Search Results (149)

Off-road vehicle control designs often neglect the complex tire–soil interactions inherent to soft terrain. This paper proposes a Variable Impedance Control (VIC) strategy integrated with a high-fidelity terramechanics model. First, a real-time sinkage estimation algorithm is derived using experimentally identified Bekker parameters and the quasi-rigid wheel assumption to capture the nonlinear feedback between soil deformation and vehicle dynamics. Building on this, the VIC strategy adaptively regulates virtual stiffness, damping, and inertia parameters based on real-time suspension states. Comparative simulations on an ISO Class-C soft soil profile demonstrate that this framework effectively balances ride comfort and safety constraints. Specifically, the VIC strategy reduces the root-mean-square of vertical body acceleration by 46.9% compared to the passive baseline, significantly outperforming the Linear Quadratic Regulator (LQR). Furthermore, it achieves a 48.6% reduction in average power relative to LQR while maintaining suspension deflection strictly within the safe range. Moreover, unlike LQR, the VIC strategy improves tire deflection performance, ensuring superior ground adhesion. These results validate the method’s robustness and energy efficiency for off-road applications. Full article

To enhance the sustainable power regulation capability of hydroelectric unit regulation systems (HURS) under modern power system requirements, this paper proposes an optimal linear quadratic regulator (LQR)-based power control strategy optimized using an improved Salp Swarm Algorithm (ISSA). First, comprehensive mathematical models of the hydraulic, mechanical, and electrical subsystems of HURS are established, enabling a unified state-space representation suitable for LQR controller design. Then, the weighting matrices of the LQR controller are optimally tuned via ISSA using a hybrid objective function that jointly considers dynamic response performance and control effort, thereby contributing to improved energy efficiency and long-term operational sustainability. A large-scale hydropower unit operating under weakly stable conditions is selected as a case study. Simulation results demonstrate that, compared with conventional LQR tuning approaches, the proposed ISSA-LQR controller achieves faster power response, reduced overshoot, and enhanced robustness against operating condition variations. These improvements effectively reduce unnecessary control actions and mechanical stress, supporting the reliable and sustainable operation of hydroelectric units. Overall, the proposed method provides a practical and effective solution for improving power regulation performance in hydropower plants, thereby enhancing their capability to support renewable energy integration and contribute to the sustainable development of modern power systems. Full article

With the rapid development of mobile robotics, wheeled bipedal robots, which combine the terrain adaptability of legged robots with the high mobility of wheeled systems, have attracted increasing research attention. To address the balance control problem during both standing and locomotion while reducing the influence of noise on control performance, this paper proposes a balance control framework based on a Linear Quadratic Regulator integrated with an Extended Kalman Filter (KLQR). Specifically, a baseline LQR controller is designed using the robot’s dynamic model, where the control input is generated in the form of wheel-hub motor torques. To mitigate measurement noise and suppress oscillatory behavior, an Extended Kalman Filter is applied to smooth the LQR torque output, which is then used as the final control command. Filtering experiments demonstrate that, compared with median filtering and other baseline methods, the proposed EKF-based approach significantly reduces high-frequency torque fluctuations. In particular, the peak-to-peak torque variation is reduced by more than 60%, and large-amplitude torque spikes observed in the baseline LQR controller are effectively eliminated, resulting in continuous and smooth torque output. Static balance experiments show that the proposed KLQR algorithm reduces the pitch-angle oscillation amplitude from approximately ±0.03 rad to ±0.01 rad, corresponding to an oscillation reduction of about threefold. The estimated RMS value of the pitch angle is reduced from approximately 0.010 rad to 0.003 rad, indicating improved convergence and steady-state stability. Furthermore, experiments involving constant-speed straight-line locomotion and turning indicate that the KLQR algorithm maintains stable motion with velocity fluctuations limited to within ±0.05 m/s. The lateral displacement deviation during locomotion remains below 0.02 m, and no abrupt acceleration or deceleration is observed throughout the experiments. Overall, the results demonstrate that applying Extended Kalman filtering to smooth the control torque effectively improves the smoothness and stability of LQR-based balance control for wheeled bipedal robots. Full article

