Traditional Control Research Articles

Fixed-time active fault-tolerant control for dynamical systems with intermittent faults and unknown disturbances

In this article, the problem of fixed-time active fault-tolerant control is investigated for dynamical linear systems with intermittent faults and unknown disturbances. Unlike traditional active fault-tolerant control, fixed-time control is taken into account in this article since intermittent faults appear and disappear within a certain period of time. The entire active fault-tolerant control framework is composed of fault detection, fault isolation, fault and state estimation as well as the reconfigurable controller. Using the homogeneity-based observers, states and faults are well estimated and a fault diagnosis scheme is proposed for the sake of detecting and isolating intermittent faults in a fixed time. The fault-tolerant controller, which provides global practical fixed-time stability of the closed-loop system, has two switching states corresponding to the appearance and disappearance of intermittent faults. As a consequence, intermittent faults are compensated via the designed active fault-tolerant control method and the system reaches practical stability with the entire convergence time bounded in a fixed time. Finally, two examples are exploited to demonstrate the effectiveness of theoretical results.

Design and evaluation of self-tuning optimal control for vibration suppression of magnetorheological semi-active suspension

The objective of this paper is to enhance the vibration damping capabilities of magnetorheological (MR) semi-active suspension by utilizing a self-tuning LQG control method. Firstly, to determine the mechanical model of MR damper using the hyperbolic tangent model, mechanical experiments were performed with a damping force test machine under various input excitations. Experimental and simulation data were compared to confirm the accuracy of the mechanical model for MR damper. The MR damper is then incorporated into the vehicle’s semi-active suspension system to create a quarter-car suspension model. In order to tackle the problems associated with low control accuracy and poor dynamic adjustment in traditional LQG controllers where the weighting matrix coefficients are difficult to determine, a self-tuning LQG controller based on gravity search algorithm (GSA-LQG) was designed. Finally, the designed controller performance was evaluated by the simulation and experimental results obtained from the random and sinusoidal excitation roads. Experimental data reveal that when subjected to GSA-LQG control, the suspension control performance is considerably enhanced compared to the passive control, PID control and conventional LQG control suspension. Based on these outcome, it can be concluded that the proposed algorithm is effective and the experimental platform is feasible.

Adaptive Control of Ships’ Oil-Fired Boilers Using Flame Image-Based IMC-PID and Deep Reinforcement Learning

The control system of oil-fired boiler units on ships plays a crucial role in reducing the emissions of atmospheric pollutants such as nitrogen oxides (NOx), sulfur dioxides (SO2), and carbon dioxide (CO2). Traditional control methods using conventional measurement sensors face limitations in real-time control due to response delays, which has led to the growing interest in combustion control methods using flame images. To ensure the precision of such combustion control systems, the system model must be thoroughly considered during controller design. However, finding the optimal tuning point is challenging due to the changes in the system model and nonlinearity caused by environmental variations. This study proposes a controller that integrates an internal model control (IMC)-based PID controller with the deep deterministic policy gradient (DDPG) algorithm of deep reinforcement learning to enhance the adaptability of image-based combustion control systems to environmental changes. The proposed controller adjusts the PID parameter values in real-time through the learning of the determination constant lambda (λ) of the IMC internal model. This approach reduces computational resources by shrinking the learning dimensions of the DDPG agent and limits transient responses through constrained learning of control parameters. Experimental results show that the proposed controller exhibited rapid adaptive performance in the learning process for the target oxygen concentration, achieving a reward value of −0.05 within just 105 episodes. Furthermore, when compared to traditional PID tuning methods, the proposed controller demonstrated superior performance, achieving a target value error of 0.0032 and a low overshoot range of 0.0498 to 0.0631, providing the fastest response speed and minimal oscillation. Additionally, experiments conducted on an actual operating ship verified the practical feasibility of this system, highlighting its potential for real-time control and pollutant reduction in marine applications.

