Search Results (86)

The growing need for high-efficiency and reliable propulsion systems in naval applications, particularly within the evolving landscape of submarine warfare, has led to an increased interest in multiphase Permanent Magnet AC motors. This study presents a modelling and simulation approach for a 3MW, seventeen-phase Permanent Magnet AC motor designed for submarine propulsion, integrating an AI-based drive control system. Despite the advantages of multiphase motors, such as higher power density and enhanced fault tolerance, significant challenges remain in achieving precise torque and variable speed, especially for externally mounted motors operating under deep-sea conditions. Existing control strategies often struggle with the inherent nonlinearities, unmodelled dynamics, and extreme environmental variations (e.g., pressure, temperature affecting oil viscosity and motor parameters) characteristic of such demanding deep-sea applications, leading to suboptimal performance and compromised reliability. Addressing this gap, this research investigates advanced control methodologies to enhance the performance of such motors. A MATLAB/Simulink framework was developed to model the motor, whose drive system leverages an AI-optimised dual fuzzy-PID controller refined using the Harmony Search Algorithm. Additionally, a combination of Indirect Field-Oriented Control (IFOC) and Space Vector PWM strategies are implemented to optimise inverter switching sequences for precise output modulation. Simulation results demonstrate significant improvements in torque response and control accuracy, validating the efficacy of the proposed system. The results highlight the role of AI-based propulsion systems in revolutionising submarine manoeuvrability and energy efficiency. In particular, during a test case involving a speed transition from 75 RPM to 900 RPM, the proposed AI-based controller achieves a near-zero overshoot compared to an initial control scheme that exhibits 75.89% overshoot. Full article

The aircraft nose-wheel steering system serves as a critical component for ensuring ground taxiing safety and maneuvering efficiency. However, its dynamic control stability faces significant challenges under complex operational conditions. Existing research predominantly focuses on single-discipline modeling, with insufficient in-depth analysis of the coupling effects between hydraulic system dynamics and mechanical dynamics. Traditional PID controllers exhibit limitations in scenarios involving nonlinear time-varying conditions caused by normal load fluctuations of the landing gear buffer strut during high-speed landing phases, including increased control overshoot and inadequate adaptability to abrupt load variations. These issues severely compromise the stability of high-speed deviation correction and overall aircraft safety. To address these challenges, this study constructs a digital twin model based on real aircraft data and innovatively implements multidisciplinary co-simulation via Simcenter 3D, AMESim 2021.1, and MATLAB R2020a. A fuzzy adaptive PID controller is specifically designed to achieve adaptive adjustment of control parameters. Comparative analysis through co-simulation demonstrates that the proposed mechanical–electrical–hydraulic collaborative control strategy significantly reduces response delay, effectively minimizes control overshoot, and decreases hydraulic pressure-fluctuation amplitude by over 85.2%. This work provides a novel methodology for optimizing steering stability under nonlinear interference scenarios, offering substantial engineering applicability and promotion value. Full article

Hydraulic excavators operate in harsh environments where direct measurement of actuator chamber pressures and boom displacement is often unreliable or infeasible. This study presents a novel control strategy that estimates actuator chamber pressures from work port pressures using differential equations, eliminating the need for direct pressure or position sensors. A fuzzy logic-based proportional–derivative (PD) controller is developed to mitigate boom oscillations, particularly under high-inertia load conditions and variable operator inputs. The controller dynamically adjusts gains through fuzzy logic-based gain scheduling, enhancing adaptability across a wide range of operating conditions. The proposed method addresses the limitations of classical PID controllers, which struggle with the nonlinearities, parameter uncertainties, and instability introduced by counterbalance valves and pressure-compensated proportional valves. Experimental data is used to design fuzzy rules and membership functions, ensuring robust performance. Simulation and full-scale experimental validation demonstrate that the fuzzy PD controller significantly reduces pressure overshoot (by 23% during extension and 32% during retraction) and decreases settling time (by 31.23% and 28%, respectively) compared to conventional systems. Frequency-domain stability analysis confirms exponential stability and improved damping characteristics. The proposed control scheme enhances system reliability and safety, making it ideal for excavators operating in remote or rugged terrains where conventional sensor-based systems may fail. This approach is generalizable and does not require modifications to the existing hydraulic circuit, offering a practical and scalable solution for modern hydraulic machinery. Full article

