Performance Of Control System Research Articles

The effect of head-tracking resolution on the stability and performance of a local active noise control headrest system.

Incorporating head-tracking techniques into local active noise control headrest systems enables the plant model used in the controller to be updated dynamically as the user moves their head. This reduces the mismatch between the plant model and the physical plant responses from the secondary sources to the users' ears, which increases the achievable noise reduction when head movement occurs. In practice, since the plant models for different head positions must be identified during a calibration procedure, it is necessary to limit the head-tracking resolution to constrain the complexity of this procedure. This leads to errors between the physical and modelled plant responses as the user's head moves, which impacts the control system's stability and performance. However, the relationship between the control system behaviour and the tracking accuracy is not well understood. This paper investigates the impact of head-tracking resolution, considering translational and rotational movements, on the stability and performance of an active headrest. Assuming the error signals at the user's ears are available for adaptive control, it is shown that the system has an upper-frequency limit beyond which controller instability occurs, and this frequency is influenced by the tracking resolution, the initial head position, and the type of head movement.

Tailless control of a four-winged flapping-wing micro air vehicle with wing twist modulation

This paper describes the tailless control system design of a flapping-wing micro air vehicle in a four-winged configuration, which can provide high control authority to be stable and agile in flight conditions from hovering to maneuvering flights. The tailless control system consists of variable flapping frequency and wing twist modulation. The variable flapping frequency creates rolling moments through differential vertical force from flapping mechanisms that can be independently driven on the left and right sides. The wing twist modulation changes wing tension, resulting in vertical and horizontal force variations during one flap cycle and generating pitching and yaw moments. We presume that the wing geometry and implementation method of wing-root actuation are related to the control authority of wing twist modulation. Then, the control system's performance is analyzed for various wing geometries and implementation methods, including wing length, leading-edge thickness, camber angle, and vein configuration. Furthermore, the cross-coupling effect is examined for the wing twist modulation, and a control surface interconnect is designed to compensate for the decrease of pitch control authority and adverse rolling moment. The refined wing and control mechanism demonstrated its high control authority without significant loss of vertical force and power efficiency. The flight experiments validated that the control system based on wing twist modulation is suitable for four-winged flapping-wing micro air vehicles, providing sufficient control moment and minimizing the cross-coupling effect.

Collaborative Control and Intelligent Optimization of a Lead–Bismuth Cooled Reactor Based on a Modified PSO Method

Accelerator-driven subcritical (ADS) reactors with lead–bismuth eutectic (LBE) coolants are some of the Gen-IV nuclear energy systems that can generate clean electricity and potentially transmute spent fuel. The dynamic characteristics and control strategy of an ADS reactor are substantially different from those of traditional nuclear reactors. In this paper, a new collaborative control strategy is proposed using an accelerator beam and a control rod, and the control system’s parameters are optimized using a modified particle swarm optimization (PSO) method. To test the control performance, a simulation platform is developed with a nonlinear reactor dynamic model, a power compensation control system and a coolant temperature control system. Four typical control transients are used, including a ±10% full-power (FP) step change load and a ±5% FP/min linear variable load. The simulation results show that the collaborative control strategy has a better load tracking capability and a higher power control accuracy than the beam single-control strategy and the rod single-control strategy. The results also show that the performance of the collaborative control system in terms of the reactor’s power and coolant temperature is significantly improved based on the modified PSO parameter optimization.

