Search Results (415)

Wheeled inverted-pendulum robots with movable upper structures and variable payloads exhibit configuration-dependent equilibrium drift and payload-dependent dynamic variation, which complicate balancing control. This paper proposes a gain-scheduled controller–observer framework for payload-adaptive balancing of such a robot. First, the multi-body system is reduced to a control-oriented equivalent inverted-pendulum model through center-of-mass lumping, from which a parameter-varying linearized model is established. On this basis, an H∞ state-feedback controller with input constraints is synthesized in a linear matrix inequality (LMI) framework, and an augmented-state observer is designed to estimate the residual equilibrium offset induced by payload variation. To improve robustness over the operating range, the frozen-point design is extended to a sampled-model multi-model synthesis framework, and gain scheduling is implemented with respect to the measurable arm angle. Nonlinear Simscape simulations show that the proposed method can recover balance at representative fixed operating points, compensate effectively for load-induced equilibrium drifts, and preserve stable balancing performance under slow arm-angle variation. Quantitative comparisons with an LQR baseline further support the effectiveness of the proposed framework for payload-adaptive balancing control. Full article

Under complex disturbances and backwater time-delay conditions, traditional open-channel gate water level control suffers from insufficient accuracy and slow response, readily causing water level overruns, control instability, and engineering safety risks. To overcome the limitations of conventional controllers in responding to rainfall disturbances, this study proposes a Model Predictive Control (MPC) algorithm based on the Integrator Delay (ID) model. The approach first integrates an LSTM-KAN (Kolmogorov–Arnold Network) model for accurate rainfall prediction, providing reliable inputs for disturbance feedforward. Subsequently, leveraging the SWMM simulation model and the PySWMM library, ID model parameters (backwater area and lag time) are identified in real time through impulse response testing. A state-space representation is then formulated and incorporated into the MPC rolling optimization framework, enabling precise water level forecasting over the prediction horizon. Simulation results demonstrate that the average computation time for 24-hour tests is only 240 seconds, with markedly reduced water level deviations. Experimental validation confirms superior performance under steady flow conditions (flow fluctuations < 0.007 m³/s; settling time ≈ 210 seconds) and constant water level control, achieving water level deviations < 0.05 m in known disturbance scenarios. Compared with the conventional Linear Quadratic Regulator (LQR), the proposed MPC algorithm reduces gate response time by 6.38–19.80% under the tested rainfall conditions. The proposed method establishes a complete closed-loop framework integrating rainfall prediction, water level forecasting, and combined feedforward-feedback control, offering an efficient and practical solution for open-channel gate water level management in smart water conservancy systems. It holds considerable theoretical significance and application value. Full article

This paper investigates the optimal control problem of opinion dynamics within complex networks. By introducing a state transformation, the original problem is reformulated within a discounted Linear Quadratic Regulator (LQR) framework, establishing a connection between opinion control and classical control theory. Within this unified framework, the optimal control law can be obtained by solving the discrete-time algebraic Riccati equation, thereby circumventing the complexity of dealing with linear terms inherent in traditional dynamic programming approaches. Numerical experiments validate the effectiveness of the algorithm in a benchmark case, a 20-node complete network, and complex topologies. They also reveal the influence mechanisms of network heterogeneity on convergence speed and control energy consumption, providing a theoretical basis for public opinion guidance strategies under different network structures. Full article

The present article introduces TRANS-D3, an innovative hybrid method that combines the Twin Delayed Deep Deterministic Policy Gradient (TD3) reinforcement learning algorithm with the Transformer architecture for predicting the remaining useful life (RUL). The model utilizes an optimized reward function based on the Linear Quadratic Regulator (LQR) to approach error correction as a dynamic control problem. On the CMAPSS dataset, TRANS-D3 demonstrates a marked advantage, achieving RMSE reductions of 84–90% in baseline situations (FD001) and 23–45% in highly variable contexts (FD003/FD004). Statistical validation demonstrates high reliability, with a coefficient of determination $R^2$ of more than 0.93 in each of the subsets; the maximum is 0.9984 in FD001. The 95% confidence intervals for the mean error, ranging from [0.709 to 1.244] in FD001 and from [−1.324 to 1.748] in FD004, also confirm that the framework is a statistically unbiased estimator. In terms of Score, the model reduces penalties by between 80% and 95% compared to advanced architectures such as DAST or STAR, ensuring very stable predictions. These findings present a novel robust optimization paradigm, which is essential for ensuring the safety and reliability of complex industrial systems in the context of Industry 4.0. Full article

Actuator placement optimization based on a reduced-order model is essential for controlling a high-dimensional system in real time. This paper discusses actuator placement in an unstable high-dimensional system based on a reduced-order model obtained by BPOD with input–output projection. Actuator locations in a linearized Ginzburg–Landau model are optimized with three objective functions based on a Riccati equation, a controllability Gramian, and an impulse response matrix. Further, the computation time for actuator selection and the resulting LQR performance are evaluated. The LQR performance is basically high when actuators are placed based on the Riccati equation or the impulse response matrix. The computation time of the method based on the impulse response matrix is much smaller than that of the other two methods. Thus, the method based on the impulse response matrix seems to have more advantages than the other two methods in terms of optimizing the actuator locations of the analyzed model. Moreover, it seems to be beneficial to place actuators with a low-dimensional model using this method. Full article

