ABSTRACT With the development of new energy vehicle technologies, distributed‐drive electric vehicles (DDEVs) have emerged as a pivotal direction for enhancing vehicle maneuverability and energy efficiency thanks to their distinctive feature of independent motor‐driven four wheels. To address strong nonlinear dynamics, sensor measurement error, and difficult parameter tuning, this paper presented a Bayesian optimization‐active disturbance rejection control (BO‐ADRC) method for lateral stability control of DDEVs. Firstly, to address the insufficient accuracy of traditional linear tire models under large lateral forces, an improved nonlinear brush tire model was proposed based on a third‐order Taylor expansion for a 2‐degree‐of‐freedom (2DOF) nonlinear vehicle dynamics model. Secondly, a sampled‐data output‐feedback controller‐based ADRC algorithm was designed to achieve dynamic compensation of complex disturbances through multimodule collaboration, which includes a tracking differentiator (TD), an extended state observer (ESO), and a nonlinear state error feedback law (NLSEF). Thirdly, a BO for NLSEF parameter tuning was proposed to achieve global adaptive tuning for improving the control effectiveness. Fourthly, a minimum driving force‐based yaw moment allocation strategy was introduced, which allocated target yaw moment to all four wheels while minimizing the total energy consumption of the drive system. Simulations were performed to validate the effectiveness of the proposed method under various scenarios. Finally, real vehicle experiments were conducted on a self‐designed vehicle platform, confirming the engineering practicability and effectiveness of the proposed method.

A collaborative neural network-based framework for distributed disturbance compensation in satellite constellations

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in complex, uncertain, and adversarial environments, yet they remain vulnerable to actuator faults, environmental disturbances, and deliberate interferences such as jamming or spoofing. Conventional control approaches typically address these challenges independently, focusing on either fault-tolerant control (FTC), disturbance rejection, or counter-UAV defense. This paper presents an integrated anti-interference and fault-tolerant control framework that unifies three complementary modules: (i) residual-based fault detection and isolation, (ii) adaptive extended state observer (AESO)-based disturbance estimation and compensation, and (iii) counter-UAV evasive maneuver strategies. The framework is formulated using a nonlinear UAV dynamic model and rigorously analyzed under Lyapunov stability theory. Simulation results demonstrate significant performance improvements compared with baseline controllers. The proposed method reduced position and attitude deviations to below 0.05m and 0.03deg, respectively, under simultaneous actuator faults and wind disturbances. Fault and disturbance estimation errors remained below 0.05 and 0.03 units, respectively, ensuring timely control reconfiguration. Furthermore, the UAV achieved a 100% success rate in counter-drone evasive maneuvers while maintaining trajectory stability. These results confirm that the integrated design provides high resilience, rapid recovery, and reliable performance in fault-disturbed and adversarial conditions. By bridging FTC, adaptive disturbance rejection, and counter-UAV defense, the proposed framework advances the state of the art in resilient UAV control for civilian and defense applications.

The paper compares the recently modified Åström-Smith predictor (ASP) developed for IPDT models with an automatic offset controller (AOC). While an excellent performance can be achieved with ASP in idealized conditions, unacceptable transients with extraordinary high excessive controller effort and a steady-state control error result in the presence of measurement noise. AOC combines a possibly higher-order (HO) stabilizing controller (SC) with compensation of disturbances using a full disturbance observer (DOB). Nominal full DOB includes both the model dead-time and inversion of the integral mode of the model. By increasing the number of output derivatives used in the SC of the AOC, together with increasing the order of the low-pass filter used both in the SC and the DOB, it is possible to significantly increase the speed and robustness of responses in time-delayed processes, together with decreasing the measurement noise impact. The AOC based on the ultralocal IPDT model can be used to replace the higher-order PID in an universal controller for a wide class of processes with dominant first-order dynamics. Significant improvements in measurement noise attenuation can be demonstrated also in application of equivalent low-pass filters to the ASP. But even after such a modification of the noise attenuation, the filtered ASP can exhibit permanent control error or even instability at higher noise amplitudes. Hence, although even the ASP can be used as a universal controller for processes with dominant first-order dynamics, the benefit of its use should always be verified depending on the amplitude of the measurement noise. From this point of view, the use of AOC is simpler and more reliable. Despite the need for appropriate selection of the degree of derivatives used in SC and the tuning of the low-pass filters used. The conclusions of the article are illustrated by simulation experiments of unstable process control and real-time thermal process control.