Corporate Governance, Management Control System and Financial Performance of State-Owned Enterprises in Tanzania

Corporate Governance, Management Control System and Financial Performance of State-Owned Enterprises in Tanzania

Sustainability management control system and sustainability performances in Indonesia

This study aims to determine the effect of the Sustainability Management Control System (SMCS) on improving Sustainable Performance (SP) or corporate sustainability performance. The study utilizes the data from 412 non-financial companies listed on the Indonesia Stock Exchange, covering the period from 2017-2021. Hypothesis testing was carried out with a least squares regression model strengthened by robustness tests. To support the results, a number of other studies were carried out, including panel data, sensitivity testing, and the coarsened exact matching approach. The results showed a positive correlation between SMCS and improving sustainability performance. This means that the better the implementation of SMCS in a company, the more its sustainability performance will improve. This indicates the important role of SMCS in supporting the improvement of corporate sustainability performance. The practical implication of this research is to provide insight for executives, policymakers, and stakeholders to encourage the implementation of SMCS to achieve the success of sustainable development. In terms of theoretical contribution, this research elucidates the relationship between SMCS and sustainability performance, highlighting that SMCS is a development of the Management Control Systems (MCS) concept, incorporating new metrics. Overall, this research is useful for understanding the effectiveness of SMCS in improving corporate sustainability performance.

New order-dependent conditions to control a class of nonlinear real-order systems

In many application points of interest, controlling the responses of real-world applications that vary with time seems quite challenging and puzzling. A linear time-varying state feedback controller is widely known and can be useful to estimate bounds to the state control matrix in the design of many control systems. The issue of initialization of real-order control system enhancement remains a challenging issue in the systems analysis subject to random initial-time placed on a real number line. We address a new design of a class of real-order control systems that consists of separated linear and nonlinear terms affected by input functions to be controlled with an implemented time-varying linear state feedback controller. We utilize the fractional comparison method and under Lipschitz nonlinearity with a constant bounding matrix of time-varying coefficients of control systems to address new order-dependent conditions that provide local and global stabilization to controlled systems. Applications of results that include practical real-order single-machine-infinite-bus power systems have been illustrated to control the responses by the utility of theoretical conditions examined along with validation of numerical simulations. It is shown that the proposed controller is practically convenient and demonstrates the efficiency of measuring the performances of control systems.

Cascade flow rate-temperature control system design based on PID controller using direct synthesis tuning method

Cascade control is one of the multi-loop control schemes that aim to increase the performance of closed-loop control systems. Temperature control on the outlet of a plate heat exchanger often from suffers errors in the control variable and designated set point, so it is necessary to use cascade control in order to stabilize output temperature and reduce the disturbance. The Proportional Integral Derivative (PID) controller in conjunction with the direct synthesis tuning method is used due to ease of implementation and to modify the second-order process model and become the first-order process model, simplifying the model. In cascade control, the flow rate control is designated as the secondary loop, while the temperature control functions as the primary loop. The PID controller model is designed with direct synthesis tuning on the cascade flow rate temperature control, resulting a proportional gain of 2.15%, of 1.976 s, and τd of 0 seconds on the flow rate control loop. Whereas on the temperature control loop, the proportional gain is 13.23%, is 66.3 s and the τd is 7 seconds. The transient responses from cascade flow rate temperature control from Simulink are rise time (tr) = 106.7 seconds, settling time (ts) = 183 seconds, and maximum overshoot = 0%. Based on this parameter, the controller generates Process Variable (PV) responses from master control that can reach the Set Point (SP) without overshoot, maintain a steady state, and reduce the disturbance from slave control within 20 seconds of the response increasing from the steady state condition

Optimization of Interstellar Travel through Design Approaches with Gimballing Ion Thrusters

Interstellar travel, constrained by current propulsion technologies, suffers from limitations in efficiency and maneuverability essential for vast interstellar distances. This paper presents a rigorous investigation into optimizing interstellar travel through advanced design methodologies, with a primary focus on the deployment of gimbaled ion thrusters to enhance maneuverability and trajectory correction. The research meticulously examines two-axis thrust vectoring, propulsion system efficiencies, fuel transfer mechanisms, and energy management strategies, aiming to significantly improve propulsion and control system performance. A detailed comparative analysis of gimbaled thrust versus traditional thrust systems evaluates the impact on fuel efficiency and overall spacecraft performance. By integrating aerospace engineering and materials science principles, this study advances the discourse on interstellar travel. The findings and recommendations from this research aim to drive the development of cutting- edge spacecraft propulsion systems, facilitating more efficient and adaptable exploration beyond the solar system.