Conventional heading-keeping autopilot logic exhibits well-known performance limitations under complex route geometry and environmental disturbances. Motivated by this limitation, this paper proposes a line-adhering intelligent navigation framework for disturbance-aware path-following of autonomous ships. The core idea is based on numerical simulation scenarios representing curved inland/coastal routes under wind- and current-disturbance conditions. The addressed gap lies in the limited integration of route-geometry adherence, human-like maneuvering logic, and disturbance-aware controller reconfiguration within conventional heading-centered ship path-following frameworks. Therefore, a rough-set classifier identifies disturbance modes and reconfigures PID, LQR, and MPC controllers in real time. Moreover, a vessel-dynamics constrained Bézier refinement method generates high-resolution reference paths aligned with navigational curvature limits. Mathematical models including the Nomoto and MMG formulations are incorporated to ensure controllability and dynamic feasibility. Results show that the proposed framework improves path-following precision, robustness, and comfort under the considered simulation conditions. Full article

The planning of trajectories for Connected Autonomous Vehicles (CAVs) represents a pivotal aspect of autonomous driving technologies, enabling secure navigation within traffic environments. Traditional models for trajectory control primarily focus on the efficiency and safety of individual vehicles but often overlook the dynamics involved in vehicle-to-vehicle and vehicle-to-infrastructure interactions. This study introduces a novel concept, the “driving risk field,” which imposes constraints on vehicular movement within designated road spaces to enhance safety. A vehicle dynamics model is developed, employing a non-linear fifth-degree polynomial to approximate the trajectory curves, with optimization performed using the Sequential Quadratic Programming (SQP) method. The efficacy of the optimized model is validated through simulations on the Prescan/Simulink platform, demonstrating a 17.9% reduction in trajectory angle slopes and a 23.4% decrease in lateral and longitudinal errors compared to conventional Model Predictive Control (MPC), Pure-Pursuit (PP) and Linear Quadratic Regulator (LQR) models. This approach significantly enhances vehicle control in traffic bottleneck areas, indicating superior trajectory adaptation. Full article

To address the significant degradation of steering performance in dual-motor steer-by-wire (DMSBW) systems caused by the coupling of communication delays and motor faults, a robust fault-tolerant control strategy is proposed under the dual-motor collaborative driving mode. First, a matrix polytopic model is employed to describe the nonlinearities introduced by delays, establishing a delay-dependent DMSBW system dynamics model. Second, for electrical faults such as internal motor short circuits that cause a sudden drop in rotational speed, an adaptive motor-switching fault-tolerant mechanism is designed based on a smooth monitoring function to achieve rapid fault detection and steering function reconstruction. Furthermore, considering the coupled impact of delays and faults, a robust linear quadratic regulator (LQR) controller with feedforward compensation is designed to enhance system fault tolerance and robustness. Simulation results demonstrate that under steering wheel angle step input with delays, the proposed strategy achieves a rapid response without significant overshoot, and the steady-state tracking error is significantly reduced. In variable-speed single lane change maneuvers with coupled delays and severe motor faults, the peak and root mean square (RMS) errors of the front wheel angle are reduced to 0.0112 rad and 0.0031 rad, respectively. Compared to the delay-compensated nonlinear model predictive control (NMPC) and sliding mode control (SMC) strategies that do not account for delays, the peak error is reduced by 15.79% and 45.37%, while the RMS error decreases by 27.91% and 35.42%, respectively. Additionally, the peak and RMS errors of the sideslip angle and yaw rate are substantially reduced, validating the strategy’s excellent steering fault tolerance, robustness, and vehicle handling stability. Full article

With the increasing global elderly population and, naturally, mobility limitations, the number of people requiring walking aids is increasing. Research on robotic walking aids tends to focus on walkers, while robotic canes are usually designed for hospital or clinical use. Research into compact, low-cost robotic canes intended for use outside clinical environments remains limited. This work aims at designing a robotic cane with a deformable wheel and exploring its dynamics in a variety of terrains and small obstacles. A flexible wheel fabricated from thermoplastic polyurethane (TPU) material allows it to adapt to different surface profiles. The motion is controlled via a LQR controller. The prototype was tested in several real-world scenarios, with users without walking difficulties, and in rehabilitation scenarios, with users with mild locomotion difficulties. The flexible wheel proved capable of adapting to terrains with some irregularities while still providing support to the users. Furthermore, expert opinions suggest benefits in terms of musculoskeletal efforts. Full article

