Search Results (465)

The control stability and accuracy of quad tiltrotor UAVs is improved when encountering external disturbances during automatic flight by an active disturbance rejection control (ADRC) parameter self-tuning control strategy based on a radial basis function (RBF) neural network. Firstly, a nonlinear flight dynamics model of the quad tiltrotor UAV is established based on the approach of component-based mechanistic modeling. Secondly, the effects of internal uncertainties and external disturbances on the model are eliminated, whilst the online adaptive parameter tuning problem for the nonlinear active disturbance rejection controller is addressed. The superior nonlinear function approximation capability of the RBF neural network is then utilized by taking both the control inputs computed by the controller and the system outputs of the quad tiltrotor model as neural network inputs to implement adaptive parameter adjustments for the Extended State Observer (ESO) component responsible for disturbance estimation and the Nonlinear State Error Feedback (NLSEF) control law of the active disturbance rejection controller. Finally, an adaptive attitude control system for the quad tiltrotor UAV is constructed, centered on the ADRC-RBF controller. Subsequently, the efficacy of the attitude control system is validated through simulation, encompassing a range of flight conditions. The simulation results demonstrate that the Integral of Absolute Error (IAE) of the pitch angle response controlled by the ADRC-RBF controller is reduced to 37.4° in comparison to the ADRC controller in the absence of external disturbance in the full-states mode state of the quad tiltrotor UAV, and the oscillation amplitude of the pitch angle response controlled by the ADRC-RBF controller is generally reduced by approximately 50% in comparison to the ADRC controller in the presence of external disturbance. In comparison with the conventional ADRC controller, the proposed ADRC-RBF controller demonstrates superior performance with regard to anti-disturbance capability, adaptability, and tracking accuracy. Full article

In this paper, we propose a hybrid algorithm that integrates an improved artificial potential field method (IAPF), model predictive control (MPC), and an extended state observer (ESO) to address the obstacle avoidance problem in multi-unmanned surface vehicle (Multi-USV) formations, including both dynamic and static obstacles, as well as navigation through narrow waterways. Initially, the virtual structure method was applied for formation control. Next, the traditional potential field method was enhanced by employing a saturated attractive potential field and a partitioned repulsive potential field, which improve formation stability and obstacle avoidance accuracy in complex environments. The extended state observer was then employed to estimate and compensate for unknown system dynamics and external disturbances from the marine environment in real time, improving system robustness. On this basis, by leveraging the multi-step predictive optimization capabilities of model predictive control, the proposed algorithm dynamically adjusts control inputs based on the desired trajectories generated from potential field forces, which ensures the stability of formation control and effective obstacle avoidance. The simulation results demonstrate that the proposed algorithm effectively avoids both dynamic and static obstacles in multi-unmanned surface vehicle formations and enables successful navigation through narrow waterways by altering the formation. Full article

An electro-hydraulic servo loading system for a pavement mechanical properties test device was designed. The simulation analysis and test results showed that the PID control met the design requirements, but the output’s maximum error did not. Therefore, a fast terminal sliding mode control strategy with an extended state observer (ESO) was proposed. A tracking differentiator was constructed to obtain smooth differential signals from the input signals. The order of the system was reduced by considering the third and higher orders of the system as the total disturbance, and the states and the total disturbance of the system were estimated using the ESO. The fast terminal sliding mode control achieved fast convergence of the system within a limited time. The simulation results showed that the proposed control strategy improved the system accuracy and anti-disturbance ability, and system control performance was optimized. Full article

The widespread use of bacteria in bioremediation led us to consider what Compulsory Secondary Education (ESO) and Baccalaureate students know about prokaryotes and what their attitudes towards them are. This study focuses on the analysis of the arguments made by secondary school students from several Spanish schools regarding the application of bioremediation to eliminate polluting plastics from the environment. Semi-structured interviews were used to obtain information on descriptive aspects regarding the application of biotechnology to bioremediation. This instrument allows for a better understanding of what students know about biotechnology and what they value when adopting a particular attitude towards improving the environment through systematic observations and recording spontaneously occurring events. The arguments used by these students were analyzed from the perspective of the knowledge and values they consider when making their justifications. It was observed that the students viewed the use of microorganisms to treat waste positively and valued the environmental impact and scientific progress, although they had doubts about certain technical aspects. A teaching approach based on the biodegradation of plastics encourages critical thinking and can be integrated transversally into teaching, promoting debates and reflections on science and values. It is recommended that these types of activities continue to be developed to improve science education. Full article