Design and Comparison of Fractional-Order Controllers in Flotation Cell Banks and Flotation Columns Used in Copper Extraction Processes

This work explores efficiency improvements in the copper flotation stage, a complex nonlinear, multivariable process subject to numerous perturbations. The primary objective is to design a fractional-order PID (FOPID) control strategy and a fractional-order model reference adaptive control (FOMRAC) system. The parameters for these controllers are optimized using the particle swarm optimization (PSO) algorithm with an objective function tailored to the control goals. This study employs models of both a bank series of five flotation cells and a flotation column. Their performance results are compared against traditional controllers, such as an integer-order PID and MRAC. The findings reveal that fractional-order controllers offer notable advantages over their integer-order counterparts, showing improved performance metrics with minimal changes to the existing control framework. This research highlights the effectiveness of fractional control in enhancing flotation processes and introduces a novel application of fractional control techniques in this area.

A Method for Optimizing Terminal Sliding Mode Controller Parameters Based on a Multi-Strategy Improved Crayfish Algorithm

This paper proposes a parameter optimization method for a terminal sliding mode controller (TSMC) based on a multi-strategy improved crayfish algorithm (JLSCOA) to enhance the performance of ship dynamic positioning systems. The TSMC is designed for the “Xinhongzhuan” vessel of Dalian Maritime University. JLSCOA integrates subtractive averaging, Levy Flight, and sparrow search strategies to overcome the limitations of traditional crayfish algorithms. Compared to COA, WOA, and SSA algorithms, JLSCOA demonstrates superior optimization accuracy, convergence performance, and stability across 12 benchmark test functions. It achieves the optimal value in 83% of cases, outperforms the average in 83% of cases, and exhibits stronger robustness in 75% of cases. Simulations show that applying JLSCOA to TSMC parameter optimization significantly outperforms traditional non-optimized controllers, reducing the average time for three degrees of freedom position changes by over 300 s and nearly eliminating control force and velocity oscillations.

Voice user interfaces for effortless navigation in medical virtual reality environments

In various situations, such as clinical environments with sterile conditions or when hands are occupied with multiple devices, traditional methods of navigation and scene adjustment are impractical or even impossible. We explore a new solution by using voice control to facilitate interaction in virtual worlds to avoid the use of additional controllers. Therefore, we investigate three scenarios: Object Orientation, Visualization Customization, and Analytical Tasks and evaluate whether natural language interaction is possible and promising in each of these scenarios. In our quantitative user study participants were able to control virtual environments effortlessly using verbal instructions. This resulted in rapid orientation adjustments, adaptive visual aids, and accurate data analysis. In addition, user satisfaction and usability surveys showed consistently high levels of acceptance and ease of use. In conclusion, our study shows that the use of natural language can be a promising alternative for the improvement of user interaction in virtual environments. It enables intuitive interactions in virtual spaces, especially in situations where traditional controls have limitations.

Efficient predictive control method for ORC waste heat recovery system based on recurrent neural network

The model predictive control performs well in regulating operating parameters and ensuring system safety when the organic Rankine cycles are operating with variable heat sources. However, traditional nonlinear predictive control based on the organic Rankine cycle mechanism model involves significant computational complexity, making it challenging to quickly find a control solution. This limitation hinders its application in the organic Rankine cycle for rapid response control. To address this issue, a fast model predictive control method is proposed in this work. A recurrent neural network model is well-trained using the input–output data of the organic Rankine cycle process, and it is used as a surrogate model in the design of the model predictive controller for the control of organic Rankine cycle operating parameters. The formulated optimal control problem is then transformed into a mixed integer linear programming problem, which can obtain high-quality and fast solutions during the control process. Through comparison with recurrent neural network-based nonlinear predictive control and pseudo-sequential method-based fast nonlinear predictive control, the results show that the designed controller can effectively accomplish the control of organic Rankine cycle operating parameters with smaller overshoot. Moreover, its average control solution time is shorter by 89.59% and 93.27% respectively while the total net output power of the system during the control process is 0.54% and 1.3% higher than that of the other two controllers. It exhibits superior control performance, even under variable waste heat conditions.

