Search Results (30)

This paper addresses the analysis and control of continuous-time linear switched systems within the linear time-varying (LTV) framework. A correspondence between switched and LTV system representations is first established, enabling the use of LTV tools for stability and controllability analysis under arbitrary switching sequences. Two state-feedback synthesis approaches are considered. The first is a switched LQR design formulated via Linear Matrix Inequalities using Multiple Lyapunov Functions. The second is a continuous-time LTV-LQR obtained by solving the differential Riccati equation. To mitigate the discontinuities associated with hard switching, a matrix smoothing strategy based on the partition of unity is introduced. This approach generates continuous approximations of the plant matrices utilized exclusively for control synthesis in the time-varying Riccati framework, while the actual plant retains its hard-switched dynamics. Numerical simulations for representative switched systems illustrate that the proposed smoothed LTV formulation leads to improved closed-loop behavior compared to the switched LQR baseline, including reduced control discontinuities and lower values of the quadratic performance index for the considered scenarios. The results indicate that the method is suitable for applications where abrupt control variations are undesirable. Full article

Continuous shared control provides an effective approach for intelligent vehicles to balance driving autonomy and system safety boundaries in complex human–machine interaction scenarios. However, existing shared control methods fail to dynamically adapt to the complex and time-varying states of the driver. To address this limitation, a hierarchical shared steering control strategy based on driver states is proposed in this paper. First, an in-vehicle eye tracker is utilized to collect data, and recognition features are extracted based on real-world datasets. Subsequently, a CNN-TCN deep learning algorithm is employed to train a model for identifying five-dimensional driver states. To mitigate excessive intervention and driving experience degradation caused by model misclassifications, a total probability weighting mechanism is developed. This mechanism integrates the real-time confidence distribution output by the neural network with the established baseline safety weights for each driving state, enabling the dynamic and continuous computation of the initial machine control authority. Furthermore, to eliminate high-frequency confidence spikes at the state perception end, a weight-smoothing strategy is designed using an adaptive nonlinear tracking differentiator based on Active Disturbance Rejection Control (ADRC). An autonomous driving controller is then constructed using the Linear Quadratic Regulator (LQR) method to ensure vehicle stability. Finally, Hardware-in-the-Loop (HIL) experiments conducted on a human–machine driving platform with hardware feedback verify the feasibility and superiority of the proposed method. Full article

In scrubbing tower (ST) systems, outlet vapor temperature regulation is subject to strict thermal constraints, requiring the bottom temperature to remain below a critical safety threshold. Transient overshoots can rapidly trigger pyrolytic coking, foul mass-transfer packed beds and spray nozzles, and ultimately force complete production shutdowns. Due to feedstock compositional drifts, high thermal inertia, and significant transport delays, high-fidelity predictive identification is essential for proactive early warning and for overcoming the limitations of reactive feedback control. To address these bottlenecks, this paper introduces an offset-free, hard-constrained, data-driven adaptive optimal control paradigm, designated as the improved GRU-coupled conjugate gradient linear quadratic regulator (IGRUCG-LQR). First, by constructing an augmented state space embedded with integral error, the proposed paradigm eliminates permanent tracking offsets induced by long-term nonstationary drifts. Second, automatic differentiation is used to extract the time-varying Jacobian matrix of a gated recurrent unit (GRU) online, thereby tracking the nonlinear evolution of the underlying thermodynamic baseline with high fidelity. To manage the critical trade-off between strict actuator saturation and short real-time sampling intervals, the conjugate gradient (CG) method is fused with a hard-boundary projection operator, enforcing physical constraints with high computational efficiency and without complex matrix inversions. Experimental validation on a real-world industrial dataset demonstrates that the proposed paradigm secures the safety baseline while achieving high-resolution transient tracking. Furthermore, it significantly suppresses high-frequency valve chattering to mitigate mechanical fatigue, establishing a solid theoretical and engineering foundation for the prolonged stable operation of safety-critical processes. The proposed framework achieves an Integral Absolute Error (IAE) of 86.66 and an Integral Time Absolute Error (ITAE) of 4064.49. Full article