As the integration of renewable energy sources into microgrid operations deepens, their inherent uncertainty poses significant challenges for dispatch scheduling. This paper proposes a hybrid game-theoretic optimization strategy to address the uncertainty of renewable energy in microgrid scheduling. An energy trading framework is developed, involving integrated energy microgrids (IEMS), shared energy storage operators (ESOS), and user aggregators (UAS). A mixed game model combining master–slave and cooperative game theory is constructed in which the ESO acts as the leader by setting electricity prices to maximize its own profit, while guiding the IEMs and UAs—as followers—to optimize their respective operations. Cooperative decisions within the IEM coalition are coordinated using Nash bargaining theory. To enhance the generality of the user aggregator model, both electric vehicle (EV) users and demand response (DR) users are considered. Additionally, the model incorporates renewable energy output uncertainty through distributionally robust chance constraints (DRCCs). The resulting two-level optimization problem is solved using Karush–Kuhn–Tucker (KKT) conditions and the Alternating Direction Method of Multipliers (ADMM). Simulation results verify the effectiveness and robustness of the proposed model in enhancing operational efficiency under conditions of uncertainty. Full article

The traditional deadbeat predictive current control (DPCC) strategies for a permanent magnet synchronous motor (PMSM), such as those based on an extended state observer (ESO) and quasi-resonant extended state observer (QRESO), usually require large observer bandwidth, rendering the system sensitive to noise. To address this issue, this paper proposes a cascaded quasi-resonant extended state observer-based DPCC (CQRESO-based DPCC) strategy. Specifically, the CQRESO is utilized to estimate the predicted values of d-axis and q-axis currents, as well as the system total disturbance caused by the deterministic and uncertain factors at time instant k + 1. Subsequently, the required control command voltage at time instant k + 1 is then calculated according to the deadbeat control principle. Finally, the comparative simulation results with ESO-based DPCC and QRESO-based DPCC strategies demonstrate that the proposed strategy can achieve dynamic and robust performance comparable to the ESO-based and QRESO-based DPCC strategies while utilizing a smaller observer bandwidth. Additionally, it exhibits superior steady-state performance and 5th and 7th harmonic current suppression capabilities (in the abc reference frame). Full article

This paper presents a model predictive control (MPC)-enabled disturbance-rejection controller approach for a pneumatic manipulator system subjected to complex nonlinear terms within the system. To facilitate the handling of the complex nonlinear terms, they are modeled as disturbances. To address these disturbances, a receding-horizon-based extended state observer (RH-ESO) incorporating a decision variable is developed. The optimal disturbance estimation error is determined through a receding-horizon optimization procedure, which provides the best estimate of the disturbance. Using this optimal estimate, the MPC-enabled disturbance-rejection controller is proposed for the pneumatic manipulator system to achieve angle tracking control. Moreover, the proposed approach ensures both the recursive feasibility of the optimization problem and the uniform boundedness of the closed-loop system. The simulation results further demonstrate the effectiveness and validity of the proposed methodology. Full article

In this paper, we tackle the decentralized-output feedback sampled-data disturbance rejection control for a dual-drive H-Gantry (DDHG) system with a symmetrical structure. For the DDHG system with disturbances, only the position information of the system at the sampling points can be utilized, such that the traditional control methods based on full state information of the DDHG system could not be used. To this end, a linear discrete-time generalized-proportional-integral observer (GPIO) based on the position information and reference trajectory of DDHG at the sampling points is constructed first, such that unmeasured states and disturbance can be estimated simultaneously. Then, a GPIO-based decentralized-output feedback sampled-data control (GPIO-DOFC) method is proposed by utilizing the estimations of the unmeasured states and disturbance. A strict theoretical analysis of the closed-loop system is carried out, which demonstrates that the desired trajectory could be tracked under the proposed GPIO-DOFC method. Finally, comparative studies are carried out between the proposed GPIO-DOFC method and the extended-state observer-based decentralized-output feedback sampled-data control (ESO-DOFC) method. These confirm the efficacy and feasibility of the proposed control scheme. Full article