Design and Control of DVR Based on Adaptive Bateman Polynomial for Power Quality Improvement

ABSTRACTPower quality issues encompass a spectrum of disturbances that affect the stability and reliability of the power supply. This study explores voltage‐related anomalies, including sags, surges, flickers, unbalances, harmonics, interruptions, and swells, examining their origins, characteristics, and repercussions. These issues arise from a myriad of sources, such as load fluctuations, equipment malfunctions, and grid dynamics. The ramifications of these disturbances extend to equipment malfunctions, reduced operational efficiency, and financial losses. Dynamic voltage restorer (DVRs) are series custom power devices for mitigating these voltage‐related anomalies and use real‐time monitoring and control to quickly inject exact voltages into the grid during disruptions, restoring voltage levels to acceptable levels. This study demonstrates the capacity of DVRs to successfully alleviate power quality concerns, hence improving equipment dependability and system stability by analyzing case studies and simulation findings. The control system used to supply switching signals to the voltage source converter (VSC) of the DVR is based on adaptive Bateman polynomial (ABMP). Traditional DVR control techniques often rely on fixed or linear control strategies, which may not adequately handle varying load conditions and disturbances. ABMP offers an adaptive approach where the control parameters can adjust dynamically based on real‐time system conditions. This adaptive capability enhances the accuracy of voltage restoration, ensuring that the DVR responds optimally to varying loads and disturbances. During voltage sag and swell conditions at the grid side, the VSC injects the compensating voltage in series with the feeder with a constant switching frequency. A battery energy‐supported system (BESS) based DVR is considered for the proposed system. The supply is connected to the critical and sensitive loads. The proposed control scheme is validated through extensive simulation and experimental results.

Movement-Based Prosthesis Control with Angular Trajectory Is Getting Closer to Natural Arm Coordination.

Traditional myoelectric controls of trans-humeral prostheses fail to provide intuitive coordination of the necessary degrees of freedom. We previously showed that by using artificial neural network predictions to reconstruct distal joints, based on the shoulder posture and movement goals (i.e., position and orientation of the targeted object), participants were able to position and orient an avatar hand to grasp objects with natural arm performances. However, this control involved rapid and unintended prosthesis movements at each modification of the movement goal, impractical for real-life scenarios. Here, we eliminate this abrupt change using novel methods based on an angular trajectory, determined from the speed of stump movement and the gap between the current and the 'goal' distal configurations. These new controls are tested offline and online (i.e., involving participants-in-the-loop) and compared to performances obtained with a natural control. Despite a slight increase in movement time, the new controls allowed twelve valid participants and six participants with trans-humeral limb loss to reach objects at various positions and orientations without prior training. Furthermore, no usability or workload degradation was perceived by participants with upper limb disabilities. The good performances achieved highlight the potential acceptability and effectiveness of those controls for our target population.

Experimental Implementation of a TD3 Agent Based Speed Controller for Direct Torque Control of PMSM Drives

This manuscript presents a novel control approach for Permanent Magnet Synchronous Motors (PMSMs) by integrating the widely used Direct Torque Control (DTC) method with Deep Reinforcement Learning (DRL), specifically the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The TD3 algorithm is well-suited to address the challenges posed by high-dimensional state and action spaces in PMSM drives. The conventional Proportional–Integral (PI) controller in the outer loop of DTC is replaced with the DRL based TD3 agent to overcome the limitations of traditional PI controllers, such as model dependency and complex parameter tuning. The DRL technique allows the agent to learn optimal control policies from experience, making it suitable for complex and nonlinear control problems. Extensive simulation studies demonstrate the effectiveness of the TD3-based control in achieving accurate speed tracking under varying operating conditions. Real-time experimental validation using a TMS320F28379D digital signal processor confirms the feasibility of the proposed DRL based approach. The research offers new insights into improving the performance of PMSM drives and paves the way for future advancements in electric drive control.

Research on design and control methods of a lightweight upper limb joint isokinetic rehabilitation training equipment.