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. Full article

Foldable quadcopters represent a new frontier in aerial robotics technology. The ability of a foldable quadcopter to reconfigure its geometry in flight and adapt to various flight scenarios enhances agility, maneuverability, aerodynamic efficiency, and mission versatility compared to a traditional quadcopter. However, the morphing function introduces significant variations in parameters such as center of gravity, inertia, and nonlinear dynamics, in addition to inherent underactuation, coupling dynamics, and external disturbances. Thus, the folding mechanism presents significant challenges to conventional control approaches. To solve the drawbacks of the conventional control approach, nonlinear control methods have been investigated. This article proposed the development of a prescribed performance-adaptive sliding-mode control for a foldable quadcopter UAV. It models the morphing quadcopter as a rigid body system with five morphing formations (X, Y, H, O, and T). The prescribed performance sliding mode control approach systematically addresses the time-varying parameter and aerodynamic properties impact resulting from the morphing formation. Using Lyapunov theory, a sliding mode controller is designed that ensures the error evolution remains within prescribed performance bounds, maintains closed-loop stability, and tracks the trajectory under uncertainties. The effectiveness of the proposed control algorithm is evaluated and benchmarked in structured and unstructured trajectories against conventional nonlinear sliding mode control (SMC), PID, and LQR control methods. The simulation results indicate that the prescribed performance adaptive SMC achieves better performance and improved robustness compared to benchmarked control methods. The simulation results demonstrated that the adaptive control approach is a viable and effective solution for managing the complex dynamics of foldable quadcopters UAV. Full article

The increasing penetration of rooftop photovoltaic (RTPV) systems in low-voltage (LV) distribution networks introduces challenges such as voltage rises, reverse power flow, and reduced hosting capacity, thereby necessitating effective active power regulation (APR) in module-level micro-inverters. This paper proposes a dual-layer control framework for a 250 watt-peak (Wp) three-switch rooftop PV micro-inverter, integrating quantum-behaved particle swarm optimization with reinforcement learning (QPSO-RL) for accurate maximum power point tracking (MPPT) and a linear quadratic regulator (LQR) for reserve-aware APR. The QPSO-RL algorithm improves available-power estimation under varying irradiance, temperature, and partial-shading conditions, while the LQR-based controller ensures fast, well-damped, and grid-compliant power regulation. The proposed framework was developed and validated using MATLAB/Simulink 2024 for simulation studies and LabVIEW with NI myRIO 2022 for real-time hardware implementation. Both simulation and experimental results confirm that the proposed method achieves 99.5% MPPT accuracy, convergence within 20 ms, grid-injected current total harmonic distortion (THD) below 3%, and a near-unity power factor. In addition, the reserve-based regulation strategy improves feeder compliance and reduces converter stress, thereby supporting reliable rooftop PV integration. These results demonstrate that the proposed QPSO-RL + LQR framework offers a practical and intelligent solution for high-performance, grid-supportive rooftop PV micro-inverter applications. Full article

This paper addresses the speed control problem of a DC motor in the presence of nonlinearities, disturbances, and unmodeled dynamics by proposing a neural backstepping control scheme based on a Recurrent High-Order Neural Network (RHONN). The proposed RHONN serves as an online approximator to compensate for uncertain nonlinear dynamics in a PD-based backstepping controller, enabling the system to handle disturbances, modeling errors, and unmodeled dynamics. Instead of relying on the traditional Extended Kalman Filter (EKF) for RHONN weight adaptation, the neural parameters are updated online using a Super-Twisting Algorithm (STA). As a result, the proposed STA-based learning law provides a simpler and robust covariance-free adaptation mechanism with practical finite-time convergence properties, making it suitable for real-time embedded implementations. The proposed method was evaluated through numerical simulations and implemented on an embedded microcontroller to assess its real-time performance. Simulation results show reductions between 0.04% and 2.04% in steady-state and integral error metrics compared with a tuned PD controller, and improvements up to 25.66% and 23.82% over LQR and MPC in the IMSE index. Experimental results demonstrate good tracking performance, robustness under varying load conditions, and low computational requirements, confirming the practical feasibility. Full article

