Control Performance Research Articles

Modeling and adaptive NN control for SMA flexible actuating systems with modified GPI hysteresis model

In this study, a shape memory alloy (SMA) actuating system is constructed to achieve flexible actuation ability. To improve the actuating performance, an effective control scheme needs to be developed to address the negative effects caused by the nonlinear nature of the internal SMA material and accommodate complex operating conditions. A modified generalised Prandtl-Ishlinskii (MGPI) hysteresis model is proposed to accurately characterise the strong saturated asymmetric hysteresis nonlinearity in the SMA wires. The modification of the input shape function allows for greater flexibility in controller design. Using this hysteresis modelling approach, an adaptive neural network (NN) control with a command filter is designed to achieve superior control performance. Experimental results validate the effectiveness of the proposed controller strategy.

Cognitive control in individuals with heroin use disorder after prolonged methadone maintenance treatment

BackgroundAlthough impaired cognitive control is common during the acute detoxification phase of substance use disorders (SUD) and is considered a major cause of relapse, it remains unclear after prolonged methadone maintenance treatment (MMT). The aim of the present study was to elucidate cognitive control in individuals with heroin use disorder (HUD) after prolonged MMT and its association with previous relapse.MethodsA total of 63 HUD subjects (41 subjects with previous relapse and 22 non-relapse subjects, mean MMT duration: 12.24 ± 2.92 years) and 31 healthy controls were enrolled in this study. Eye tracking tasks, prospective memory tasks, the Behavior Rating Inventory of Executive Function-Adult Version (BRIEF-A) and the Prospective and Retrospective Memory Questionnaire (PRMQ) were used to assess cognitive control.ResultsHUD individuals exhibited worse saccade error rate and executive dysfunction but showed no significant impairment in prospective memory. Additionally, the relapsers performed worse in terms of antisaccade amplitude and velocity at higher difficulty gradients (11° or 16°). Antisaccade performance in terms of amplitude and velocity was negatively correlated with executive function scores. Deficits in inhibition, cognitive flexibility, and self-monitoring were found to mediate the relationship between previous relapse and impaired antisaccade performance.ConclusionsEven after prolonged MMT, HUD individuals still show partial impairments in cognitive control and antisaccade performance. Previous relapse exacerbates cognitive control deficits through executive dysfunction in inhibition, cognitive flexibility and self-monitoring, which can be screened by higher difficulty of antisaccade amplitude and velocity. More importantly, saccade error rate can reflect impaired inhibitory control in HUD individuals, whereas antisaccade amplitude and velocity appear to have potential diagnostic value for relapse.

An Extended Study of the Performance of Flexible Controllers Composed of Micro-controllers

Generic controllers for self-adaptive systems can be configured parametrically according to system needs, even though their reuse is restricted because of the wide range of services that may be provided by each stage of a feedback control loop, like MAPE-K. Rainbow is a typical example of such a generic, monolithic controller. This article revisits and extends prior work that advocates structurally flexible controllers, as ensembles of micro-controllers each providing specific services. We experimented with our approach with three different architectural configurations for the controller: monolithic, decentralised, and decentralised with a meta-controller. Our results indicate that despite the decentralised configuration with a meta-controller demanding more computational resources, it performed comparatively well compared to the other configurations, including when measuring the target system’s response time. Moreover, we found that variations of the control loop timing at the different layers of the controller impact the stability of the target system. We have also evolved the controller by adding a new micro-controller, which caused no impact on the other micro-controllers, and mostly kept the target system’s performance. We conclude that a multi-layered controller design, based on micro-controllers, provides the basis for defining structurally flexible controllers at operational-time, and may promote reuse at development-time.

State switch control of magnetically suspended flywheel energy storage system in uninterrupted power supply system

The magnetically suspended flywheel energy storage system (MS-FESS) is an energy storage equipment that accomplishes the bidirectional transfer between electric energy and kinetic energy, and it is widely used as the power conversion unit in the uninterrupted power supply (UPS) system. First, the structure of the FESS-UPS system is introduced, and the working principles at different working states are described. Furthermore, the control strategy of the FESS-UPS is developed, and the switch oscillation of the FESS-UPS system between the charging and discharging states is analyzed. Then, the switch strategy using the angle compensation of the flux linkage is designed to control the FESS-UPS system among different working states, and the peak values of current and voltage at the switch moment are suppressed. Finally, the simulations and experiments are performed to validate the performances of the switch strategy used in the FESS-UPS system, and the results prove that the current/voltage peaks during the switching process are effectively mitigated, so the impact on the power converter caused by the state switch is suppressed. Therefore, the control performance of the UPS using the MS-FESS could be further improved, and the FESS-UPS could realize the fast and safe discharge/charge for the grid source and three-phase loads.

