Search Results (1,168)

Conventional grid-forming (GFM) inverter control strategies often rely on fixed parameters and overlook the dynamic variation in energy stored in the DC link capacitor. This limitation can degrade transient performance and stability, particularly under power fluctuations and grid disturbances in renewable energy systems. To address this issue, this paper proposes an adaptive GFM control method that integrates real-time estimation of the DC link capacitor energy into the control loop. A Kalman filter-based observer is designed to estimate the capacitor energy state accurately and robustly using only local voltage and current measurements. The estimated energy deviation is then used to dynamically adjust key control parameters, including the virtual inertia and droop coefficients in the virtual synchronous generator (VSG) framework. These adaptive adjustments enhance the inverter’s damping and inertial behavior according to the internal energy buffer, improving performance under variable operating conditions. Simulation results in MATLAB/Simulink R2023b demonstrate that the proposed method significantly reduces power and voltage overshoots, shortens settling time, and improves DC link voltage regulation compared to conventional fixed-parameter control. Full article

Parameter uncertainties and fluctuating disturbances have posed significant challenges to the smooth and precise control of Flexible Joint Robots (FJRs) in industrial environments. To mitigate such disturbances, Disturbance Observers (DOBs) are commonly employed; however, the model uncertainties inherent in FJR systems make accurate dynamic modeling challenging, and the efficacy of DOBs hinges heavily on the accuracy of the dynamic model, which limits their applicability to FJR control. This paper presents a hybrid RBFNN-based Disturbance Observer (RBFNNDOB) state feedback controller for FJRs. By combining a nominal model-based DOB with an RBFNN, this method effectively addresses the unknown dynamics of FJRs while simultaneously compensating for external time-varying disturbances. In this framework, an adaptive neural network weight update law is formulated using Lyapunov stability theory. This enables the RBFNN to selectively estimate the unmodeled uncertainties in FJR dynamics, thereby minimizing computational redundancy in model estimation while allowing dynamic compensation for residual uncertainties beyond the nominal model and DOB estimation errors—ultimately enhancing computational efficiency and achieving robust compensation for rapidly changing disturbances. The boundedness of the tracking error is proven using the Lyapunov approach, and experimental validation is conducted on the FJR system to confirm the efficacy of the proposed control method. Full article

In the context of autonomous vehicles, proper lane merging is critical as it can reduce the traffic bottleneck and lead to safer road transportation. To obtain a collision-free and efficient lane merging, advanced control algorithms need to be designed to smoothly coordinate multiple vehicles to form a platoon. Model predictive control (MPC) is such a controller capable of forecasting future states of multiple vehicles by optimizing their control inputs while satisfying the constraints. Prior MPC-based studies mostly utilized offline planning with a precomputed lookup table of feasible maneuvers to model lane merging. Although these model designs reduce the online computational load, they lack flexibility, as they rely on predefined scenarios and cannot easily adapt to dynamic or unpredictable situations. In this study, we present a centralized MPC framework capable of online trajectory tracking under dynamic constraints and disturbances, for collision-free operation in tightly spaced multi-vehicle platoons. To evaluate the flexibility of our online algorithm, we examine the role of prediction horizon—the time window over which future states are forecasted—and platoon size in determining both the feasibility and efficiency of merging maneuvers. Our results reveal that there exists an optimal prediction horizon at which braking and acceleration can be minimized, thereby reducing energy consumption by 35–40%. Additionally, we observe that increasing the prediction horizon beyond the minimum required for feasibility can alter the vehicle sequence in the platoon. Capturing the changes in vehicle sequence (e.g., who leads or yields) when prediction horizon varies, is a consequence of online trajectory optimization. This vehicle sequence change cannot be captured by offline planning that relies on precomputed look-up table maneuvers. We also found that as the number of vehicles increases, the minimum feasible prediction horizon increases significantly. Full article

To address the problems of random noise interference, inadequate disturbance estimation and compensation, and the difficulty in controller parameter tuning in speed tracking control of high-speed trains, an improved Active Disturbance Rejection Control (ADRC) strategy combined with a Sobol-based Black Widow Optimization (SBWO) algorithm is proposed. An improved Tracking Differentiator (TD) is adopted by integrating a novel optimal control synthesis function with a phase compensator to suppress input noise and ensure a smooth transition process. A novel Extended State Observer (ESO) using a nonlinear saturation function is designed to improve the observation accuracy and decrease chattering. An enhanced Nonlinear State Error Feedback (NLSEF) law that incorporates an error integral and adaptive parameter update laws is developed to reduce steady-state error and achieve self-tuned proportional and derivative gains. A feedforward compensation term is added to provide real-time dynamic compensation for ESO estimation errors. Finally, an enhanced Black Widow Optimization (BWO) algorithm, which initializes its population with Sobol sequences to improve its global search capability, is employed for parameter optimization. The simulation results demonstrate that compared with the control methods based on Proportional–Integral–Derivative (PID) control and conventional ADRC, the proposed strategy achieves higher steady-state tracking accuracy, better adaptability to dynamic operating conditions, stronger anti-disturbance ability, and more precise stopping precision. Full article