This study introduces a rigid–flexible coupled constant force actuator integrated with Active Disturbance Rejection Control (ADRC) to tackle the rigidity–compliance trade-off in precision force-sensitive applications. The actuator utilizes compliant hinges to decrease contact stiffness by three orders of magnitude (${10}^6\to {10}^3 \mathrm{N}/\mathrm{m}$), facilitating effective force management through millimeter-scale placement (0.1∼1 mm) and inherently mitigating high-frequency disturbances. The ADRC framework, augmented by an Extended State Observer (ESO), dynamically assesses and compensates for internal nonlinearities (such as friction hysteresis) and external disturbances without necessitating accurate system models. Experimental results indicate enhanced performance compared to PID control: under dynamic disturbances, force deviations are limited to $\pm 0.2 \mathrm{N}$ with a 98.5% reduction in mean absolute error, a 96.3% increase in settling speed, and 99% suppression of oscillations. The co-design of mechanical compliance with model-free control addresses the constraints of traditional high-stiffness systems, providing a scalable solution for industrial robots, compliant material processing, and medical device operations. Validation of the prototype under sinusoidal perturbations demonstrates reliable force regulation (settling time $<0.56 \mathrm{s}$, errors $<0.5 \mathrm{N}$), underscoring its relevance in dynamic situations. This study integrates theoretical innovation with experimental precision, enhancing intelligent manufacturing systems via adaptive control and structural synergy. Full article

In this study, a novel semi-liquid gel material based on bisphenol A-type epoxy resin (E51), methylhexahydrophthalic anhydride (MHHPA), and epoxidized soybean oil (ESO) was developed for high-performance wellbore sealing. The gel system exhibits tunable gelation times ranging from 1 to 10 h (±0.5 h) and maintains a low viscosity of <100 ± 2 mPa·s at 25 °C, enabling efficient injection into the wellbore. The optimized formulation achieved a compressive strength exceeding 112.5 ± 3.1 MPa and a breakthrough pressure gradient of over 50 ± 2.8 MPa/m with only 0.9 PV dosage. Fourier transform infrared spectroscopy (FTIR) confirmed the formation of a dense, crosslinked polyester network. Interfacial adhesion was significantly enhanced by the incorporation of 0.25 wt% octadecyltrichlorosilane (OTS), yielding an adhesion layer thickness of 391.6 ± 12.7 nm—approximately 9.89 times higher than that of the unmodified system. Complete degradation was achieved within 48 ± 2 h at 120 °C using a γ-valerolactone and p-toluenesulfonic acid solution. These results demonstrate the material’s potential as a high-strength, injectable, and degradable sealing solution for complex subsurface environments. Full article

This study proposes an event-triggered fault-tolerant active disturbance rejection control (ADRC) method for variable-load quadrotors with prescribed performance. The quadrotor, as a nonlinear and underactuated system, faces challenges such as payload variations, actuator faults, and external disturbances, which degrade trajectory tracking accuracy and stability. The proposed approach integrates a cascaded ADRC framework, decoupling the system into position and velocity subsystems, each equipped with extended state observers (ESOs) for real-time disturbance estimation and compensation. To enhance robustness, prescribed performance functions dynamically constrain tracking errors within predefined bounds, while event-triggered mechanisms reduce computational load through condition-based updates of control signals. Additionally, a particle swarm optimization (PSO) algorithm is employed for online parameter tuning, improving adaptability. Theoretical analysis confirms the system stability, and simulation results demonstrate the controller effectiveness in handling actuator faults and variable payloads, ensuring accurate trajectory tracking and reduced resource consumption. The method offers a promising solution for robust and efficient quadrotor control in complex environments. Full article

Precision spraying technology has attracted increasing attention in orchard production management. Traditional chemical pesticide application relies on subjective judgment, leading to fluctuations in pesticide usage, low application efficiency, and environmental pollution. This study proposes a machine vision-based precision spraying control system for orchards. First, a canopy leaf wall area calculation method was developed based on a multi-iteration GrabCut image segmentation algorithm, and a spray volume calculation model was established. Next, a fuzzy adaptive control algorithm based on an extended state observer (ESO) was proposed, along with the design of flow and pressure controllers. Finally, the precision spraying system’s performance tests were conducted in laboratory and field environments. The indoor experiments consisted of three test sets, each involving six citrus trees, totaling eighteen trees arranged in two staggered rows, with an interrow spacing of 3.4 m and an intra-row spacing of 2.5 m; the nozzle was positioned approximately 1.3 m from the canopy surface. Similarly, the field experiments included three test sets, each selecting eight citrus trees, totaling twenty-four trees, with an average height of approximately 1.5 m and a row spacing of 3 m, representing a typical orchard environment for performance validation. Experimental results demonstrated that the system reduced spray volume by 59.73% compared to continuous spraying, by 30.24% compared to PID control, and by 19.19% compared to traditional fuzzy control; meanwhile, the pesticide utilization efficiency increased by 61.42%, 26.8%, and 19.54%, respectively. The findings of this study provide a novel technical approach to improving agricultural production efficiency, enhancing fruit quality, reducing pesticide use, and promoting environmental protection, demonstrating significant application value. Full article