Seismic-induced torsional response remains a significant barrier to achieving resilient and sustainable building foundations, as traditional passive isolation systems often fail to regulate rotational motion effectively. This study examines an adaptive gyroscopic feedback-based foundation control system designed to provide automated torsional seismic mitigation. The proposed system integrates real-time angular velocity sensing using MEMS gyroscopes, Kalman filter state estimation, and an adaptive Linear Quadratic Regulator to modulate damping in response to changing ground motion. A single-degree-of-freedom torsional foundation model was developed and evaluated in GNU Octave 8.4.0/MATLAB R2024a Simulink using the recorded El Centro 1940 NS earthquake input. The adaptive controller achieved notable improvements, reducing total vibration energy by 69%, peak angular displacement by 47.6%, and RMS angular velocity by 39.5% relative to the uncontrolled case, while keeping control energy below 19% of the seismic input. These results demonstrate that gyroscopic feedback enhances damping, limits torsional resonance, and stabilises foundation behaviour under actual earthquake excitation. The system’s low energy requirement, compatibility with embedded hardware, and automated response characteristics underscore its potential for integration into sustainable and intelligent foundation designs. While results are demonstrated using the El Centro 1940 record as a benchmark, broader generalisation will be established through multi-record suites and uncertainty quantification in future work. The study highlights a feasible pathway for advancing automated seismic protection in buildings through active, sensor-driven torsional control. Full article

To meet the high-fidelity testing environment requirements for autonomous driving system development, driving simulators are gradually evolving from tools that “only provide scenes and interaction interfaces” into integrated verification platforms for autonomous driving capabilities. These simulators, in particular, need to feature testable and scalable decision-making modules. However, the autonomous driving functions in existing driving simulators mostly rely on rule-based or simplified model approaches, which are inadequate for depicting the complex interactions in real-world traffic and fail to meet the personalized decision-making needs under various driving styles. To address these challenges, this paper designs and implements a trajectory data-driven personalized autonomous driving decision system, using drone aerial imagery as the core data source to provide realistic background traffic flow and human-like decision-making capabilities. The proposed system can be interpreted as an integrated decision–planning–control framework deployed within a high-fidelity driving simulation platform. It consists of a driving style classification module based on drone trajectory data, a personalized decision module integrating inverse reinforcement learning and dynamic game theory, and a planning and control module. First, a natural driving database is built using 4997 real vehicle trajectories, and prior features of different driving styles are extracted through trajectory feature engineering and an improved K-means++ method. Based on this, a personalized decision-making framework that combines dynamic game theory and maximum entropy inverse reinforcement learning is proposed, aiming to learn the preference weights of different driving styles in terms of safety, comfort, and efficiency. Furthermore, the Dueling Network Architecture (DuDQN) is used to generate human-like lane-changing strategies. Subsequently, a real-time closed-loop execution of personalized decisions in the simulation platform is achieved through fifth-order polynomial trajectory planning, lateral Linear Quadratic Regulator (LQR) control, and longitudinal cascade Proportional–Integral–Derivative (PID) control. Experimental results show that the personalized decision model trained with drone data can realistically reproduce vehicle decision-making behaviors in natural traffic flows within the simulation environment and generate autonomous driving strategies that are highly consistent with different driving styles. This significantly enhances the humanization and personalization capabilities of the autonomous driving module in the driving simulator. Full article

This study presents a comparative evaluation of path-tracking controllers designed from reduced-order state-space vehicle models. A four-state state-space model is formulated from the bicycle-model dynamics and target-path geometry, where the state variables are the previewed lateral error, heading error, side-slip angle, and yaw rate. To reduce the dependence on variables that are difficult to obtain in practice, a three-state model is derived by eliminating the explicit side-slip dynamics, and a two-state model is further obtained by replacing the yaw-rate dynamics with a kinematic approximation. Based on these three models, linear-quadratic regulator (LQR) controllers are designed. In addition, two linear quadratic static output-feedback (LQ SOF) controllers are constructed from the original four-state model by using reduced output sets. The five controllers are evaluated by vehicle simulations carried out in CarSim under front-wheel-steering and four-wheel-steering configurations. The results clarify the influence of controller structure and model order on path-tracking performance and identify the controller–actuator combination that provides the most favorable performance under the conditions considered. Full article

Underactuated system control is a central topic in nonlinear system control. For the three-link vertical underactuated manipulator with only the first joint actuated (APP manipulator), the control objective of swing-up and balancing is challenging. The advantages of this paper are as follows: (i) The proposed method avoids balancing region division in common partitioned control, preventing failure to stabilize at the target position due to improper partitioning. (ii) The time-based switching condition optimized via parameter tuning is easier to satisfy than the state-based condition. (iii) The proposed controller effectively suppresses state fluctuations caused by switching, yielding a smoother transition. (iv) The proposed controller avoids the singularity problem. The main procedures are as follows. First, the dynamic model of the APP manipulator is established. Then, a trajectory is designed to guide the active link from the initial position to the vicinity of the target position. On this basis, to ensure that all links can simultaneously reach the vicinity of the target position, the trajectory parameters are optimized according to the coupling relationship between the links. Next, an NFTSM-based tracking controller is developed to steer the links along the optimized trajectory. After that, an LQR-based stabilization controller is further employed to lock the system at the target position. Finally, the effectiveness of the proposed method is verified through simulations. 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