Traditional coal-fired power units are required to improve their operational flexibility to accommodate increasing renewable energy. In this paper, an optimized operation approach of the cold-end system is proposed to improve the flexibility of coal-fired power units. The dynamic models of the cold-end system of a 330 MW coal-fired power unit are developed. The model validation results show that the error between the simulated results and measured values is <3% at the common load range and <5% at the low load range. The applications of cold-end optimization in the load-variation processes with ±3% Pe/min ramps and actual automatic generation control (AGC) response are then studied. The results show that when the back pressure of the unit is relatively low, the cold-end optimization is more effective in improving the ramp-down rate. On the contrary, when the unit operates with relatively high back pressure, this approach is more suitable for improving the ramp-up rate. Moreover, the AGC response quality is noticeably enhanced, which improves the phenomenon of overshooting and reverse regulation. The comprehensive performance indicator K_P increased from 2.27 to 4.63 in the summer scenario, while it increased from 2.08 to 4.34 in the winter scenario. Moreover, the profits under the two scenarios are raised by 39.2% and 42.5%, respectively. The findings of this study are also applicable to supercritical units or other power units with the cold end adopting similar water cooling systems. Future work will incorporate advanced control theories to enhance control robustness, which is critical for the practical implementation of the proposed cold-end optimization approach. Full article

To advance the construction of intelligent mining, electro-hydraulic digital control technology has emerged as a critical direction for the digital transformation of mining machinery. This study proposes a digital control scheme based on the pressure state of the system and the operating state of the actuator. The scheme utilises a novel convergence rate sliding film position control method to regulate the system pressure in real time by controlling the pilot valve, which is driven by a permanent magnet synchronous motor (PMSM). Moreover, a prototype of an incremental digital pressure control valve was developed for high-pressure, high water-based working conditions. A simulation model of the valve was established using AMESim/Simulink, and dynamic characteristics under various operating conditions were analyzed. The relative error between simulated and experimental pressure results remained within ±4.7%. Finally, a multi-parameter optimization was conducted using a genetic algorithm. The results demonstrate that the optimized digital pressure control valve achieved a stabilized inlet pressure within 44.8 ms, with a pressure overshoot of 4.1% and a response time of 20.1 ms, exhibiting excellent real-time dynamic pressure regulation capabilities. This study provides a theoretical foundation and practical reference for comprehensive research on pressure control in underground emulsion pump stations. Full article

With the enhancement of safety performance requirements in the car manufacturing field, the quality standards for the sheet molding process have imposed higher demands. However, during the pressure-building phase of pump-controlled hydraulic die cushion systems, the combined effects of high-order dynamics, system uncertainties, and strong nonlinearities pose significant challenges to maintaining precise control and dynamic response performance of the blank holder force (BHF). To address these challenges, we propose an intelligent model predictive control (MPC) strategy that synergistically integrates an extended state observer (ESO) for disturbance compensation with parameters optimized by a genetic algorithm (GA). The mathematical model and state-space model of the system are established. Subsequently, the ESO is integrated with MPC to enable active compensation for internal and external disturbances. The GA is employed to optimize the controller parameters within the MPC framework. Finally, a simulation testbed for the pump-controlled hydraulic die cushion experimentally validates the process. Experimental results demonstrate that compared to MPC and conventional PID control, the proposed strategy achieves significant reductions in pressure overshoot (0.87% and 1.8% at 100 bar; 3.3% and 5.9% at 200 bar), pressure-building time (13.9% and 31.4% at 100 bar; 6.7% and 11.5% at 200 bar), and stroke length (10.5% and 32% at 100 bar; 11.5% and 28.1% at 200 bar). This validates its effectiveness in enhancing both control precision and dynamic response performance, providing a reliable solution for large-scale applications of pump-controlled hydraulic die cushions in high-dynamic stamping scenarios. Full article