Isokinetic exercise can improve joint muscle strength and stability, making it suitable for early rehabilitation of stroke patients. However, traditional isokinetic equipment is bulky and costly, and cannot effectively avoid external environmental interference. This paper designed a lightweight upper limb joint isokinetic rehabilitation training equipment, with a control system that includes a speed planning strategy and speed control with disturbance rejection. Based on the established human-machine kinematic closed-loop model between the equipment and the user, a dynamic evaluation method of torque at the joint level was proposed. To validate the effectiveness of the equipment, experiments were conducted by manually applying random disturbances to the equipment operated at an isokinetic speed. The results showed that the root mean square error between the observed torque curve of the second-order linear extended state observer used in this paper and the actual disturbance curve was 0.52, and the maximum speed tracking error of the speed control algorithm was 1.27%. In fast and slow sinusoidal speed curve tracking experiments, the root mean square errors of the speed tracking results for this algorithm were 9.65 and 5.27, respectively, while the tracking errors for the PID speed control algorithm under the same environment were 19.94 and 12.11. The research results indicate that compared with traditional PID control method, the proposed control strategy demonstrates superior performance in achieving isokinetic control and suppressing external disturbances, thereby exhibiting significant potential in promoting upper limb rehabilitation among patients.

Study on low-mid-frequency broadband sound absorption properties of bending acoustic metamaterials with embedded porous materials

With the rapid development of the traffic industry, noise issues are becoming increasingly serious, and the traditional noise control technologies have the problems of poor low-frequency noise absorption and narrow bandwidth. This study proposes a variable-section bending acoustic metamaterial with an embedded porous material (VS_BAMP). A theoretical model of the VS_BAMP unit is developed based on the Johnson-Champoux-Allard (JCA) model and the impedance transfer method. The sound absorption unit with a thickness of 48 mm exhibits a quasi-perfect (α = 0.98) at 736 Hz, and an efficient sound absorption (α > 0.8) in the range of 574 Hz–966 Hz. Based on the complex frequency plane method, this work designs sound absorption units that exhibit perfect sound absorption at discrete frequencies. By connecting two different absorption units (PVS_BAMP) in parallel, efficient sound absorption from 424 Hz to 1500 Hz is achieved. Finally, the accuracy of the theoretical model is verified by experiments and simulations, confirming the effective sound absorption of PVS_BAMP structure in the middle and low frequency bands. The prepared PVS_BAMP is highly adjustable, has a wide bandwidth, and can be prepared through a simple manufacturing process. Our results can provide a theoretical basis for the design of compact low-mid-frequency broadband noise reduction structures for practical application.

Advances and Challenges for the Malaria Vaccine: A Review

Malaria remains a leading cause of morbidity and mortality worldwide, particularly in sub-Saharan Africa and Southeast Asia, where transmission rates are highest. Traditional control measures, such as insecticide-treated bed nets and antimalarial drugs, have helped reduce the disease’s impact, but challenges such as drug and insecticide resistance continue to undermine these efforts. A highly effective malaria vaccine represents a crucial step towards achieving long-term control and eventual eradication. This review explores recent advances in malaria vaccine development, including the RTS, S/AS01 and R21/Matrix-M vaccines, while highlighting their limitations related to efficacy, immunity duration, and accessibility in resource-poor regions. Additionally, it addresses the challenges in developing multivalent vaccines, targeting different strains of Plasmodium, and the need for integrated approaches combining vaccination with existing control strategies. Looking ahead, the review discusses the potential of next-generation vaccines, mRNA platforms, and hybrid strategies that could enhance global malaria control efforts. Keywords: Malaria vaccine, RTS, S/AS01, R21/Matrix-M, transmission-blocking vaccines, malaria control.

A Direct Contour Control Method for Free-Form Surface Machining Trajectories Based on Coordinate Transformation

The requirement for high-speed and high-precision contouring with free-form surfaces in the CNC machining process is increasing. The contour error is an essential criterion for the quality of CNC machining. For the problem of contour error control, the present research is mainly based on position tracking, and the control method directly aiming at contour control has not been well studied. This paper proposed a new type of coordinate transformation-based direct contour control method (CT-DCC) for free-form surface machining trajectories. By real-time monitoring of the contour error and the foot point of the free-form parametric trajectory, the contouring task is decomposed into the contour error control task in the normal direction and the velocity control problem in the feed direction. Using coordinate transformation, the output commands of the two tasks are converted to the axial motion command. CT-DCC completely eliminates position tracking in the traditional control to achieve direct control of the contour error, which provides a new solution for the contour control problem of free-form surface machining. Both the axial velocity control and the axial torque control-based CT-DCC scheme were studied, and the two-axis and three-axis direct contour controllers were tested. The simulation and experiment results show the feasibility of the proposed method.