Secure Control of Networked Control Systems Under Replay Attacks: A Switching‐Like Approach

ABSTRACTThis paper focuses on the secure control design of networked control systems (NCSs) under replay attacks. Mitigating replay attacks is a challenging task since the statistical similarity between replayed signals and real observations makes them difficult to distinguish. Given the persistence of potential attackers and the probability of individual attack failures, a novel switching‐based replay attack model is introduced. By employing this replay attack model, NCSs vulnerable to replay attacks can be transformed into a class of switching‐like systems. Within the framework of the average dwell time switching strategy and multiple Lyapunov stability theory, sufficient conditions for the mean‐square exponential stability of the underlying system are presented. Compared with the existing control schemes using the delay‐based replay attack model, the proposed secure control method effectively reduces the impact of replay attacks on the control performance. The effectiveness of the proposed approach is validated through simulation experiments carried out on two practical systems.

A Distributed Actor‐Critic Learning Approach for Affine Formation Control of Multi‐Robots With Unknown Dynamics

ABSTRACTFormation maneuverability is particularly important for multi‐robots (MRs), especially when the robots are operating cooperatively in complex and dynamic environments. Although various methods have been developed for affine formation, it is still a difficult problem to design an affine formation controller for MRs with unknown dynamics. In this paper, a distributed actor‐critic learning approach (DACL) in a look‐ahead rollout manner is proposed for the affine formation of MRs under local communication, which improves the online learning efficiency. In the proposed approach, a distributed data‐driven online optimization mechanism is designed via the sparse kernel technique to solve the near‐optimal affine formation control issue of MRs with unknown dynamics as well as improve control performance. The unknown dynamics of MRs are learned offline based on precollected input‐output datasets, and the sparse kernel‐based approach is employed to increase the feature representation capability of the samples. Then, the proposed distributed online actor‐critic algorithm for each robot in the formation includes two neural networks, which are utilized to approximate the costate functions and the near‐optimal policies. Moreover, the convergence analysis of the proposed approach has been conducted. Finally, numerical simulation and KKSwarm‐based experiment studies are performed to verify the effectiveness of the proposed approach.

Power Sharing of Different Distributed Generator Sets in Microgrid Based on Consensus Control

ABSTRACTMicrogrids incorporate renewable energy sources, battery energy storage systems (BESS), and local loads to operate either with the main grid or independently. In microgrids, virtual synchronous generators (VSGs) and droop control are commonly used to regulate renewable energy sources, providing frequency and voltage control. The VSG and droop control adjust active power based on frequency deviation. However, their effectiveness depends on parameters and does not fully address power‐sharing issues. Traditionally, power sharing is handled by secondary control via the microgrid central controller (MGCC), introducing a centralized, hierarchical approach. To enhance decentralization in microgrids, this paper proposes a consensus based power sharing scheme and its corresponding control for the distributed generators (DGs). The output power levels of DG units are determined by their capacities, ensuring efficient operation. Unlike existing approaches using a hierarchical framework, this work integrates primary and secondary control into a unified distributed control framework. Building on VSG control for microgrid voltage regulation, consensus control is added to manage the distribution of active power. A rigorous theoretical analysis is conducted to validate the proposed method. The method is capable of ensuring active power sharing without relying on an MGCC, and the sharing is also based on the ratio of rated capacities. The controller proposed in this paper achieves the expected control performance, effectively distributing active power sharing in the microgrid. Finally, the effectiveness and performance of the proposed method are verified through comprehensive case studies.

Exploring Motor–Cognitive Interference Effects and the Influence of Self-Reported Physical Activity on Dual-Task Walking in Parkinson’s Disease and Healthy Older Adults