Aiming to solve the difficult problem of the miniaturization of servo valves, this paper designs a subminiature two-dimensional (2D) electro-hydraulic servo valve, which realizes the integration of the pilot stage and the power stage and significantly improves the work-to-weight ratio. Meanwhile, a high-power-density brushless DC motor (BLDC) is adopted as the electro-mechanical converter to further reduce the volume and mass. Firstly, the structure and working principle of the two-dimensional (2D) servo valve are described, and the mathematical model of the electro-mechanical converter is established. Aiming at the special working condition of the electro-mechanical converter with high-frequency oscillation at a small turning angle, this paper designs a position–current double closed-loop PID control algorithm based on the framework of the vector control algorithm (FOC). At the same time, the current feedforward compensation technique is included to cope with the high-frequency nonlinear disturbance problem of the electro-mechanical converter. The stability conditions of the electro-mechanical converter and the main valve were established based on the Routh–Hurwitz criterion, and the effects of the control algorithm of the electro-mechanical converter and the main parameters of the main valve on the stability of the system were analyzed. The dynamic and static characteristics of the 2D valve were simulated and analyzed by establishing a joint simulation model in Matlab/Simulink and AMESim. The prototype was fabricated, and the experimental bench was built; the size of the experimental prototype was 31.7 mm × 29.3 mm × 31 mm, and its mass was 73 g. Under a system pressure of 7 MPa, the flow rate of this valve was 5 L/min; the hysteresis loop of the spool-displacement input–output curve was 4.8%, and the linearity was 2.54%, which indicated that it had the ability of high-precision control and that it was suitable for the precision fluid system. The step response time was 7.5 ms, with no overshoot; the frequency response amplitude bandwidth was about 90 Hz (−3 dB); the phase bandwidth was about 95 Hz (−90°); and the dynamic characterization experiment showed that it had a fast response characteristic, which can satisfy the demand of high-frequency and high-dynamic working conditions. Full article

The reactor system has multivariate, nonlinear, and strongly coupled dynamic characteristics, which puts high demands on the robustness, real-time demand, and accuracy of the control strategy. Conventional control approaches depend on the mathematical model of the system being controlled, making it challenging to handle the reactor system’s dynamic complexity and uncertainties. This paper proposes a multi-variable coupled control strategy for a nuclear reactor steam supply system based on a Deep Deterministic Policy Gradient reinforcement learning algorithm, designs and trains a multi-variable coupled intelligent controller to simultaneously realize the coordinated control of multiple parameters, such as the reactor power, average coolant temperature, steam pressure, etc., and performs a simulation validation of the control strategy under the typical transient variable load working conditions. Simulation results show that the reinforcement learning control effect is better than the PID control effect under a ±10% FP step variable load condition, a linear variable load condition, and a load dumping condition, and that the reactor power overshooting amount and regulation time, the maximum deviation of the coolant average temperature, the steam pressure, the pressure of pressurizer and relative liquid level, and the regulation time are improved by at least 15.5% compared with the traditional control method. Therefore, this study offers a theoretical framework for utilizing reinforcement learning in the field of nuclear reactor control. Full article

With the continuous development of industrial automation, diesel engines play an increasingly important role in various types of construction machinery and power generation equipment. Improving the dynamic and static performance of the speed control system of single-cylinder diesel engines can not only significantly improve the efficiency of the equipment, but also effectively reduce energy consumption and emissions. Particle swarm optimization (PSO) fuzzy PID control algorithms have been widely used in many complex engineering problems due to their powerful global optimization capability and excellent adaptability. Currently, PSO-based fuzzy PID control research mainly integrates hybrid algorithmic strategies to avoid the local optimum problem, and lacks optimization of the dynamic noise suppression of the input error and the rate of change of the error. This makes the algorithm susceptible to the coupling of the system uncertainty and measurement disturbances during the parameter optimization process, leading to performance degradation. For this reason, this study proposes a new framework based on the synergistic optimization of the untraceable Kalman filter (UKF) and PSO fuzzy PID control for the speed control system of a single-cylinder diesel engine. A PSO-optimized fuzzy PID controller is designed by obtaining accurate speed estimation data using the UKF. The PSO is capable of quickly adjusting the fuzzy PID parameters so as to effectively alleviate the nonlinearity and uncertainty problems during the operation of diesel engines. By establishing a Matlab/Simulink simulation model, the diesel engine speed step response experiments (i.e., startup experiments) and load mutation experiments were carried out, and the measurement noise and process noise were imposed. The simulation results show that the optimized diesel engine speed control system is able to reduce the overshoot by 76%, shorten the regulation time by 58%, and improve the noise reduction by 25% compared with the conventional PID control. Compared with the PSO fuzzy PID control algorithm without UKF noise reduction, the optimized scheme reduces the overshoot by 20%, shortens the regulation time by 48%, and improves the noise reduction effect by 23%. The results show that the PSO fuzzy PID control method with integrated UKF has superior control performance in terms of system stability and accuracy. The algorithm significantly improves the responsiveness and stability of diesel engine speed, achieves better control effect in the optimization of diesel engine speed control, and provides a useful reference for the optimization of other diesel engine control systems. In addition, this study establishes the GT-POWER model of a 168 F single-cylinder diesel engine, and compares the cylinder pressure and fuel consumption under four operating conditions through bench tests to ensure the physical reasonableness of the kinetic input parameters and avoid algorithmic optimization on the distorted front-end model. Full article

