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The closed-loop constant pressure drop control valve is widely used in aero-engine fuel servo metering systems. However, the available constant pressure drop control valve cannot realize servo tracking without static error and, often, a high proportional gain is used to reduce the static error and improve the servo tracking performance, which reduces the stability margin. In this paper, an integral constant pressure drop control valve is designed, which consists of an integral controller and a stabilizing controller. Moreover, a robust LQR design method is proposed to complete the design task. Firstly, the controlled plant’s state–space model is derived, and the augmented model with tracking error is established based on the robust servo system design theory. Secondly, a servo controller with dual functions of integral control and stabilization control is constructed and decoupled, in which the stabilizing controller guarantees the asymptotic stability as well as the anti-disturbance performance, and the integral controller realizes the servo tracking without static error. Finally, based on the robust LQR design method, two key design parameters, including the integral control gain and the stabilization control gain, are designed to complete the design task. The simulation results indicate that, even when suffering 50 mm2 metered flow area step disturbance and 1 MPa inlet pressure step change, the designed integral constant pressure drop control valve can realize the function of servo tracking without static error. The static error is almost 0, the settling time is within 0.01 s, the overshoot is within 10%, and the phase margin is more than 55°.

A dynamic analysis of a compressor system is presented to characterize its behavior and establish a mathematical framework for identifying stable and unstable operating regions. The study is grounded in the nonlinear Moore–Greitzer model, which describes compressor dynamics in terms of mass flow and pressure rise as functions of rotor speed. To predict the onset of surge and system instability, advanced nonlinear techniques are employed, including the Jacobian matrix, linear parameter-varying (LPV) modeling, Bendixson’s criterion, and phase plane analysis. These tools enable the identification of both stable and unstable regions, as well as the limit cycle associated with surge phenomena. All of these analyses of the compressor are innovative. Accurate prediction of compressor surge and instability is essential for defining and designing effective control strategies, as surge can damage the compressor, interrupt downstream flow, and inherently represents an unstable operating condition. However, analysis alone is insufficient for practical compressor operation. Therefore, three active control methods are considered: Proportional–Integral–Derivative (PID), Linear Quadratic Regulator (LQR), and Model Predictive Control (MPC). The comparative analysis reveals that insufficient consideration of varying system conditions in LQR design may lead to inferior performance relative to MPC and PID control, particularly under changing disturbances. In contrast, MPC and PID exhibit stronger robustness to disturbance variations and provide effective disturbance rejection. In the proposed approach, MPC simulations are conducted to evaluate controller performance. Due to disturbances in the closed-loop model, the LQR controller demonstrates reduced robustness compared to PID and MPC. Under surge-related disturbances, the minimum input mass flow by both PID and MPC controllers is 0.495 (very close to setpoint), and both controllers exhibit an overshoot of 33% and a rise time of 3 s.

A cascade technique with two control loops is designed for a DC Buck‐Boost converter that is a right half‐plane zero (RHPZ) structure called a non‐minimum phase system. This concept presents several challenging constraints for designing well‐behaved control techniques. Cascade controllers can provide various benefits compared with single loop controllers such as higher safety, higher robustness, and higher stability. This strategy assumes the system as a black‐box structure without the need for a mathematical model of the system. This benefit can decrease the computational burden and provides faster dynamics along with ease of implementation. This technique consisted of an outer Fractional‐order PID voltage controller tuned with the Antlion Optimizer (ALO) algorithm, which provides a reference current for the inner control loop of the Neural Network‐based LQR (NN‐LQR) controller. The basic principle in cascade controllers is a more rapid performance of the inner loop that has been satisfied with the NN‐LQR strategy, which optimizes and tunes the gains of the LQR controller and shows faster dynamics and higher robustness. It should be mentioned that the number of neurons is limited to 2 and 4 in each layer to decrease the computational burden with lower complexity. Also, the ALO algorithm is a modern nature‐inspired algorithm used to tune the PID gains with better results under‐constrained problems with diverse search spaces. Considering the negative impacts of various disturbances on a power converter, a Fractional‐order‐based PID (FO‐PID) control technique is a proper alternative since it shows higher robustness in load uncertainties along with better dynamical responses based on its extra degree of freedom. Moreover, to evaluate the superiority of this controller, two other controllers are designed using the PSO algorithm for PID and FO‐PID controllers. Finally, the presented cascade controller has been tested in various working conditions through simulation and experiment results. A cascade technique with two control loops for a DC/DC Buck‐Boost converter is presented here. The proposed Buck‐Boost converter is a topology working in both step‐down and step‐up modes that is a non‐minimum system in its boost mode.