Introduction: Parkinson’s disease (PD) is characterized by motor and cognitive impairments that often manifest as distinct motor subtypes: Postural Instability Gait Difficulty (PIGD) and Tremor-Dominant (TD). Motor–cognitive interference, especially under dual-task (DT) walking conditions, may vary by subtype, providing insights into specific impairments. This study explored DT interference effects in PD subtypes, focusing on the potential impact of self-reported physical activity, which may help mitigate subtype-specific impairments and improve motor–cognitive function. Methods: PD patients classified as PIGD or TD and healthy controls completed single-task (ST) and DT walking assessments involving different cognitive tasks (Serial Subtraction, Auditory Stroop, and Clock Task). Physical activity levels were evaluated using the CHAMPS questionnaire, analyzing the self-reported frequency and duration of weekly exercise-related activities. Results: Interference effects were significantly different between PD patients and controls, with the PIGD group showing greater motor impairment under high cognitive load, primarily affecting gait, than the TD and control groups. Performance differences between groups diminished as cognitive load increased. Self-reported physical activity does not significantly moderate motor performance under DT conditions, suggesting that activity levels in this sample are insufficient to offset motor–cognitive interference. However, like group affiliation, physical activity directly influences motor performance during DT conditions, indicating that both factors independently impact motor–cognitive function in PD. Discussion: These findings suggest that DT assessments help differentiate PD motor subtypes, as group differences were minimal in ST conditions. While physical activity is associated with general improvements in motor ST and DT performance in PD and controls, the lack of a significant moderating effect from self-reported exercise-related physical activity indicates that current activity levels may not be high enough to counter motor–cognitive interference. More intensive or DT-specific exercise may be required to reduce interference effects. Future research should examine the role of structured physical activity programs, potentially incorporating DT training, to evaluate their impact on motor–cognitive interference in PD.

Three-Dimensional Dynamic Positioning Using a Novel Lyapunov-Based Model Predictive Control for Small Autonomous Surface/Underwater Vehicles

Small Autonomous Surface/Underwater Vehicles (S-ASUVs) are gradually attracting attention from related fields due to their small size, low energy consumption, and flexible motion. Existing dynamic positioning (DP) control approaches suffer from chronic restrictions that hinder adaptability to varying practical conditions, rendering performance poor. A new three-dimensional (3D) dynamic positioning control method for S-ASUVs is proposed to tackle this issue. Firstly, a dynamic model for the DP control problem considering thrust allocation was established deriving from dynamic models of S-ASUVs. A novel Lyapunov-based model predictive control (LBMPC) method was then designed. Unlike the conventional Lyapunov-based model predictive control (LMPC), this study used multi-variable proportional–integral–derivative (PID) control as the secondary control law, improving the accuracy and rapidity of the control performance significantly. Both the feasibility and stability were rigorously proved. A series of digital experiments using the S-ASUV model under diverse conditions demonstrate the proposed method’s advantages over existing controllers, affirming satisfactory performances for 3D dynamic positioning in complex environments.

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.

Improving Quadrotor Tracking Performance in Wind Disturbances Using Hybrid Reinforcement Learning Control

The control performance of model-based control approaches depends on the accuracy of the system dynamics. However, due to unknown environmental disturbances, quadrotor dynamics are time-varying during real-world flight, and real-time updating of the dynamics is required to avoid degradation of control performance due to model errors. To address this challenging issue, this paper proposes a hybrid controller that combines model predictive control (MPC) with model-based reinforcement learning (MBRL) to improve quadrotor trajectory tracking accuracy in unknown wind conditions. The MPC controller uses simple dynamics identified from offline flight data. The MBRL controller learns MPC-augmented dynamics represented by the Gaussian process (GP) and the residual policy from online flight data. The RL controller outputs additional actions to compensate for MPC control errors, while MPC can ensure the safety of RL exploration. The computational cost of GP when dealing with a large dataset affects the real-time performance. We design a priority data selection criterion to maintain a small dataset, balancing model accuracy and computational time. The accurate tracking results for different reference trajectories under various wind conditions demonstrate the robustness of the proposed approach. Comparative results with nominal MPC and related baselines show the advantages of the proposed approach in terms of control and real-time performance.

Research on the Coordinated Control of Mining Multi-PMSM Systems Based on an Improved Active Disturbance Rejection Controller

This study focuses on the problems of poor control performance, synchronization performance and stability in multi-motor permanent magnet drive systems in mining belt conveyors when a Proportional Integral Derivative (PID) controller is used to control the multi-motor. In this paper, a system model for three-motor synchronous control of a mine belt conveyor is established. On this basis, an Enhanced first-order Active Disturbance Rejection Controller (Efal_ADRC) is designed based on an optimized nonlinear function. Additionally, a weighted arithmetic mean is used to enhance the compensator of the ring coupling control structure. Finally, the system model is evaluated and simulated using various algorithms. Results show that synchronous control of a multi-Permanent Magnet Synchronous Motor (multi-PMSM) drive system based on the Efal_ADRC ring coupling control structure has better anti-interference ability, control accuracy and synchronization, which is conducive to the stable and efficient safe operation of the belt conveyor.