Balance is an essential component in both everyday movement and sports performance. Balance boards are commonly used for training and physical therapy to improve balance. Conventional balance boards primarily rely on the user’s voluntary actions, whereas active/actuated balance boards can provide dynamic motion for both balance and rehabilitation. While this enables more effective training, it also introduces strong user-dependent and time-varying dynamics that are difficult to regulate with conventional controllers. This study addresses this limitation by developing a neuro-adaptive sliding mode controller to handle the strong inter-user variability and nonlinear pressure–force dynamics of pneumatic artificial muscles. The controller combines a learning neural network that updates online with a robust control structure to ensure stable motion in the presence of disturbances. The proposed approach was evaluated against commonly used PID and LQR controllers under sudden changes in operating conditions. Simulation results show that the proposed controller improves stability, reduces control effort, and adapts more effectively to different users and external disturbances. These findings suggest that neuro-adaptive control strategies can improve the reliability and responsiveness of balance training and rehabilitation devices, supporting safer and more personalized therapy. Full article

Accurate trajectory tracking of quadrotor UAVs remains challenging due to highly nonlinear dynamics, model uncertainties, and time-varying external disturbances, which make it difficult to achieve both precise position tracking and stable attitude regulation under control constraints. To tackle these coupled problems, this paper develops a hierarchical control framework in which the outer-loop particle swarm optimization (PSO)-compensated model predictive controller (PSMC) adaptively mitigates prediction errors and enhances robustness, while the inner-loop enhanced linear quadratic regulator (LQR), augmented with gain scheduling and control-rate relaxation, accelerates attitude convergence and ensures smooth control actions under varying flight conditions. A Lyapunov-based stability analysis is conducted to ensure closed-loop convergence. Simulation results on a helical reference trajectory show that, compared with the conventional MPC–LQR baseline, the proposed framework reduces the mean tracking errors by more than 13.2%, 17.1%, and 28% in the x-, y-, and z-directions under calm conditions, and by more than 34%, 26.2%, and 46.8% under wind disturbances. These results prove that the proposed hierarchical PSMC–LQR framework achieves superior trajectory tracking accuracy, strong robustness, and high practical implement ability for quadrotor control applications. Full article

This paper presents an efficient algorithm for Explicit Model-Following (EMF) control using an Output-derivative Feedback Control (OFC) scheme within the Reciprocal State Space (RSS) framework, aimed at overcoming the performance limitations associated with state-derivative dependence. For Lipschitz Nonlinear Systems (LNS), two approaches are proposed: a linear EMF (LEMF) strategy, which transforms the system into a Linear Parameter-Varying (LPV) representation via the Differential Mean Value Theorem (DMVT) to facilitate controller design, and a nonlinear EMF (NEMF) scheme, which enables the direct tracking of a nonlinear reference model. The stability of the closed-loop system is ensured by deriving control gains through Linear Quadratic Regulator (LQR) optimization. The proposed algorithms are validated through Real-Time Implementation (RTI) on an Arduino DUE platform, demonstrating their effectiveness and practical feasibility. Full article

Pipeline systems are essential across various industries for transporting fluids over various ranges of distances. A notable application is natural circulation through thermo-syphoning, driven by temperature-induced density variations that generate fluid flow in closed loops. This passive mechanism is widely employed in sectors such as process engineering, oil and gas, geothermal energy, solar water heaters, fertilizers, etc. Natural Circulation Loops eliminate the need for mechanical pumps. While this passive mechanism reduces energy consumption and maintenance costs, maintaining stability and efficiency under varying operating conditions remains a challenge. This study investigates thermo-syphoning in a rectangular closed-loop system and develops optimal control strategies like using a Linear Quadratic Regulator (LQR) and Model Predictive Control (MPC) to ensure stable and efficient heat removal while explicitly addressing physical constraints. The results demonstrate that MPC improves system stability and reduces energy usage through optimized control actions by nearly one-third in the initial energy requirement. Compared to the LQR and unconstrained MPC, MPC with active constraints effectively manages input limitations, ensuring safer and more practical operation. With its predictive capability and adaptability, the proposed MPC framework offers a robust, scalable solution for real-time industrial applications, supporting the development of sustainable and adaptive natural circulation pipeline systems. Full article