ANFIS robust control application and analysis for load frequency control with nonlinearity

This study investigates and demonstrates the adaptive neuro-fuzzy inference system (ANFIS) controller performance on a dynamic system with inherent nonlinearity. Here, dynamics of automatic load frequency control is considered under this case. The ANFIS controller is designed, trained, and optimized to regulate the frequency deviation of an isolated power delivery area. The frequency deviation data under a sample disturbance are taken with the desired control effort and are picked to train with five different membership functions. The tuning is carried out by the hybrid method. The ANFIS controller, developed, yields a better result, with less settling time of up to 20 s than the standard disturbance rejection proportional–integral–derivative (PID) controller takes. Designed ANFIS performs very robustly considering variations in inertia and damping. All the experimental setup is built under a MATLAB Simulink environment.

Real-Time Information Acquisition and Processing Method for Penetration Information Based on Multi-Information Fusion

To improve the real-time performance and the target adaptability of penetration fuze detonation control systems, and to enhance the system fusion processing capability for multi-sensor information, this paper uses a modular design concept to construct a miniaturized (ø38[Formula: see text]mm[Formula: see text]×[Formula: see text]4[Formula: see text]mm) fuze detonation control system that is capable of real-time processing of data from multiple information sources. The core component of this system is the GD32E230 microcontroller, which features a high dominant frequency and low power consumption. This device is integrated with a ferroelectric memory and signal processing circuits that match the sensors. To address the issue of unclear traditional acceleration signal penetration and the difficulties associated with the identification of these signals, the approach in this paper improves feature recognition accuracy through rapid acquisition and fusion of multiple types of sensor output signal, and self-adaptive identification of multilayered targets and single-layer thick targets is achieved. During the programming of the embedded system, the hardware register is operated directly, the instruction execution sequence is optimized, and the program execution efficiency is improved by using the function characteristic that some microcontroller unit peripherals do not occupy the central processing unit when working, thus allowing the intended purpose of improving the system’s real-time performance to be achieved. A semi-physical simulation method is then used to verify the performance of the penetration fuze detonation control system. The results obtained show that the system has 100%-layer counting accuracy for multilayered targets and a relative error of less than 1% for the calculated residual velocities of single-layer thick targets, thus validating the effectiveness of the system.

Complementary Filter‐Based Incremental Nonlinear Model Following Control Design for a Tilt‐Wing UAV

ABSTRACTThis article presents an incremental nonlinear model following control (INMFC) strategy for a tilt‐wing vertical take‐off and landing (VTOL) unmanned aerial vehicle (UAV). To ensure a good and robust regulation performance, a two‐loop feedback controller, based on the incremental backstepping (IBS) methodology, is used to handle uncertainties and external disturbances robustly. In order to ensure a good mode following and to aid the tracking performance of the flight control system, the first and second‐time derivatives of the desired model response, computed by a model reference (MR) system, are either directly used as feedforward signals for the outer loop or are additionally transformed for the inner‐loop by using an incremental nonlinear dynamic inversion (INDI) grounded approach to transform the signals based on known systems kinematics. In order to uniformly handle the overactuated flight vehicle in both mission model and during the transition, an operation mode‐based weighted incremental linear control allocation approach is applied for safe operation. The performance of the suggested control approach is investigated by utilizing a high‐fidelity nonlinear flight dynamics model of the tilt‐wing system. The results presented in this paper demonstrate that the proposed control approach provides significant benefits for the robust control of the considered tilt‐wing UAV.