To improve the dynamic property and the disturbance suppression ability of an electro-optical tracking system, this paper presents a disturbance-observer-based LQR tracking control method. The disturbance-observer-based robust controller is composed of three parts: one is the LQR tracking controller, one is the reference model controller and the other is a compensatory controller designed with the output of the disturbance observer. The uncertainty and disturbances are considered in the controller design. By Lyapunov stability theory and linear matrix inequality (LMI) technique, the sufficient conditions for observer gain and controller gain of the tracking reference model of the electro-optical system are given. Simulation and experimental results show that the proposed method in this paper not only improved the disturbance suppression ability of the electro-optical tracking system but also improved the dynamic property of the electro-optical tracking system, such as rise time, settling time and system overshoot. Specially, compared with other methods in this paper, the tracking accuracy and the disturbance suppression ability of the proposed method are about two to three times higher. The method presented in this paper has important reference value in the field of electro-optical system applications. But, with the development of electro-optical system applications, the tracking accuracy and disturbance suppression ability of the proposed method cannot meet the actual requirements of an electro-optical system. The next step of this paper will consider a variety of practical requirements, such as the controller saturation problem and tracking reference target with strong maneuverability, and further optimize the proposed method.

A skid-to-turn (STT) missile is an axisymmetric structure missile, and its control system consists of a pitch channel and a yaw channel with an axisymmetric structure. To achieve the fast response of the STT missile system, a compound control method of aerodynamic force and lateral thrust based on regional pole assignment (RPA) is proposed. In the aerodynamic control system, the linear quadratic regulator (LQR) is used to design the controller, and the sliding mode control method is used to design the controller of the lateral thrust system. The regional pole assignment is introduced into the aerodynamic system to improve the compound control system response speed. The problems of regional pole assignment and system stability are solved by a linear matrix inequality (LMI). Considering that the missile flies at different altitudes, the missile system is controlled by gain scheduling. Compared to previous designs of time-varying compound control systems for STT missiles or hypersonic vehicles, in order to meet the practical requirement of a fast response for the vehicle, this time-varying compound control strategy can achieve faster tracking response and attitude control for the STT missile. Finally, through simulations of the pitch channel and yaw channel control systems of the STT missile, the effectiveness of the designed compound control system in achieving a fast response is verified.

This paper presents the design and analysis of a hybrid Model Reference Adaptive Controller combined with a Linear Quadratic Regulator (MRAC–LQR) for a quadrotor unmanned aerial vehicle (UAV), addressing challenges posed by nonlinear dynamics, underactuated configurations, and sensitivity to external disturbances. A baseline MRAC scheme is first developed to ensure stable tracking under varying payloads and wind disturbances. The proposed cascaded hybrid MRAC–LQR framework incorporates integral action to improve steady-state accuracy while preserving the original adaptive update laws. Performance is compared to the existing parallel MRAC–LQR and MRAC–PID control schemes. Simulation results on a nonlinear quadrotor model demonstrate that MRAC–LQR significantly enhances tracking accuracy and disturbance rejection. While MRAC–PID achieves slightly lower tracking error at the cost of higher control effort, MRAC–LQR offers smoother transients and greater control efficiency.