In this paper, an adaptive sliding mode fault-tolerant control (FTC) scheme is proposed for small Quadrotor Unmanned Aerial Vehicles (QUAVs) used in education and teaching formation in the presence of systematic unknown external disturbances with actuator failures. A radial basis function neural network (RBFNN) is employed to handle the nonlinear interaction function, and a fault-tolerant-based NN extended state observer (NNESO) is designed to estimate the unknown external disturbance. Meanwhile, an adaptive fault observer is developed to estimate and compensate for the fault parameters of the system. To achieve satisfactory trajectory tracking performance for the QUAV, an adaptive sliding mode control (SMC) strategy is designed. This strategy mitigates the strong coupling effects among the design parameters within the QUAV formation. The stability of the closed-loop system is rigorously demonstrated by Lyapunov analysis, and the controlled QUAV formation can achieve the desired tracking position. Simulation results verify the effectiveness of the proposed control method. Full article

This paper presents a novel control-based observer (CbO) framework for robust state and disturbance estimation in the longitudinal dynamics of fixed-wing aircraft. In this approach, the observer design problem is recast as an equivalent control problem, enabling the use of advanced control techniques for observer synthesis. Within the proposed framework, the estimation of both system states and unknown disturbance inputs is achieved by integrating disturbance rejection capabilities into the control sub-block of the observer. This integration ensures that the output mismatch between the plant and observer model is minimized, even in the presence of modeling uncertainties and external disturbances. Two observer designs are developed: (i) an ${\mathcal{H}}_{\infty }$-CbO, formulated as an ${\mathcal{H}}_{\infty }$ control problem around a linearized model at a nominal operating point, and (ii) a robust ${\mathcal{H}}_{\infty }$-CbO, which extends the design to account for significant model nonlinearities and variations by incorporating multiple operating points and optimizing for the worst-case estimation error. The longitudinal dynamics of a fixed-wing aircraft are derived and linearized to provide the basis for observer design. The performance of the proposed observers is evaluated through comprehensive simulation studies under three scenarios: operation at the nominal point, operation around neighboring points, and comparison with conventional linear observers. Simulation results demonstrate that the proposed observer offers superior robustness and accuracy in estimating both states and external disturbances, particularly in the presence of model uncertainties and varying flight conditions. Full article

In this paper, the concept of symmetry is utilized to design active fault tolerant trajectory-tracking control of autonomous distributed-drive electric vehicles—that is, the construction and the solution of active fault tolerant trajectory-tracking controllers are symmetrical. This paper presents a hierarchical fault tolerant control strategy for improving the trajectory-tracking performance of autonomous distributed-drive electric vehicles (ADDEVs) under steer-by-wire (SBW) system failures. Since ADDEV trajectory dynamics are inherently affected by complex traffic conditions, various driving maneuvers, and other road environments, the main control objective is to deal with the ADDEV trajectory-tracking control challenges of system uncertainties, SBW failures, and external disturbance. First, the differential steering dynamics model incorporating a 3-DOF coupled system and stability criteria based on the phase–plane method is established to characterize autonomous vehicle motion during SBW failures. Then, by integrating cascade active disturbance rejection control (ADRC) with Karush–Kuhn–Tucker (KKT)-based torque allocation, the active fault tolerant control framework of trajectory tracking and lateral stability challenges caused by SBW actuator malfunctions and steering lockup is addressed. The upper-layer ADRC employs an extended state observer (ESO) to estimate and compensate against uncertainties and disturbances, while the lower-layer utilizes KKT conditions to optimize four-wheel torque distribution to compensate for SBW failures. Simulations validate the effectiveness of the controller with serpentine and double-lane-change maneuvers in the co-simulation platform MATLAB/Simulink-Carsim^® (2019). Full article

