smooth enough at the breakpoints, which directly affects the control performance, an innovative nonlinear function is proposed to effectively improve the convergence and stability. On this basis, the proposed IADRC is constructed, and comparative simulation results with ADRC and other IADRC show that faster response speed, higher accuracy and stronger robustness are obtained.

Performance of Robust Type-2 Fuzzy Sliding Mode Control Compared to Various Conventional Controls of Doubly-Fed Induction Generator for Wind Power Conversion Systems

This paper presents a novel hybrid type-2 fuzzy sliding mode control approach for regulating active and reactive power exchanged with the utility grid by a doubly-fed induction generator in a wind energy conversion system. The main objective of this hybridization is to eliminate the steady-state chattering phenomenon inherent in sliding mode control while improving the transient delays caused by type-2 fuzzy controllers. In addition, the proposed control approach has proven to be successful in coping with varying generator parameters and exhibited good reference tracking. An in-depth comparative study with state-of-the-art advanced control techniques is also the focus of the present paper. The comparative study has three objectives, namely: a qualitative comparative study that aims to compare response times and reference tracking capabilities; a quantitative evaluation that takes into account time-integrated performance criteria; and finally, robustness capabilities. The simulation results, carried out in the Matlab/Simulink environment, have demonstrated the effectiveness and best performance of the proposed hybrid type-2 fuzzy sliding mode control with respect to other advanced techniques included in the comparison study.

Modified Three-Loop Autopilot for Acceleration Control Performance Improvement of a Canard-Controlled Aircraft

Modified Three-Loop Autopilot for Acceleration Control Performance Improvement of a Canard-Controlled Aircraft

Novel scheme for uncertainty and disturbance estimation to control of spacecraft system with input constraint

In this paper, a novel scheme is presented to estimate the uncertainty and disturbance of the spacecraft system by a compensatory vector calculated online from the information of measured angular velocities. The compensatory vector is used in the constrained attitude control law to compensate for the perturbations of the spacecraft system. The attitude controller considering the input limitations of reaction wheels is developed by solving an optimization problem through the Karush-Kuhn-Tucker (KKT) theorem. By using noisy angular velocities in the observer design, the stochastic stability of the closed loop system under the constrained multivariable controller is demonstrated. In the results, at first, the open-loop performance of the uncertainty and disturbance observer is evaluated through computer simulations. Subsequently, the performance of the proposed controller in both constrained and unconstrained versions is evaluated. Finally, the proposed controller performance in compensating for uncertainties and disturbances is compared with the adaptive backstepping controller reported in the literature. The comparative results of two controllers show the superior performance for the proposed constrained controller in the presence of disturbances, uncertainties and input constraints. Furthermore, the proposed control system could attenuate the effect of measurement noises of angular velocities by weighting the compensatory vector in the observer design.

Computation of the exact time delay margin for vehicle platoon under generic communication topologies

Due to the limited bandwidth and transmission congestion of the vehicle platoon's communication, it is inevitable to induce time delay, which significantly degrades the control performance of the vehicle platoon, even resulting in instability. This paper focuses on analyzing the internal stability under generic communication topologies and presents a method of computing the exact time delay margin (ETDM). The proposed method can offer a necessary and sufficient internal stability condition with no conservatism. Firstly, to reduce the analytical complexity and computational burden elegantly, we decompose the closed-loop platoon dynamics into a set of individual subsystems via similarity transformation and matrix factorization. This decomposition approach is applicable for any general communication topology. Secondly, an explicit formula is deduced to compute the ETDM by surveying the characteristic roots' distribution of all these individual subsystems. It is further demonstrated that only the positive purely imaginary roots need to be considered to compute the ETDM. Finally, simulations are conducted to demonstrate the effectiveness of the theoretical claims.