Improving Performance of ADRC Control Systems Affected by Measurement Noise Using Kalman Filter-Tuned Extended State Observer

Improving Performance of ADRC Control Systems Affected by Measurement Noise Using Kalman Filter-Tuned Extended State Observer

A multivariable adaptive formation control for multiple UAVs with external uncertain disturbances

PurposeA multivariable model reference adaptive control method is proposed to solve a distributed leader–follower formation control problem of unmanned aerial vehicles (UAVs) with uncertain parameters and unknown external disturbances for both leader and followers.Design/methodology/approachA case of uncertain stochastic external disturbances for UAVs is considered, and based on the distributed communication network of UAVs, a state-feedback adaptive controller is proposed to maintain the formation of UAVs consistently. Then, the stability and asymptotic tracking performance of the UAV formation control system are analyzed by the Lyapunov function.FindingsThe simulation results demonstrate that this formation control scheme can effectively solve the stochastic external disturbance problem of UAVs and ensure the stability of their formation.Originality/valueThe proposed multivariable model reference adaptive control method reduces the error of formation control system and improves the stability and control performance of UAV compared with fixed control.

Momentum-Based Adaptive Laws for Identification and Control

In this paper, we develop momentum-based adaptive update laws for parameter identification and control to improve parameter estimation error convergence and control system performance for uncertain dynamical systems. Specifically, we introduce three novel continuous-time, momentum-based adaptive estimation and control algorithms and evaluate their effectiveness via several numerical examples. Our proposed adaptive architectures show faster parameter convergence rates as compared to the classical gradient descent and model reference adaptive control methods.

ADHDP-based robust self-learning 3D trajectory tracking control for underactuated UUVs.

In this work, we propose a robust self-learning control scheme based on action-dependent heuristic dynamic programming (ADHDP) to tackle the 3D trajectory tracking control problem of underactuated uncrewed underwater vehicles (UUVs) with uncertain dynamics and time-varying ocean disturbances. Initially, the radial basis function neural network is introduced to convert the compound uncertain element, comprising uncertain dynamics and time-varying ocean disturbances, into a linear parametric form with just one unknown parameter. Then, to improve the tracking performance of the UUVs trajectory tracking closed-loop control system, an actor-critic neural network structure based on ADHDP technology is introduced to adaptively adjust the weights of the action-critic network, optimizing the performance index function. Finally, an ADHDP-based robust self-learning control scheme is constructed, which makes the UUVs closed-loop system have good robustness and control performance. The theoretical analysis demonstrates that all signals in the UUVs trajectory tracking closed-loop control system are bounded. The simulation results for the UUVs validate the effectiveness of the proposed control scheme.

Enhanced Modeling and Control of Organic Rankine Cycle Systems via AM‐LSTM Networks Based Nonlinear MPC

ABSTRACTThe organic Rankine cycle (ORC) serves as an effective means of converting low‐grade heat sources into power, playing a pivotal role in environmentally friendly production and energy recovery. However, the inherent complexity, strong and unidentified nonlinearity, and control constraints pose significant challenges to designing an optimal controller for ORC systems. To address these issues, this research introduces a novel modeling and control framework for ORC systems. Leveraging an attention mechanism‐based long short‐term memory (AM‐LSTM) network, the dynamic characteristics of ORC systems, which are subject to non‐Gaussian disturbances, are accurately modeled. A performance metric based on survival information potential (SIP) is developed to optimize the network parameters. Furthermore, a multi‐objective optimization approach that integrates nonlinear model predictive control (NMPC) with the multiverse optimizer (MVO) algorithm is implemented to ensure effective control under varying operating conditions and constraints. Through extensive simulations, the proposed framework demonstrates superior accuracy, robustness, and control performance for ORC systems.