The high-speed rotor electric drive system in control moment gyroscopes (CMGs) is essential for precise spacecraft attitude control. Rigorous testing of this system is critical for ensuring reliability and longevity throughout orbital missions. However, conventional test bench methods exhibit numerous limitations. In contrast, the electric motor emulator (EME) provides a flexible and efficient alternative for power-level testing of the CMG high-speed rotor brushless DC motor drive system. To address the challenges of trapezoidal back-electromotive force (back-EMF) emulation and insufficient square-wave current tracking accuracy in existing brushless DC motor emulator (BLDCME) implementations, this paper proposes a sliding time window-based dynamic current compensation control (STW-DCCC) strategy for the CMG high-speed rotor BLDCME. First, based on the VSC single-conversion-circuit topology, the BLDCME basic control strategy based on the motor port current and the current change rate is implemented to achieve a tracking control of the square-wave current and emulation of the trapezoidal back-EMF. Building upon this foundation, a sliding time window-based RMS current compensation optimization strategy for the BLDCME is designed to provide dynamic compensation for system disturbances and thereby enhance the tracking accuracy of the square-wave current. Furthermore, by incorporating fault information, the proposed STW-DCCC strategy can also emulate the resistance unbalance fault of the brushless DC motor. Finally, through experiments, a comparative analysis is conducted between the basic control strategy and the proposed STW-DCCC strategy under normal operating conditions, parameter mismatch operating conditions, and resistance unbalance fault conditions, thereby validating the effectiveness of the proposed method.

Assembly in Motion With a Mobile Manipulator Based on Servoing Control and Disturbance Compensation

Abstract This paper proposes a sliding-mode-based optimal attitude control framework that integrates reinforcement learning (RL) and sliding mode control (SMC) to address model uncertainties and unknown time-varying disturbances in quadrotor UAVs. The SMC is embedded into the optimal control design to achieve coordinated regulation of multiple attitude states, thereby enhancing closed-loop robustness and fast convergence performance. A neural network is introduced to perform online approximation and adaptive compensation of unknown nonlinearities and unknown time-varying disturbances in the UAV attitude dynamics, which reduces the dependence on an accurate mathematical model and improves control accuracy. An actor–critic reinforcement learning architecture is adopted to enable online optimization of the attitude control policy without requiring persistent excitation or continuous reward conditions, allowing the adaptive parameters to be effectively trained. Furthermore, the stability of the entire control system is rigorously analyzed using Lyapunov theory, guaranteeing that the attitude tracking errors are semi-globally uniformly ultimately bounded (SGUUB). Comprehensive numerical simulations and real-time flight experiments, including comparative studies with existing control strategies, are conducted to validate the effectiveness, robustness, and practical feasibility of the proposed method. The results demonstrate that the proposed control framework provides improved adaptability, control accuracy, and engineering applicability for quadrotor UAVs operating in complex and uncertain environments.