Performance analysis and degradation mechanism of acrylamide co-polymer as rheology and filtrate reducers in different salt aqueous-based drilling mud systems at high-temperature conditions

To maintain performance of an aqueous-based drilling muds (ABDMs), it was imperative to understand the decay mechanism of the incorporated synthetic polymers, when exposed to the elevated temperatures. The understanding of the decay mechanism could provide a polymer replenishment strategy for the fluid to retain a specific rheology and filtrate control performance. In this context, thermal-degradation of various acrylamide co-polymers was investigated in different monovalent brines. The acrylamide co-polymers were custom synthesized, and their molecular weight and % sulfonic substitution was verified using the capillary viscometer and nuclear magnetic resonance spectroscopy techniques respectively. The co-polymer thermal degradation mechanism (i.e., polymer hydrolysis) in various monovalent brines (KCl, NaCl and NaBr) was quantified by novel titration for temperature range {121 °C, 177 °C}. The degradation response of the co-polymers was then correlated with their rheology and HPHT (high-pressure-high-temperature) filtrate performance; for instance, the titration studies showed that co-polymer degradation was 12–15% and 44–47% after sixteen hours aging at 121 °C and 177 °C respectively; correspondingly the co-polymer performance in ABDM, exhibited HPHT filtrate of 12–18 mL and 38–40 mL at those respective temperatures after sixteen hours of aging. The quantified understanding of the co-polymer thermal degradation was used to device a new approach for co-polymer replenishment strategy; it was illustrated that a 7% replenishment of the co-polymer for every eight hours, at 121 °C, enabled sustained HPHT filtrate of 12–18 mL for the studied evaluation period of thirty-two hours. The replenishment approach presented in the study would provide a valuable tool for drilling automation to ensure sustained fluid performance.

Inverse compensation mechanism-based adaptive fuzzy-neural fault-tolerant control for an uncertain quadrotor UAV

Actuator faults, system uncertainties, and external disturbances can affect tracking control performance of the quadrotor UAV, and even cause the instability of the system. To address this issue, an adaptive fuzzy-neural fault-tolerant control (AFNFTC) scheme is proposed for the quadrotor UAV integrated with inverse compensation strategy and fuzzy neural networks (FNNs) technology. Firstly, we design an inverse compensation mechanism to address actuator faults and utilize the approximation capabilities of the FNNs to compensate for system uncertainties. Then, based on the Lyapunov direct method, the AFNFTC scheme is designed to ensure the stability of the quadrotor UAV. Under the proposed scheme, the closed-loop system for the quadrotor UAV is proven to be uniformly bounded, and the tracking error of the quadrotor UAV is demonstrated to converge to a small compact set ultimately by appropriately selecting design parameters. Finally, the feasibility and effectiveness of the proposed scheme is validated through simulation results.

Enhancement of DDST-MFAC for tracking performance by using dynamic data reconciliation

Abstract Model-free adaptive control (MFAC) stands out as an effective data-driven method for addressing nonlinear problems in industrial processes. To maintain good control performance, a data-driven set-point tuning (DDST) method is used to update the virtual set-point of the MFAC system. The DDST-based MFAC (i.e. DDST-MFAC) constantly approaches the target of the process through the nonlinear set-point tuning method. However, due to equipment errors and external interference, industrial sensors often suffer from measurement noise, which can have adverse effects on the control performance. In this article, an available dynamic data reconciliation (DDR) technique is adopted to improve the tracking performance of the DDST-MFAC, which suppresses the impact of process noise by using predicted information and measured data to achieve high-precision requirements for controlling nonlinear processes. Finally, considering both Gaussian and non-Gaussian distribution of measurement noise, the effectiveness of the proposed method was verified through the simulation of a nonlinear nonaffine plant. It is also applied to the steam-water heat exchange process, the control result is improved ultimately.

Secure consensus for positive multi‐agent systems with bumpless transfer control under Denial‐of‐Service attacks

AbstractThis article investigates the secure consensus control problem of positive multi‐agent systems under multiple‐mode Denial‐of‐Service (DoS) attacks. A bumpless transfer (BT) control based secure mode strategy is proposed to improve the switching transient performance due to the system being actively switched to secure mode when attack is detected. Considering possible time delays for agents dealing with the issues of DoS attacks, an asynchronous switching model is established between system models and communication topology. With the stability theory of positive systems, sufficient and necessary conditions are established to guarantee the positivity of multi‐agent systems under the secure mode strategy. Because the switching law caused by external attacks is unknown in advance, the BT control is specifically implemented through the controller gain interpolation technique. By multiple Lyapunov functions (MLFs) approach, secure consensus is achieved with a trade‐off among controller performance, attack parameters, and switching delays. Numerical examples are presented to demonstrate the effectiveness of the obtained results.