Positioning servo systems utilizing permanent magnet synchronous linear motors (PMSLMs) are conventionally governed by cascaded P-PI controllers, which, despite their simplicity and robustness, suffer from limited tracking and anti-disturbance performance due to their single-degree-of-freedom (1-DOF) structure. This paper introduces a novel two-degree-of-freedom (2-DOF) control algorithm that integrates explicit model predictive control (EMPC) with a differential-compensated extended state observer (DCESO). The EMPC framework leverages position and velocity as state variables, eliminating the need for integral terms and thereby enhancing dynamic response. By employing an offline optimization approach, a control law is explicitly formulated to handle system constraints while minimizing online computational overhead. Additionally, a velocity feedforward term derived from the MPC framework is incorporated to further reduce tracking errors. To bolster disturbance rejection, the proposed DCESO introduces a differential compensator that mitigates the low-pass effects inherent in traditional ESOs, thereby improving estimation dynamics. Experimental results demonstrate that the proposed method significantly outperforms the conventional P-PI controller, increasing the position loop bandwidth from 147 Hz to 208 Hz and markedly enhancing anti-disturbance performance. The algorithm’s low online computational demand makes it highly suitable for industrial applications. Full article

In view of the difficulties encountered when tuning parameters and the lack of anti-interference capabilities exhibited by high-precision trajectory-tracking control of quadrotor UAVs in complex dynamic environments, this paper proposes a fusion control framework based on an improved crowned pig optimisation algorithm (ICPO) and preset performance anti-disturbance control (PPC-ADRC). Initially, this paper addresses the limited convergence efficiency of the traditional crowned pig algorithm (CPO) by introducing a dynamic time threshold mechanism and an adaptability-based directed elimination strategy to balance the algorithm’s global exploration and local development capabilities. This results in a significant improvement in the convergence speed and optimisation accuracy. Secondly, a hierarchical control architecture is designed, with the outer loop using a PPC-ADRC controller to dynamically constrain the tracking error boundary using an exponential performance funnel function and a combined state observer (ESO) to estimate the compound disturbance in real time. The inner-loop attitude control uses ADRC, and the 24-dimensional parameters of the ADRC (including the ESO bandwidth and non-linear feedback gain) are optimised autonomously using the ICPO to achieve efficient parameter tuning. The simulation experiments demonstrate that, in comparison with the original CPO, the ICPO attains an average fitness ranking that is superior in the CEC2014–2022 benchmark test, thereby substantiating its global optimisation capability. In the PPC-ADRC controller parameter optimisation, the preset performance of the ICPO-tuned PPC-ADRC controller (PPC-ADRC) is superior to that of the particle swarm optimisation (PSO), genetic algorithm (GA) and original CPO. The ICPO-based PPC-ADRC controller is shown to reduce the total error by more than 45.6% compared to the ordinary ADRC controller in the task of tracking a spiral trajectory, and it effectively reduces the overshoot. Its capacity to withstand complex wind disturbances is notably superior to that of the traditional PID and ADRC architectures. Stability analysis further proves that the system satisfies the Lyapunov convergence condition in a finite time. This research provides a theoretical foundation for the high-precision control of UAVs in complex dynamic environments. Full article

Achieving excellent speed control in permanent magnet synchronous motors (PMSMs) relies on the simultaneous suppression of both aperiodic and periodic disturbances. This paper presents an enhanced Active Disturbance Rejection Control (ADRC) strategy specifically designed to address these disturbances in single-rotor compressors (SRCs). To achieve simultaneous suppression, a Recursive Gauss–Newton (RGN) algorithm is implemented in parallel with the conventional extended state observer (ESO) to enhance the ADRC framework. The RGN algorithm iteratively estimates the amplitude and phase information of periodic disturbances, while the ESO primarily observes the system’s aperiodic disturbances. In contrast to existing methods, the proposed angle-based approach demonstrates superior performance during speed transients. Detailed convergence and decoupling analyses are provided to facilitate parameter tuning. The effectiveness of the proposed method is validated through simulations and experiments conducted on a 650 W SRC, demonstrating its superiority over proportional–integral (PI) control, conventional ADRC, and quasi-resonant controller-based ADRC (QRC-ADRC) under both steady-state and dynamic conditions. Full article