Optimization and design of electromechanical control automation based on dual motor control algorithm

IntroductionIn response to the high demand for dynamic characteristics and control in current electromechanical automatic control systems.MethodsThis study first analyzes the dual motor system. A novel electromechanical control automation model based on a dual motor control algorithm is proposed through the control strategy of dual motor backlash elimination and digital proportional integral derivative control algorithm.Results and DiscussionThe results indicated that the optimization of the model had a promoting effect on the control performance of the electromechanical automatic control system. Compared with other popular electromechanical control automation models of the same type, the performance of the research method was the best. During the no-load start-up phase, the maximum tracking error and synchronization error speed of the proposed new electromechanical control automation model showed a significant decreasing trend, with the maximum synchronization error between the two motors being only 0.02%. Under steady-state sudden load, the research model could reach a stable state within 3 s, with errors within ±5%.ConclusionIn summary, combining the dual motor control algorithm with the electromechanical control automation method can provide a theoretical basis and practical guidance for designing and implementing efficient dual motor electromechanical control systems.

Management Control System and Firm Performance: A Strategic Approach

This research aims to examine the relationship between management control systems and firm performance through firm capability. The research used a survey method in processing industrial companies with a population of 137. The number of samples analyzed was 63 samples. The analytical tool used to test the hypothesis is Partial Least Square (PLS) with the help of smart PLS software version 4.0. The research results succeeded in proving a direct relationship between the management control system affecting firm capability, the management control system on firm performance, and firm capability on firm performance. Apart from that, the results of this research also prove that firm capability can mediate the relationship between the management control system and firm performance. This indicates that the company’s success in achieving optimal performance and achieving competitive advantage lies in the strategy of implementing the management control system and firm capability. Therefore, the company continues to improve its management control system and capability so that firm performance can improve and achieve competitive advantage.

Implementation and Performance Evaluation of Intelligent Techniques for Controlling a Pressurized Water Reactor

Pressurized water reactors (PWRs) are the most common and widely used type of reactor, and ensuring the stability of the reactor is of utmost importance. The challenges lie in effectively managing power fluctuations and sudden changes in reactivity that could result in unsafe situations. Reactor power fluctuations can cause changes in behavior. At the same time, the transfer of heat from the fuel to the coolant and reactivity changes resulting from differences in fuel and coolant temperatures can also make the systemunpredictable. The primary goal of a power controller used in a nuclear reactor is to sustain the specified power level, which guarantees the security of the power plant. To address these challenges, this paper presents a dynamic model of a PWR and applies several control techniques to the system for power level control. Specifically, a traditional PID controller, a Neural Network controller, a Fuzzy self-tuned PID controller, and a Neuro-Fuzzy self-tuned PID controller were individually designed and evaluated to enhance the performance of the reactor power control system under constant and variable reference power. In addition, the robustness of each controller was appraised against time delays and external disturbances. The system was tested with various initial power values to evaluate its performance under different conditions. The results demonstrate that the Neuro-Fuzzy self-tuned PID controller has the best performance, as well as the fastest response time compared to the other controllers.Furthermore, the intelligent controllers were found to exhibit good robustness against time delays and external disturbances. The system's stability was not significantly affected by changes in the initial power value, although it had a minor effect on the transient response. Overall,the findings of this study can inform the design and optimization of control systems for PWRs, with the ultimate goal of improving their safety, reliability, and performance.

Battery charging system for electric vehicle

Selecting the appropriate charger for electric vehicles (EVs) is crucial for enhancing performance, with non-isolated DC-DC converters playing a significant role in charging EV batteries. The efficient conversion of input power into output as per the requirement is main perspective in the design of DC-DC converters. This paper delves into the landscape of non-isolated DC-DC converters utilized in EV charging, emphasizing their pivotal role. Additionally, it introduces a novel approach by incorporating machine learning-based pulse width modulation (PWM) control for the buck DC-DC converter. By integrating machine learning algorithms into the control scheme, the efficiency and performance of the charging system can be greatly enhanced, resulting in improved overall EV operation. This innovative application of machine learning not only optimizes charging efficiency but also enables adaptability to varying input/output conditions, ultimately leading to more efficient and effective charging processes for EVs.