The stabilisation and control mechanisms of an Unmanned Aerial System (UAS) must be properly designed to ensure acceptable flight performance. During their operation, these mechanisms are subjected to unknown and random environmental effects, making it imperative that all available information should be taken into consideration during the mechanisms’ design process (e.g. system dynamics, actuators, flight conditions and certain criteria requirements such as phugoid and short modes for longitudinal dynamics, and roll subsidence, spiral and Dutch-roll modes for lateral dynamics) in order to guarantee flight stability. Therefore, this paper introduces a novel methodology for the stabilisation and control of the UAS-S45 Bálaam, designed and manufactured by Hydra Technologies. This methodology uses composite controllers that combine feedback Linear Quadratic Regulators (LQR) and Proportional Integral Feed-Forward (PI-FF) compensation controller for stabilisation and tracking tasks, respectively. Furthermore, a Generalised Extended State Observer was implemented to provide robustness to the closed loop dynamics by introducing disturbance compensation. Furthermore, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was adopted to perform a gain scheduling by computing the gains of each composite controller for certain unknown trim conditions within a given flight domain. Finally, several numerical assessments were performed to highlight the efficiency of the proposed methodology.

Wheel‐legged robots offer high mobility on flat terrain and adaptability in complex environments, yet achieving stable motion under uncertain ground conditions and during height variations poses significant control challenges. This study presents a control framework for a bipedal wheel‐legged robot (BWLR) to enable stable locomotion and posture transitions on uneven and inclined terrains. An adaptive Fuzzy‐linear quadratic regulator (Fuzzy‐LQR) controller is presented, where a fuzzy supervisory layer dynamically tunes the LQR gains and estimates the center of mass (CoM) using intermediate postures. Additionally, a proportional‐derivative (PD)‐based hip stabilization controller is integrated to regulate the roll angle via coordinated hip joint actuation, enhancing balance on uneven surfaces. The framework is implemented in a Robot Operating System (ROS)2‐based architecture with torque‐level joint control. Its effectiveness is validated through high‐fidelity simulations in ROS2–Gazebo and real‐world experiments on a physical BWLR prototype, including detailed aspects of multiplugin integration and torque‐feedback hip control. Results demonstrate robust height adaptation during locomotion and stable balance on inclined terrain, highlighting the adaptability of the combined Fuzzy‐LQR and PD approach for wheel‐legged robotic systems.

Quad-tilt-wing (QTW) Unpiloted Aerial Vehicles (UAVs) combine the vertical takeoff and landing capabilities of rotary-wing designs with the high-speed, long-range performance of fixed-wing aircraft, offering significant advantages in both civil and military applications. The unique configuration of QTW UAVs presents complex control challenges due to nonlinear dynamics, strong coupling between translational and rotational motions, and significant variations in aerodynamic characteristics during transitions between flight modes. To address these challenges, this study develops an optimal control framework tailored for real-time operations. A State-Dependent Riccati Equation (SDRE) approach is employed for attitude control, addressing nonlinearities, while a Linear Quadratic Regulator (LQR) is used for position and velocity control to achieve robustness and optimal performance. By integrating these strategies and utilizing the inverse dynamics approach, the proposed control system ensures stable and efficient operation. This work provides a solution to the optimal control complexities of QTW UAVs, advancing their applicability in demanding and dynamic operational environments.

This paper introduces the concept of intelligent control using Tikhonov regularization for nonlinear coupled systems. This research is driven by the increasing demand for advanced control techniques and aims to explore the impact of Tikhonov regularization on these systems. The primary objective is to determine the optimal regularization term and its integration with other control methods to enhance intelligent control for nonlinear coupled systems. Tikhonov regularization is a technique employed to adjust neural network weights and prevent overfitting. Additionally, the incorporation of ReLU activation function in the neural network simplifies thearchitecture, avoiding issues like gradient explosion, and optimizes controller performance. Furthermore, sliding surfaces are designed to improve control system stability and robustness. The proposed Tikhonov-tuned sliding neural network (TSN) controller ensures both stability and superior system performance. The methodology emphasizes the importance of determining optimal neural network weights and regularization terms to prevent overfitting, facilitating accurate predictions in inverted pendulum control system applications. To assess the functionality and stability of TSN, this paper employs simulations and experimental implementations to control both the rotary inverted pendulum and the arm-driven inverted pendulum. The results indicate that the proposed TSN methodologies are effective and feasible.