Evaluation of advanced control strategies for PMSG in wind turbines: backstepping fuzzy logic control and adaptive neural network control approaches

This study presents a comparative analysis of two advanced control strategies for Permanent Magnet Synchronous Generators (PMSGs) in wind turbines: Backstepping Fuzzy Logic Control and Adaptive Neural Network Control. The aim is to evaluate their effectiveness in handling structured and unstructured uncertainties and enhancing system performance. Backstepping Fuzzy Logic Control integrates backstepping techniques with fuzzy logic regulation to improve flexibility and robustness. This method addresses the limitations of traditional PI controllers by enhancing adaptability to parameter variations and external disturbances. In contrast, Adaptive Neural Network Control employs neural networks with dynamic compensators to address high uncertainties and nonlinearities, aiming to surpass the performance of conventional PI controllers. The study involves simulations to compare these control strategies based on several criteria: robustness in managing parameter changes and external disturbances, flexibility in adapting to dynamic conditions, and overall performance including efficiency and stability. Simulation results indicate that both strategies offer significant improvements over traditional PI controllers. Backstepping Fuzzy Logic Control demonstrates strong adaptability and robustness, while Adaptive Neural Network Control shows superior efficiency and optimality, particularly in high-uncertainty environments. This comparative analysis provides insights into the strengths and limitations of each approach, offering guidance for selecting the most suitable control strategy based on specific operational requirements. The findings contribute to advancing control methodologies in wind energy systems and suggest directions for future research and practical implementation.

Robust decentralized controller design for highly Maneuverable Aircraft

Large-scale control systems often encounter challenges due to their intricate control structures, leading to delays and uncertainties when traditional centralized control methods are implemented in engineering applications. In response to these challenges and with the aim of enhancing control efficiency, the exploration of decentralized control approaches has become increasingly prominent. This paper presents two decentralized control strategies: fixed structure control theory, which is built upon the foundational principles of traditional robust controller design, and the weighted model approximation method, which integrates weighted model reduction with optimization strategies. The fixed structure control theory predetermines architecture of the controllers, significantly alleviating the computational burden associated with the development of robust controllers. Conversely, the weighted model approximation method proposes a more intuitive and succinct pathway for controller design and engineering application. The efficacy of these strategies is evaluated through their application to the Highly Maneuverable Aircraft (HIMAT) system. The results demonstrate that, the fixed structure control theory generally exhibits good performance. However, in certain engineering scenarios, the conclusions derived from the weighted model approximation method for decentralized controller design are easier to comprehend and facilitate rapid application.

Comparison Between Conventional PID Controller and Neural Network PID Controller Based on DC Motor Speed Control

DC motor is a multivariable, strong coupling and nonlinear controlled system, and is difficult to setting up the accurate mathematical model. So, it cannot achieve good control effect using traditional PID control method. To tackle this problem ANN based PID controller is proposed in this paper. The objective here is to compare the response of DC motor controlled by two different PID controllers.

ЗБЕРЕЖЕННЯ РІДКІСНИХ ЛІСОВИХ ВИДІВ СУДИННИХ РОСЛИН У СИСТЕМІ ПРИРОДООХОРОННИХ ЗАХОДІВ МЕЗИНСЬКОГО НАЦІОНАЛЬНОГО ПРИРОДНОГО ПАРКУ