This paper addresses the spatial path tracking problem of the X-rudder autonomous underwater vehicle (AUV) under random sea current disturbances. An adaptive line-of-sight guidance-linear quadratic regulator (ALOS-LQR) control strategy with roll constraints is proposed to enhance the tracking control accuracy and stability of the X-rudder AUV in such environments. First, to mitigate the roll-instability-induced depth and heading coupling deviations caused by unknown environmental disturbances, a roll-constrained linear quadratic regulator (LQR) heading-pitch control strategy is designed. Second, to handle random disturbances and model uncertainties, a nonlinear extended state observer (ESO) is employed to estimate dynamic disturbances. At the kinematic level, an adaptive line-of-sight guidance method (ALOS) is utilized to transform the path tracking problem into a heading and pitch tracking problem, while compensating in real time for kinematic deviations caused by time-varying sea currents. Finally, the effectiveness of the proposed control scheme is validated through simulation experiments and lake trials. The results confirm the effectiveness of the proposed method. Specifically, the roll-constrained ESO-LQR reduces lateral and longitudinal errors by 77.73% and 80.61%, respectively, compared to the roll-constrained LQR. ALOS navigation reduced lateral and longitudinal errors by 85.89% and 94.87%, respectively, compared to LOS control, while exhibiting faster convergence than ILOS. In physical experiences, roll control reduced roll angle by 50.52% and depth error by 33.3%. Results demonstrate that the proposed control strategy significantly improves the control accuracy and interference resistance of the X-rudder AUV, exhibiting excellent accuracy and stability. Full article

Advancements in multi-rotor quadcopter technology and sensing capabilities have led to their increased utilisation for last-mile delivery. However, battery capacity constraints limit their use in extended-distance delivery scenarios. A visual servoing implementation is first proposed that leverages a CUDA-accelerated tag detection algorithm for real-time pose estimation of the target. A new approach is then developed to enhance quadcopter package collection by implementing a control scheme to attenuate aggressive load-swing in a payload arm that shifts from horizontal to vertical after obtaining a vertically mounted payload. The motion of the payload arm imposes a shift in the system’s centre of mass, leading to a possible instability. A non-linear control scheme is then introduced to address this problem through attenuation of the residual energy from payload oscillation. The performance of the visual servoing approach is validated through both numerical simulations and a physical quadcopter implementation, along with the performance of the load-swing attenuation through numerical simulations. Full article

This paper presents the formulation and simulation-based validation of a novel hierarchical fuzzy-adaptive Proportional–Integral–Derivative (PID) control framework for a rectilinear active mass damper, designed to enhance vibration suppression in structural applications. The proposed scheme utilizes a Linear–Quadratic Regulator (LQR)-optimized PID controller as the baseline regulator. To address the limitations of this baseline PID controller under varying seismic excitations, an auxiliary fuzzy adaptation layer is integrated to adjust the state-weighting matrices of the LQR performance index dynamically. The online modification of the state weightages alters the Riccati equation’s solution, thereby updating the PID gains at each sampling instant. The fuzzy adaptive mechanism modulates the said weighting parameters as nonlinear functions of the classical displacement error and normalized acceleration. Normalized acceleration provides fast, scalable, and effective feedback for vibration mitigation in structural control using AMDs. By incorporating the system’s normalized acceleration into the adaptation scheme, the controller achieves improved self-tuning, allowing it to respond efficiently and effectively to changing conditions. The hierarchical design enables robust real-time PID gain adaptation while maintaining the controller’s asymptotic stability. The effectiveness of the proposed controller is validated through customized MATLAB/SIMULINK-based simulations. Results demonstrate that the proposed adaptive PID controller significantly outperforms the baseline PID controller in mitigating structural vibrations during seismic events, confirming its suitability for intelligent structural control applications. Full article

Wheel slipping during trajectory tracking presents significant challenges for wheeled mobile robots (WMRs), degrading accuracy and stability on low-friction or dynamic terrain. Effective control requires addressing unknown slipping parameters while balancing tracking precision and energy efficiency. To address this challenge, a control framework integrating a sliding mode observer (SMO), an improved particle swarm optimization (PSO) algorithm, and a linear quadratic regulator (LQR) is proposed. First, a dynamic model incorporating longitudinal slipping is established. Second, an SMO is designed to estimate the slipping ratio in real-time, with chattering suppressed using a low-pass filter. Finally, an improved PSO algorithm featuring a nonlinear cosine-decreasing inertia weight strategy optimizes the LQR weighting matrices (Q/R) online to both minimize tracking errors and control energy consumption. Simulations including both circular and sine wave trajectories demonstrate that the SMO achieves rapid and accurate slipping ratio estimation, while the PSO-optimized LQR significantly enhances tracking accuracy, achieves smoother control inputs, and maintains stability under varying slipping conditions. Full article