Integrated fault estimation and control for unknown discrete-time systems: a data-based multivariable-coordinated optimisation method

This paper addresses the integrated fault estimation and robust control problem for unknown linear discrete-time systems. The considered problem is formulated as a multivariable multiobjective optimisation one. A data-based coordinated design strategy that co-designs an output feedback H ∞ / H ∞ controller and a residual generator is proposed to optimise both H ∞ fault estimation and robust control performances. Based on the input and output data, the design parameters of the controller and the residual generator are determined by using Q-learning technique and introducing a new matrix block identification Q-learning method, respectively. Compared with the existing single-variable multiobjective optimisation methods, the proposed strategy can achieve better fault estimation performance, remove the restriction condition of using input and output data, and extend the traditional Q-learning technique to the case of designing a residual generator with a low-rank condition. Finally, simulation results illustrate the effectiveness of the proposed method.

Benefits of Using Functional Joint Coordinate Systems in In Vitro Knee Testing.

To measure knee joint kinematics, coordinate systems (CS) must be assigned to the tibia and femur. Functional CS have been shown to be more reproducible than Anatomical. This study aims to quantify the benefits of using Functional CS in in vitro testing. Seven cadaveric knee joints were loaded in a 6-Degree of Freedom (DOF) joint simulator. Anatomical CS were established for each joint and Functional CS were calculated based on joint kinematics during passive motion. Loading profiles were applied to the knee joints using different CS definitions. Resulting kinematics and kinetics were obtained to quantify the 1) reduction in intra-knee kinematic response variation, 2) reduction in kinematic cross-talk, 3) reduction in inter-knee kinematic response variation, and 4) improvement in force control performance, when using Functional CS compared to Anatomical. Functional CS, compared to Anatomical, 1) significantly reduced intra-knee kinematic response variation across 12 combined loading conditions for nearly all DOF, 2) significantly reduced kinematic cross-talk during anterior-posterior, varus-valgus and internal-external rotation laxity testing across many DOF, 3) significantly reduced inter-knee kinematic response variation for all DOFs over a gait profile and combined loading conditions, and 4) significantly improved Anterior-Posterior and Varus-Valgus force/torque control performance during dynamic loading profiles. The advantage of using Functional CS for in vitro testing has been demonstrated across all considered domains. Functional CS should be used when performing in vitro knee joint testing.

PTPOLOS guidance-based path following control for underactuated hovercraft with predefined-time converge

ABSTRACT This paper presents a novel predefined-time performance observer based-line-of-sight (PTPOLOS) guidance approach for path following of underactuated hovercraft. A predefined-time performance observer (PTPO) is developed to estimate vehicle sideslip caused by environmental disturbances and lumped disturbances in the system. In order to improve the control performance, a new predefined-time performance function (PTPF) is applied to the design of the controller, which guarantees that the tracking error converges to the desired boundaries in a predefined time. Finally, we demonstrate through stability analysis that the closed-loop system is globally uniformly ultimately bounded, and the validity of the proposed method is verified by simulation comparison.

Experimental investigation of the effects of different DBD plasma actuators on the aerodynamic performance of the NACA0012 airfoil

Abstract This study aims to investigate the flow control performance of linear and serpentine dielectric barrier discharge (DBD) plasma actuators mounted on the leading edge of a NACA0012 airfoil to control flow separation and improve aerodynamic performance. Experiments were conducted in a subsonic wind tunnel at Reynolds numbers of 87 × 103, 131 × 103, and 175 × 103. Velocity profiles in the wake and static pressure distributions over airfoil were measured using hot-wire anemometry and pressure sensors, respectively. All experiments conducted in the different plasma actuation parameters, including wave voltages (6, 8, 10 kV) and wave frequencies (6, 10 kHz), and the optimal combination of V pp = 10 kV and f AC = 10 kHz was identified. The results of several wind tunnel experiments showed that, a substantial increase in the lift coefficient of up to 26.58% and a delay in stall angle of attack up to 4º due to the implementation of DBD plasma actuators. Additionally, the serpentine actuator demonstrated a more significant impact on separation control compared to the linear actuator where the serpentine actuator has controlled the stall phenomenon more effectively in post-stall angles compared to the linear actuator due to generation of 3D structures.