Measurement and compensation of electric field disturbances of an electrostatic deflector

An adaptive disturbance compensation method for force-sensorless control systems applied to robotic milling

Data-driven integral parameterized predictive control with disturbance compensation for space combination attitude takeover after capture

ABSTRACT Disturbance suppression has long been a challenging issue in the study of adaptive dynamic programming (ADP) algorithms. This paper introduces a novel critic neural network (NN)‐based robust ADP algorithm for a class of continuous‐time affine nonlinear systems to handle the unknown bounded external disturbances. The algorithm adopts an improved robust cost function (RCF), which simultaneously enhances the capability of rejecting the disturbances and reduces the controller's energy consumption. Based on this, a disturbance compensator is further proposed to achieve asymptotic stability of the closed‐loop system. To facilitate the application of the proposed control algorithm to practical engineering problems, a critic‐only NN is employed to approximate the optimal cost function using a novel tuning law. Consequently, the proposed critic NN and RCF‐based optimal control algorithm, combined with the RCF‐based disturbance compensator and NN tuning law, ensures the uniform ultimate boundedness (UUB) of the system's state trajectory. The effectiveness of our algorithm is demonstrated through software simulations.

Electrodialysis is an efficient separation and recovery method for ionic species such as sulfate ions. The batch operation of the electrodialysis process unit involves several decisions that affect its separation and economic performance. An optimal control system is developed that monitors the separation efficiency under real-time conditions and identifies the most suitable operating profile for the applied current voltage and the recirculation flow rate. A dynamic model is employed for the electrodialysis process, which is subsequently utilized within a dynamic optimization framework that aims to meet the separation and recovery specifications in the most economical way while satisfying the operating constraints. A discretized model using orthogonal collocation on finite elements enables the calculation of the optimal profile for the current voltage using nonlinear programming techniques. The control system has been successfully applied in the compensation of process disturbances mainly attributed to the variation of the membrane activity and other factors. Under severe membrane-activity loss (50-65%), the adaptive control profile achieved an increase of 34.9% in the degree of separation while limiting the batch-time penalty to 15.5% at the expense of higher energy consumption. An optimization problem is further formulated that determines the optimal design and operational characteristics of an industrial-scale size unit. In addition to the control variable profiles, the membrane surface that minimizes a comprehensive objective function is calculated. The objective function incorporates several targets for the electrodialysis process, such as batch duration, energy requirements, achieved degree of separation, membrane size, and control action behavior. The obtained optimal solutions are analyzed by Pareto front methods to reveal the critical trade-offs among the various competing objective function terms. The proposed approach enables the efficient separation of ions by electrodialysis in a diversely operating environment.

Purpose Multi-source disturbances present a major challenge to the control systems of hypersonic vehicles, making their rapid estimation and compensation imperative for performance assurance. In response, this study aims to present a novel fixed-time robust control strategy grounded in sliding mode theory. Design/methodology/approach Guided by an analysis of the vehicle model’s dynamics, a control-oriented framework comprising multiple decoupled subsystems is developed. Then, a fixed-time control structure based on filters, observers and controllers is constructed. In this structure, the filters are used to soften the reference signals, while the observers are used to estimate the lumped disturbances arising from the flexible state variables, parameter perturbations, actuator faults and other contributing factors. Finally, the controllers incorporating disturbance compensation are used to drive the velocity and altitude tracking of reference values. Furthermore, the stability of the closed-loop system is analyzed based on Lyapunov theory. Findings A stable control system with fixed-time convergence has been developed for hypersonic vehicles, demonstrating strong robustness against multi-source disturbances and satisfactory velocity and altitude tracking performance. Compared to the robust control system based on disturbance observer, the developed control system has obvious advantages in the fuel equivalence ratio. Originality/value A key advancement presented in this study is a fixed-time control strategy that synergistically integrates filtering, extended state observers and sliding mode control. Such a synthesized strategy opens up new possibilities for maintaining robust flight control of hypersonic vehicles amidst challenging and rapidly changing operational environments.

ABSTRACT High precision control under disturbances and uncertainties is critical to the safe, stable, and long‐term continuous operation of the system. In this paper, a composite anti‐disturbance control strategy for systems affected by both multiplicative and implicit disturbances is proposed. First, a reduced neural network functional observer is developed to estimate the partially unknown states and the implicit disturbances, which takes into account the effect of the multiplicative disturbances and the uncertainties in the implicit disturbances, both of which are related to the system's states. The neural network is employed for approximating the multiplicative disturbances. Then, a dynamic sliding mode surface with disturbance compensation is introduced to ensure exponential convergence of systems with the multiplicative disturbances and the implicit disturbances. Furthermore, a novel barrier function‐based adaptive sliding mode control law is designed to guarantee that the system trajectories reach the sliding surface, which greatly reduces chattering without requiring prior knowledge of the upper bound on system uncertainties. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed approach.