To address the key issues in cooperative collision avoidance of multiple aircraft, such as unknown dynamics, external disturbances, and limited communication resources, this paper proposes a reference correction method based on the Artificial Potential Field-Control Barrier Function (APF-CBF) and combines it with a dynamic event-triggered mechanism to achieve efficient cooperative control. This paper adopts a Fuzzy Wavelet Neural Network (FWNN) to design a finite-time disturbance observer. By leveraging the advantages of FWNN, which integrates fuzzy logic reasoning and the time-frequency locality of wavelet basis functions, this observer can synchronously estimate system states and unknown disturbances, to ensure the finite-time uniformly ultimate boundedness of errors and break through the limitation of insufficient robustness in traditional observers. Meanwhile, the APF is embedded in the CBF framework. On the one hand, APF is utilized to intuitively describe spatial interaction relationships, thereby reducing reliance on prior knowledge of obstacles; on the other hand, CBF is used to strictly construct safety constraints to overcome the local minimum problem existing in APF. Additionally, the reference correction mechanism is combined to optimize trajectory tracking performance. In addition, this paper introduces a dynamic event-triggered mechanism, which adjusts the triggering threshold by real-time adaptation to error trends and mission phases, realizing “communication on demand”. This mechanism can reduce communication resource consumption by 49.8% to 69.8% while avoiding Zeno behavior. Theoretical analysis and simulation experiments show that the proposed method can ensure the uniformly ultimate boundedness of system states and effectively achieve safe collision avoidance and efficient formation tracking of multiple aircraft. Full article

Leveling of the crane support platform plays a vital role in operational safety and lifting efficiency; it requires both precise horizontal positioning and the rational distribution of outrigger load. However, the current synchronous leveling methods mainly focus on displacement synchronization leveling while neglecting the control of outrigger load, resulting in the problem of individual outrigger overloading. To address this problem, a synchronous leveling control method with variable load constraints (SLCM-VLC) is proposed in this paper based on the framework of model predictive control. Firstly, the proposed method conducts independent outrigger modeling and decoupling of outriggers through adjacent cross-coupling; then a displacement synchronization controller (DSC) is designed to ensure efficient synchronous leveling. Secondly, a collaborative controller of displacement and force (DFCC) under variable load constraints is designed to overcome the limitations of traditional independent optimization. Subsequently, an extended state observer (ESO) is introduced to compensate for environmental disturbances and control deviations. Finally, the effectiveness of the proposed method is verified through a co-simulation using Matlab, Adams, and Solidworks. The results show that, compared with existing leveling control methods, the proposed method can achieve high precision and rapid leveling under smaller peak load, thereby extending the service life of the platform’s electric cylinders. Full article

Unmanned aerial vehicles (UAVs) often face significant challenges in trajectory tracking within complex dynamic environments, where uncertainties, external disturbances, and nonlinear dynamics hinder accurate and stable control. To address this issue, a bio-inspired deep reinforcement learning (DRL) algorithm is proposed, integrating behavior cloning (BC) and long short-term memory (LSTM) networks. This method can achieve autonomous learning of high-precision control policy without establishing an accurate system dynamics model. Motivated by the memory and prediction functions of biological neural systems, an LSTM module is embedded into the policy network of the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. This structure captures temporal state patterns more effectively, enhancing adaptability to trajectory variations and resilience to delays or disturbances. Compared to memoryless networks, the LSTM-based design better replicates biological time-series processing, improving tracking stability and accuracy. In addition, behavior cloning is employed to pre-train the DRL policy using expert demonstrations, mimicking the way animals learn from observation. This biomimetic plausible initialization accelerates convergence by reducing inefficient early-stage exploration. By combining offline imitation with online learning, the TD3-LSTM-BC framework balances expert guidance and adaptive optimization, analogous to innate and experience-based learning in nature. Simulation experimental results confirm the superior robustness and tracking accuracy of the proposed method, demonstrating its potential as a control solution for autonomous UAVs. Full article

A dual-loop fault-tolerant control scheme is investigated for UAV attitude control systems subject to actuator faults, input saturation, and external disturbances in this paper. In the outer loop of attitude angles, a nonlinear dynamic inversion controller is developed as baseline controller for fast response and is augmented by a neural network disturbance observer to enhance the adaptability and robustness. Considering input saturation, actuator faults, and external disturbances in the inner loop of attitude angle velocities, the unbalanced input saturation is first converted into a time-varying system with unknown parameters and disturbances using a nonlinear function approximation method. An L1 adaptive fault-tolerant controller is then introduced to compensate for the effects of lumped uncertainties including system uncertainties, actuator faults, external disturbances, and approximation errors, and the stability and performance boundaries are verified by Lyapunov theorem and L1 reference system. Some simulation examples are carried out to demonstrate its effectiveness. Full article