The thrust system, an important subsystem of a tunnel boring machine (TBM), primarily provides thrust force and adjusts TBM’s attitude in real time. In the tunneling process, only controlling the thrust speed causes pressure oscillations, increases soil deformation, and leads to surface subsidence or upheaval. Conversely, solely relying on pressure control causes fluctuations in speed, making it difficult to ensure that the deviation between the designed tunneling axis (DTA) and the actual tunneling axis (ATA) remains within the permissible range. Due to the increase in geological complexity and higher construction quality standards, primarily relying on single-mode speed or pressure control has become inadequate to meet operational demands. Therefore, to realize higher safety and precise trajectory tracking, it is necessary to ensure speed and pressure compound control for thrust systems. This paper proposes a novel adaptive sliding mode control (ASMC) strategy for thrust systems, which is composed of a proportional pressure relief valve (PPRV) and a proportional flow control valve (PFCV). Firstly, PPRV and PFCV are modeled as a second-order system and an ASMC is employed to control the pressure and speed. Next, to assess the performance of the ASMC controller, simulation experiments were conducted under various conditions, including speed regulation, sudden changed load, and disturbed load. The simulation results indicate that compared to the Proportion–Integral–Differential (PID) controller, the ASMC controller shows almost no overshoot in speed and pressure control during the initial stages, with the response time reduced by approximately 70%. During speed regulation process and sudden changed load process, the response time for both speed and pressure control is shortened by about 80%. In the disturbed load process, the ASMC controller maintains pressure stability. In conclusion, the ASMC controller significantly improves the response speed and stability of the thrust system, exhibiting better control performance under various operating conditions. Full article

The speed of the engine–propeller directly determines the power output for Unmanned Aerial Vehicles (UAV) with internal combustion engines. However, variable air pressure can impact the engine’s air exchange and combustion processes, causing minor changes that affect the engine speed and result in variations in propeller thrust. A single-loop control strategy was proposed incorporating a feed-forward air-intake model with throttle feedback for small UAVs equipped with a two-stroke scavenging internal combustion engine and propeller. The feed-forward model was built with a simplified model of the airpath based on the scavenging measurement, which combined the tracer gas method and CFD simulation by a two-zone combustion chamber model. The feed-forward control strategy was built by a simplified crankcase–scavenging–cylinder model with CFD results under different air pressures, demonstrating a 1% error compared with CFD simulation. An iterative method of feed-forwarding was suggested for computing efficiency. A feedback controller was constructed using fuzzy PID for minimal instrumentation in engine control for small aircraft. Finally, the single-loop control strategy was validated through simulation and experimentation. The results indicate an 89% reduction in average speed error under varying air pressure and an 83.7% decrease in average speed overshoot in continuous step speed target experiments. Full article

To address significant pressure fluctuations in anchor drilling machine systems during the drilling and anchoring support of complex coal roadway surrounding rock, a variable universe fuzzy PID control strategy was proposed to regulate the speed of the servo motor. Additionally, a pump-controlled pressure approach was introduced to further reduce throttling losses and energy consumption. To validate the proposed algorithm, disturbances were simulated by adjusting the opening size of a three-position four-way proportional directional valve. A co-simulation model was first established using AMESIM 2020.1 and SIMULINK R2022a software, followed by experimental verification and comparison with conventional PID control. The experimental results demonstrated that when the system pressure signal was set to a constant value, the proposed algorithm reduced the pressure error by approximately 36.5% compared to PID control. For step pressure inputs, the algorithm decreased the response time and overshoot by 6.8–8.2% and 10.3–16.2%, respectively, under different valve openings. Furthermore, when the system pressure followed various sinusoidal signals, the proposed algorithm exhibited lower pressure error and faster response times than PID control. This study provides theoretical and experimental references for maintaining stable pressure during the operation of anchor drilling machines, offering an efficient and reliable control solution for complex drilling environments. Full article