This paper proposes a hybrid optimization–control framework that combines the Particle Swarm Optimization (PSO) algorithm, the Alternating Direction Method of Multipliers (ADMM), and a Linear–Quadratic Regulator (LQR) for energy-efficient and reliable operation of parallel pump systems. The PSO layer performs global exploration over mixed discrete–continuous design variables, while the ADMM layer coordinates distributed flows under head and reliability constraints, yielding hydraulically feasible operating points. The inner LQR controller achieves optimal speed tracking with guaranteed asymptotic stability and improved robustness against nonlinear load disturbances. The overall PSO–ADMM–LQR co-design minimizes a composite objective that accounts for steady-state efficiency, transient performance, and control effort. Simulation results on benchmark multi-pump systems demonstrate that the proposed framework outperforms conventional PSO- and PID-based methods in terms of energy savings, dynamic response, and robustness. The method exhibits low computational complexity, scalability to large systems, and practical suitability for real-time implementation in smart water distribution and industrial pumping applications. Full article

Automated insulin delivery (AID) systems depend on their actuators’ behavior since saturation limits, rate constraints, and hardware degradation directly affect the stability and safety of glycemic regulation. In this paper, we conducted an actuator-centric evaluation of five control strategies: Nonlinear Model Predictive Control (NMPC), Linear MPC (LMPC), Adaptive MPC (AMPC), Proportional-Integral-Derivative (PID), and Linear Quadratic Regulator (LQR) in three physiologically realistic scenarios: the first combines exercise and sensor noise to test for stress robustness; the second tightens the actuation constraints to provoke saturation; and the third models partial degradation of an insulin actuator in order to quantify fault tolerance. We have simulated a full virtual cohort under the two-actuator configurations, DG3.2 and DG4.0, in an effort to investigate generation-to-generation consistency. The results detail differences in the way controllers distribute insulin and glucagon effort, manage rate limits, and handle saturation: NMPC shows persistently tighter control with fewer rate-limit violations in both DG3.2 and DG4.0, whereas the classical controllers are prone to sustained saturation episodes and delayed settling under hard disturbances. In response to actuator degradation, NMPC suffers smaller losses in insulin effort with limited TIR losses, whereas both PID and LQR show increased variability and overshoot. This comparative analysis yields fundamental insights into important trade-offs between robustness, efficiency, and hardware stress and demonstrates that actuator-aware control design is essential for next-generation AID systems. Such findings position MPC-based algorithms as leading candidates for future development of actuator-limited medical devices and deliver important actionable insights into actuator modeling, calibration, and controller tuning during clinical development. Full article

The development of control strategies for autonomous vehicles requires a reliable and cost-effective validation approach. In this context, testbeds enabling repeatable experiments under controlled conditions are gaining relevance. Scaled vehicles have proven to be a valuable alternative to full-scale or simulation-based testing, enabling experimental validation while reducing costs and risks. This work presents a 1:5 scale modular vehicle platform, derived from a commercial Radio-Controlled (RC) vehicle and adapted as experimental testbed for control strategy validation and vehicle dynamics studies. The vehicle features an electric powertrain, operated through a Speedgoat Baseline Real-Time Target Machine (SBRTM). The hardware architecture includes a high-performance Inertial Measurement Unit (IMU) with embedded Global Navigation Satellite System (GNSS). An Extended Kalman Filter (EKF) is implemented to enhance positioning accuracy by fusing inertial and GNSS data, providing reliable estimates of the vehicle position, velocity, and orientation. Two path-following algorithms, i.e., Stanley Controller (SC) and the Linear Quadratic Regulator (LQR), are designed and integrated. Outdoor experimental tests enable the evaluation of tracking accuracy and robustness. The results demonstrate that the proposed scaled testbed constitutes a reliable and flexible platform for benchmarking autonomous vehicle controllers and enabling experimental testing. Full article