To mitigate rotor vibrations in magnetic bearing systems arising from mass imbalance, this study proposes a novel suppression strategy that integrates the crested porcupine optimizer (CPO) with an enhanced linear active disturbance rejection control (ELADRC) framework. The approach introduces a disturbance estimation and compensation scheme based on a linear extended state observer (LESO), wherein both the LESO bandwidth and the LADRC controller parameter are adaptively tuned using the CPO algorithm to enable decoupled control and real-time disturbance rejection in complex multi-degree-of-freedom (DOF) systems. Drawing inspiration from the crested porcupine’s layered defensive behavior, the CPO algorithm constructs a state-space model incorporating rotor displacement, rotational speed, and control current, while leveraging a reward function that balances vibration suppression performance against control energy consumption. The optimized parameters guide a real-time LESO-based compensation model, achieving accurate disturbance cancelation via amplitude-phase coordination between the generated electromagnetic force and the total disturbance. Concurrently, the LADRC feedback structure adjusts the system’s stiffness and damping matrices to improve closed-loop robustness under time-varying operating conditions. Simulation studies over a wide speed range (0~45,000 rpm) reveal that the proposed CPO-ELADRC scheme significantly outperforms conventional control methods: it shortens regulation time by 66.7% and reduces peak displacement by 86.8% under step disturbances, while achieving a 79.8% improvement in adjustment speed and an 86.4% reduction in peak control current under sinusoidal excitation. Overall, the strategy offers enhanced vibration attenuation, prevents current saturation, and improves dynamic stability across diverse operating scenarios.

Achieving high-precision motion control for hydraulic manipulators presents a challenging task. Addressing the issue of low motion control accuracy caused by the strong electromechanical-hydraulic coupling characteristics of hydraulic manipulator systems, this paper innovatively introduces an RBF neural network and an Actor–Critic reinforcement learning architecture within a performance-based control framework designed using the inverse method. This approach enables dual compensation for both internal uncertainties and external disturbances within the manipulator, thereby enhancing the system’s control performance. First, within the control architecture, the performance function ensures system transient performance while employing an RBF neural network to estimate and compensate for internal unmodeled errors caused by mechanical coupling and hydraulic parameter uncertainties. Stability proofs are used to derive the network weight update rate. Second, a disturbance compensator is designed based on reinforcement learning. Deployed into the controller through offline training and online adaptation, it compensates for external system disturbances, further improving control accuracy. Finally, comparative and ablation experiments conducted on a hydraulic manipulator testbed demonstrate the effectiveness of the disturbance compensator. Compared to PID control, the proposed approach achieves a 60–65% improvement in control accuracy.