Design of circulating temperature control system for rubber and plastic industry based on mechatronics

IntroductionIn the modern rubber and plastics industry, temperature control becomes a key factor affecting product quality and production efficiency. However, the existing temperature control system generally has the problem of temperature lag and high energy consumption. Aiming at the problem of poor control effect of current temperature control system, a new circulating temperature control system for plastic industry was proposed in this study.MethodsFirstly, fuzzy neural network and improved particle swarm optimization algorithm were introduced in this study, and then the hybrid algorithm was combined with mechatronics technology to design and implement a set of rubber and plastic industry cycle temperature control system.ResultsAccording to the test data, the improved algorithm performed well in both scenarios. Compared with the comparison algorithm, the improved algorithm has the following parameters: tuning time (scenario 1:513s, scenario 2:521s), overshoot (scenario 1:2.5%, scenario 2:2.3%), steady-state error (scenario 1:005, scenario 2:004) and absolute integral error (scenario 1:008, scenario 2: 0.007) has been significantly optimized. Simulation analysis shows that the proposed temperature control system has stronger anti-interference ability and lower energy consumption (0.6 hour energy consumption: 1086.5J, energy saving rate: 38.7%).DiscussionThe results show that the cyclic temperature control system of rubber and plastics industry is superior to the comparison system in terms of temperature control performance, anti-interference ability and energy consumption. The research not only improves the accuracy and efficiency of temperature control systems in the rubber and plastics industry, but also promotes the promotion of green and sustainable production methods. This is essential to promote the high-quality development of the plastics and rubber industry.

The Simulation of Pose Control for Stewart Platform Based on Gain-Enhanced PID with Generalized Boolean Algebra

Due to the nonlinear and time-varying uncertainties commonly present in practical industrial systems, traditional PID controllers often fail to achieve optimal control performance under significant input variations. This paper proposes a gain-enhanced PID control method optimized by generalized Boolean algebra, applying Boolean algebraic logic control strategies to traditional PID control to improve parameter adaptability and real-time control performance. Using MATLAB/Simulink simulation tools, a dual-loop control simulation model is established for the Stewart platform, incorporating both conventional PID control and the gain-enhanced PID control optimized by generalized Boolean algebra. Both models are configured with identical PID parameters for simulation, and comparative experimental results are presented. The experimental results demonstrate that, in the Simulink simulation model of the Stewart platform's pose control system, the proposed gain-enhanced PID control system optimized by generalized Boolean algebra improves the dynamic performance and response speed compared to the traditional PID control system, enhancing the real-time performance and stability of the Stewart platform control.

Easy and Straightforward FPGA Implementation of Model Predictive Control Using HDL Coder

Model Predictive Control (MPC) is widely adopted for power electronics converters due to its ability to optimize system performance under dynamic constraints. However, its FPGA implementation remains challenging due to the complexity of Hardware Description Language (HDL) programming. This paper addresses this challenge by introducing a straightforward methodology that simplifies FPGA implementation using MATLAB Simulink HDL Coder. It is shown that HDL Coder yields favorable synthesis outcomes, both in terms of area and time, compared to hand-coded HDL. Notably, the proposed method achieves a significantly reduced sampling step for the MPC algorithm—down to 32 ns—marking a substantial improvement over state-of-the-art implementations. The Integrated Logic Analyzer (ILA) IP available in the Vivado tool is used during the HIL testing phase to facilitate the real-time observation and analysis required for debugging and confirming the FPGA-implemented controller performance. Additionally, this paper discusses the advantages of utilizing HDL Coder for simplifying the FPGA programming process in power electronics applications and addresses the design challenges encountered using this methodology.

New Approach of Hybrid Momentum Exchange Devices for Spacecraft Attitude Control System

Control moment gyroscopes (CMGs) are a favorable choice for spacecraft attitude control systems thanks to their torque amplification capability. However, their performance is hindered by geometric singularities, which can severely limit control capabilities during attitude maneuvers. This paper proposes a hybrid attitude control system incorporating a single reaction wheel into the standard minimum redundant four-CMG pyramid configuration, providing an additional degree-of-freedom to avoid and escape singularities effectively. A comprehensive mathematical analysis of the system’s Jacobian matrix, using row echelon form, is performed alongside a geometrical analysis of the hybrid configuration’s momentum envelope. These analyses demonstrate the reflection of a reaction wheel inclusion into the system of four CMGs in the improvement of the system’s capacity to avoid/escape singularity. Additionally, an asymptotic control law and an adapted steering law are developed to optimize control performance. The effectiveness of the proposed hybrid system is validated through simulation, which includes a comparative analysis with traditional CMG-only system. The results highlight the superiority of the hybrid system in handling singularities.