In order to deeply absorb the power generation of new energy, coal-fired circulating fluidized bed units are widely required to participate in power grid dispatching. However, the combustion system of the units faces problems such as decreased control performance, strong coupling of controlled signals, and multiple interferences in measurement signals during flexible operation. To this end, this paper proposes a model predictive control (MPC) scheme based on the extended state Kalman filter (ESKF). This scheme optimizes the MPC control framework. The ESKF is used to filter the collected output signals and jointly estimate the state and disturbance quantities in real time, thus promptly establishing a prediction model that reflects the true state of the system. Subsequently, taking the minimum output signal deviation of the main steam pressure and bed temperature and the control signal increment as objectives, a coordinated receding horizon optimization is carried out to obtain the optimal control signal of the control system within each control cycle. Tracking, anti-interference, and robustness experiments were designed to compare the control effects of ESKF-MPC, ID-PI, ID-LADRC, and MPC. The research results show that, when the system parameters had a ±30% perturbation, the adjustment time range of the main steam pressure and bed temperature loops of this method were 770~1600 s and 460~1100 s, respectively, and the ITAE indicator ranges were 0.615 × 10⁵~1.74 × 10⁵ and 3.9 × 10⁶~6.75 × 10⁶, respectively. The overall indicator values were smaller and more concentrated, and the robustness was stronger. In addition, the test results of the actual continuous variable condition process of the unit show that, compared with the PI strategy, after adopting the ESKF-MPC strategy, the overshoot of the main steam pressure loop of the combustion system was small, and the output signal was stable; the fluctuation range of the bed temperature loop was small, and the signal tracking was smooth; the overall control performance of the system was significantly improved. Full article

Drilling fluids are essential for maintaining cutting suspension during drilling, exhibiting gel-like behavior at rest and liquid-like behavior under shearing. These fluids display shear-thinning behavior, yield stress, and thixotropy. This study investigates the impact of aging time on stress overshoot and the deformation required to disrupt the gelled structure of water-based and synthetic-based drilling fluids. Flow start-up tests were conducted using a rotational rheometer at 25 °C and atmospheric pressure. The results show that aging time significantly affects both stress overshoot and the shear strain needed to disrupt the gelled structure. Longer aging times reduce the strain required to break the structure, indicating a correlation between aging time and stress overshoot. The fitted model strongly correlates with the experimental data, providing valuable insights for the planning and simulation of offshore drilling wells, primarily in well stability. Full article

Valve-controlled hydraulic systems are widely used in various hydraulic equipment, but their asymmetric characteristics are the most critical factor restricting further improvements in system performance. This paper takes large asymmetric complex hydraulic equipment as the industrial background, and proposes a state feedback-based symmetric switching control method to address the complex control strategy and difficult control accuracy caused by input–output asymmetry and the inconsistent response of asymmetric valve-controlled hydraulic systems. A system state space model is established, and the parameterized expression that satisfies the state space switching-based symmetric control law is solved. Feedback and feedforward links based on state space symmetric switching are designed to transform the asymmetric system into a state space symmetric system. And the research results will be applied to the experimental setup of the 300 MN forging hydraulic press control system. Through simulation verification, under asymmetric PID control conditions, due to the influence of the asymmetric characteristics of the system structure, load, and their coupling relationship, the forward response time is shorter than the unloaded response time, and the overshoot is larger than the unloaded response time. The reverse response time is longer than the unloaded response time, and the overshoot is smaller than the unloaded response time. After symmetric control, the forward and reverse dynamic system characteristic curves completely overlap, proving that the system has achieved symmetric transformation; through experimental verification, under asymmetric PID control conditions, when the proportional valve opening remains constant, changes in the load pressure will cause changes in the load speed. For every 1 MPa increase in the load pressure, the load speed will slow down by about 0.0033 m/s. The load speed of the system after symmetrical control replacement will be much less affected by changes in the load pressure. The simulation and experimental results have shown that this method is expected to solve the key problem of inconsistent dynamic characteristics of complex equipment hydraulic systems in both the forward and reverse directions due to asymmetry, and the inability to ensure control accuracy in both directions using symmetric control strategies. This paper has developed a set of control theories and methods applicable to hydraulic systems with complex asymmetry. Full article