The strong coupling of the six-degree-of-freedom (6-DoF) electro-hydraulic Stewart parallel mechanism manifests as adjusting the elongation of one actuator potentially inducing motion in multiple degrees of freedom of the platform, i.e., a change in pose; this pose change leads to time-varying and unbalanced load forces (disturbance inputs) on the six hydraulic actuators; unbalanced load forces exacerbate the time-varying nature of the acceleration and velocity of the six hydraulic actuators, causing instantaneous changes in the pressure and flow rate of the electro-hydraulic system, thereby enhancing the pressure–flow nonlinearity of the hydraulic actuators. Considering the advantage of artificial intelligence in learning hidden patterns within complex environments (strong coupling and strong nonlinearity), this paper proposes a reinforcement learning motion control algorithm based on deep deterministic policy gradient (DDPG). Firstly, the static/dynamic coordinate system transformation matrix of the electro-hydraulic Stewart parallel mechanism is established, and the inverse kinematic model and inverse dynamic model are derived. Secondly, a DDPG algorithm framework incorporating an Actor–Critic network structure is constructed, designing the agent’s state observation space, action space, and a position-error-based reward function, while employing experience replay and target network mechanisms to optimize the training process. Finally, a simulation model is built on the MATLAB 2024b platform, applying variable-amplitude variable-frequency sinusoidal input signals to all 6 degrees of freedom for dynamic characteristic analysis and performance evaluation under the strong coupling and strong nonlinear operating conditions of the electro-hydraulic Stewart parallel mechanism; the DDPG agent dynamically adjusts the proportional, integral, and derivative gains of six PID controllers through interactive trial-and-error learning. Simulation results indicate that compared to the traditional PID control algorithm, the DDPG-PID control algorithm significantly improves the tracking accuracy of all six hydraulic cylinders, with the maximum position error reduced by over 40.00%, achieving high-precision tracking control of variable-amplitude variable-frequency trajectories in all 6 degrees of freedom for the electro-hydraulic Stewart parallel mechanism. Full article

This paper addresses the three-dimensional trajectory tracking control problem for the underactuated Autonomous Underwater Vehicle (AUV) operating in complex ocean environments characterized by dynamic disturbances and model uncertainties. A super-twisting extended state observer (STESO) was designed to accurately estimate and compensate for external disturbances and unmodeled dynamics in finite time. A fractional-order proportional–integral–derivative (FOPID) controller was then developed based on the disturbance estimates provided by the STESO. Leveraging the superior frequency-domain tuning flexibility of fractional calculus, the controller enhances tracking precision and robustness against dynamic disturbances. Furthermore, a strict Lyapunov-based stability analysis is presented, and the tracking error converges to zero asymptotically when disturbance estimation errors vanish. Numerical simulations validated the effectiveness and robustness of the proposed control strategy under various disturbance scenarios. Full article

Under complex operating conditions, the single-loop control structure of permanent magnet synchronous motors (PMSMs) suffers from various uncertain disturbances. Although extended state observers with high-gain designs have been widely adopted for disturbance rejection control due to their rapid convergence characteristics, they typically induce significant noise amplification and increased sensitivity to disturbances. To address this issue, this paper proposes a hybrid extended state observer-based control with adaptive switching strategy (AS-HyESO) for suppressing uncertain disturbances. In the AS-HyESO framework, matched disturbances from the control channel and unmatched disturbances from non-control channels are separately estimated using the HyESO, which are subsequently eliminated through the designed control law to ensure precise tracking of the speed reference input. Furthermore, the proposed observer incorporates an adaptive bandwidth switching mechanism that employs larger bandwidth during steady-state operation and reduced bandwidth during dynamic transients. This innovative approach achieves overshoot-free speed regulation while maintaining enhanced disturbance rejection capability, thereby effectively resolving the inherent conflict between dynamic response performance and anti-disturbance robustness. Experimental validation conducted on a 64 W PMSM dual-motor test platform demonstrates the superior effectiveness of the AS-HyESO, control strategy in practical applications. Full article

Electro-hydraulic proportional multi-way valves are widely used in construction machinery and equipment, and their performance directly affects the operation performance of construction machinery and equipment. In this paper, an electro-hydraulic proportional multi-way valve was taken as the research object, and the precise control of the position of the spool of electro-hydraulic proportional multi-way valves under multi-source disturbances was studied. A mathematical model of the electro-hydraulic proportional multi-way valve was established to analyze the influence of hardware controller parameters on the control current. A proportional multi-way valve expansion state observer was established to observe the changes in internal and external multi-source disturbances, and a sliding mode controller was used to compensate for the influence of these disturbances on the position control accuracy of the valve spool of proportional multi-way valves. Compared with the PID control method, the results show that, when using the control method proposed in this paper, the reduction in the step response time and improvement in pressure control accuracy for the pilot proportional reducing pressure valve were maximally 44.4% and 0.61%, respectively; the reduction in the step response time and improvement in control accuracy for the spool of the pilot proportional multi-way valve were maximally 69.48% and 61.74%, respectively. The research results in this paper can help to improve the performance and multi-condition adaptability of electro-hydraulic proportional multi-way valves. Full article