Introduction. Rapid capacity development and the incorporation of new loads are adding complexity to the distribution power system. As a result, the distribution system faces additional power quality issues, particularly with increasingly sensitive equipment and distributed generation. Problem. Modern power systems face escalating power quality degradation due to non-linear loads. Voltage disturbances (sags, swells) and harmonic distortions directly affect the sensitive equipment, causing significant economic losses. The goal of this work is to design, model, and evaluate series active power filters (SAPFs) integrated with energy management for an independent solar storage system, using a multi-stage DC-DC converter. The objective focuses on mitigating voltage harmonics and grid disturbances resulting from diverse loads (linear, non-linear, and combined) and integrating renewable energy (PV). Control is achieved through an intelligent fuzzy logic controller (FLC) and a PI controller to ensure a stable DC voltage and reduce the total harmonic distortion (THD) of the voltage to less than 5 %. Methodology. This study models and analyzes a unique SAPF configuration integrated with a PV-battery storage system utilizing MATLAB/Simulink. Outcomes of the proposed control, wherever the FLC regulates the DC-link voltage reference signals utilize the instantaneous reactive power theory. The suggested methodology entails simulation studies across four scenarios: an analysis of performance to keep voltage components and a comparison of the proposed SAPF performance with existing research on linear, non-linear, and combined loads. Results. Simulation results show the effectiveness of the control approach in mitigating the voltage THD level to less than 5 % under various operating conditions that included the main supply voltage and loads, which satisfies the international PQ standards (IEEE Std. 519). The scientific novelty lies in the combination of a new 3-phase SAPF with a PV-battery system by FLC and a cascaded DC/DC converter. This allows effective voltage disturbance and harmonic compensation in various load situations without conventional transformers. Practical value. This research offers a robust solution for power quality problems in modern grids, reducing losses by ensuring stable, no-distortion power for sensitive industrial loads across varied operating conditions. References 46, tables 3, figures 19.

Introduction. This study proposes a finite-time robust control law for position tracking of a DC motor under conditions of model uncertainty and external disturbances. The motor operates through a pulse-width modulation (PWM) unit and an H-bridge power circuit, aiming to achieve finite-time position tracking while minimizing the effects of model uncertainties and external disturbances. Problem. The main challenge lies in achieving accurate and rapid position and speed regulation for the DC motor while maintaining high performance, despite model inaccuracies and external disturbances. The goal of this paper is to design a robust finite-time position tracking control law for a DC motor based on the differential geometric approach, ensuring high tracking accuracy and control efficiency in the presence of disturbances and parameter uncertainties. Scientific novelty. The integration of finite-time control based on a virtual system, diffeomorphism transformation, and disturbance compensation introduces an innovative solution for DC motor position tracking under incomplete modeling and external perturbations. Methodology. The study employs the differential geometric method to construct a virtual system with finite-time characteristics and uses Lyapunov theory to prove global stability in the presence of uncertainties and disturbances. A finite-time virtual system is proposed after analyzing the incomplete dynamic model of the DC motor. Results. To validate the proposed approach, MATLAB simulations were conducted and compared with a conventional sliding mode controller. The results demonstrate improved settling time and robustness of the proposed method in DC motor position tracking. The findings confirm that the proposed controller provides intuitive and precise control, accurate position tracking, and enhanced performance regulation. It also exhibits strong robustness against model uncertainties and external disturbances. The practical value of the proposed method is considerable, as it offers a reliable and efficient position control scheme for DC motors using PWM. The method ensures precise position control and robust performance under varying conditions and external interferences, making it well-suited for real-world DC motor control applications. References 23, tables 1, figures 12.

As the core component in intelligent manufacturing systems, the precise control of the welding clamp’s electrode pressure plays a decisive role in ensuring the quality of spot welding. This paper proposes a novel pressure control strategy for robotic welding clamp based on partitioned adaptive RBF neural networks: (1) Deformation of the clamp body can lead to deviations in workpiece positioning. To address this issue, a deflection compensation method for robot welding clamp based on the PSO-RBF neural network is proposed. By leveraging pre-calibrated empirical data, the intrinsic mapping relationships are identified, and the derived deflection compensation value is integrated into the real-time position command of the robot end-effector. (2) During electrode motion, the system is subjected to external disturbances such as friction and gravitational forces. So, a sliding mode control strategy incorporating adaptive RBF disturbance compensation is proposed to achieve robust speed regulation. Furthermore, the electrode’s reference velocity is dynamically adjusted based on the welding force error and improved admittance control algorithm, enabling indirect regulation of the welding force to reach the desired set value. The results demonstrate that the proposed composite control strategy reduces electrode pressure overshoot to less than 5% and enhances steady-state control accuracy to ±1.5%.

Observer-based nonlinear cascade control approach of rewinding systems with uncertainties and disturbances compensation