The article characterizes the peculiarities of the formation of forest ecosystems as centers for the distribution of rare species within the territory of Mezyn National Nature Park. The research area is notable for its diverse ecotopic conditions, which support a wide variety of forest species of vascular plants and corresponding communities, including oak, linden-oak, maple-linden-oak, linden, and hornbeam-oak forests. The forest group of vascular plant flora in the territory of Mezynskyi NNP includes 18 species, with 10 species listed in the Red Data Book of Ukraine and 8 species having regional protection status in Chernihiv Oblast. Among the forest species from the Red Data Book of Ukraine, the non-moral group predominates, including Neottia nidus-avis, Listera ovata, Epipactis atrorubens, Epipactis helleborine, Platanthera bifolia, Platanthera chlorantha, Lilium martagon, and Allium ursinum. Among the species of vascular plants in forest ecosystems that are protected at the regional level in Chernihiv region, within the study area, 7 species of the division Polypodiophyta are distinguished, specifically: Dryopteris cristata, Gymnocarpium dryopteris, Matteuccia struthiopteris, Polypodium vulgare, Polystichum aculeatum, P. brauni, and Phegopteris connectilis. Anthropogenic impacts on the forest areas of Mezyn NNP, including on rare species of vascular plants, can be classified into the following groups: production and traditional economy, recreational activities, forestry activities and control measures, and violation of the protected area. The greatest impact of the recreational factor is on recreational forest areas and areas around settlements. The development of measures and plans for the protection of rare species will ensure the principles of protecting forest ecosystems as a whole (general) and their individual components (special). The article presents a system of environmental measures for the protection and conservation of rare forest species of vascular plants, and suggests measures for further conservation and monitoring of these species. To address the issues of conserving rare species, the article recommends optimizing the zoning system of the park territory, expanding the protected area, and including existing protected areas and habitats of rare flora species within its composition.

Larvicidal activity and chemical composition of essential oils against Aedes aegypti Linn (Diptera: Culicidae) and Aedes albopictus Skuse (Diptera: Culicidae)

Introduction Aedes mosquitoes are the main vectors of the dengue, chikungunya, and Zika viruses. Essential oils have been used in research as alternatives to the synthetic insecticides traditionally used in programs to control these diseases. Methods: leaves of plants from the Caatinga biome were used to obtain essential oils which were used for larvicide essays against Aedes aegypti and Aedes albopictus larvae. Results: significant differences were observed in the LC50 values of C. zeylanicum, C. winterianus, E. citriodora, O. micranthum, and T. erecta essential oils in Ae. aegypti, while these differences were observed for A. conyzoides, C. winterianus, E. citriodora, and O. micranthum essential oils in Ae. albopictus, at the analyzed times. The LC50 for 24 h revealed a significant larvicidal effect for all tested samples, with emphasis on the effect of A. conyzoides and L. gracilis essential oils on Ae. aegypti, and the effect of A. conyzoides, C. zeylanicum, C. winterianus, L. gracilis, and O. micranthum essential oils on Ae. albopictus, all with an LC50 < 100 ppm. It was observed a predominance of terpenes as components in the essential oils, and, in some of them, representing the major constituent (citronellal: 47.71% in E. citriodora and 47.63% in C. winterianus; geraniol: 30.54% in C. winterianus; citronellol: 25.61% in E. citriodora; carvacrol: 55.13% in L. gracilis; and cyclohexen-1-one, 2-isopropyl-5-methyl-: 69.94% in T. erecta). The other major compounds observed were precocene (chromene - 97.66% - A. conyzoides), and eugenol (phenylpropanoid - 96.28% and 73.21% - C. zeylanicum and O. micranthum, respectively). Conclusions: the findings of this study revealed the larvicidal potential of these essential oils on Ae. aegypti and Ae. albopictus control, representing an alternative source to the traditional chemical controls used against the populations of these vectors. Further studies on the effects on non-target organisms and the combined action of two or more essential oils evaluated under field conditions are essential for obtaining commercially efficient formulations of these extracts.

Improved Sliding Mode Model Reference Adaptive System Observer

In order to improve the accuracy and stability of sensorless control system of permanent magnet synchronous motor, considering the influence of external load torque on system performance, the symbol function of sliding mode control system of vehicle-mounted permanent magnet synchronous motor is prone to chattering and the robustness of traditional PI control is insufficient. In this paper, a method of combining Non-singular Fast Terminal Sliding Mode (NFTSM) and Model Reference Adaptive System (MRAS) is proposed. A Non-singular Fast Terminal Sliding Mode Controller (NFTSMC) is designed. A control strategy combining NFTSMC and Model Reference Adaptive System Observer (MRASO) (NFTSMC-MRASO) converts the observed torque value into torque current feedforward compensation to the current loop input. The large sliding mode gain is avoided, and the chattering problem of the system is overcome when the external disturbance is suddenly added. At the same time, the robustness of the system is improved under the premise of suppressing the chattering.