This paper develops an algebraic framework for operator matrix polynomials and demonstrates its application to control-design problems in aeroservoelastic systems. We present constructive spectral-factorization and linearization tools (block spectral divisors, companion forms and realization algorithms) that enable systematic block-pole assignment for large-scale MIMO models. Building on this theory, an adaptive block-pole placement strategy is proposed and cast in a practical implementation that augments a nominal state-feedback law with a compact neural-network compensator (single hidden layer) to handle un-modeled nonlinearities and uncertainty. The method requires state feedback and the system’s nominal model and admits Laplace-domain analysis and straightforward implementation for a two-degree-of-freedom aeroelastic wing with cubic stiffness nonlinearity and Roger aerodynamic lag is validated in MATLAB R2023a. Comprehensive simulations (Runge–Kutta 4) for different excitations and step disturbances demonstrate the approach’s advantages: compared with Eigenstructure assignment, LQR and $H_2$-control, the proposed method achieves markedly better robustness and transient performance (e.g., closed-loop ${‖\mathit{H}\left(i\omega \right)‖}_2$ ≈ 4.64, condition number χ ≈ 11.19, and reduced control efforts μ ≈ 0.41, while delivering faster transients and tighter regulation (rise time ≈ 0.35 s, settling time ≈ 1.10 s, overshoot ≈ 6.2%, steady-state error ≈ 0.9%, disturbance-rejection ≈ 92%). These results confirm that algebraic operator-polynomial techniques, combined with a compact adaptive NN augmentation, provide a well-conditioned, low-effort solution for robust control of aeroelastic systems. Full article

This paper investigates the problem of data-driven optimal preview repetitive control of linear discrete-time systems. Firstly, by integrating prior information into the preview time domain, an augmented state-space system is established. Secondly, the original output tracking problem is mathematically reconstructed and transformed into the optimization problem form of a linear quadratic tracking (LQR). Furthermore, a Q-function-based iterative algorithm is designed to dynamically calculate the optimal tracking control gain based solely on online measurable data. This method has a dual-breakthrough feature: it neither requires prior knowledge of system dynamics nor provides an initial stable controller. Finally, the superiority of the proposed scheme is verified through numerical simulation experiments. Full article

Aerial refueling with hose–drogue systems provides operational flexibility but is highly susceptible to disturbances from tanker wakes, receiver bow waves, and atmospheric turbulence, which induce drogue oscillations and reduce docking success. To address these challenges, this study develops a dynamic model and introduces a strake-fin-based actively stabilized drogue. The hose is represented as a chain of rigid segments with aerodynamic drag estimated using Hoerner’s empirical correlations, while the drogue’s aerodynamic characteristics are obtained from CFD simulations. An efficient neighbor-cell search algorithm is implemented to map the hose–drogue configuration onto the CFD flow field, and atmospheric turbulence is modeled using the Dryden model. The drogue is equipped with two pairs of strake-type control fins, whose relative deflections are regulated by a linear quadratic regulator (LQR) to generate corrective aerodynamic forces. Simulation results under tanker wake, bow-wave, and severe turbulence conditions show that the proposed system effectively suppresses drogue oscillations, reducing displacement amplitudes by over 80% and maintaining positional deviations within 0.1 m. These results confirm the robustness of the modeling framework and demonstrate the potential of the strake-fin-based active stabilization strategy to ensure safe and reliable aerial refueling operations. Full article

Active suspension systems have gained significant attention for their capability to improve vehicle dynamics and energy efficiency. However, achieving consistent control performance under diverse and uncertain road conditions remains challenging. This paper proposes a prior-guided residual reinforcement learning framework for active suspension control. The approach integrates a Linear Quadratic Regulator (LQR) as a prior controller to ensure baseline stability, while an enhanced Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm learns the residual control policy to improve adaptability and robustness. Moreover, residual connections and Long Short-Term Memory (LSTM) layers are incorporated into the TD3 structure to enhance dynamic modeling and training stability. The simulation results demonstrate that the proposed method achieves better control performance than passive suspension, a standalone LQR, and conventional TD3 algorithms. 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

This paper presents an optimal control for autonomous vehicles navigating in intersection scenarios. The proposed controller is based on solving a Quadratic Programming optimization technique to provide a feasible control signal respecting actuator constraints. The proposed controller was implemented in a scale-sized vehicle and is executed using only on-board perception and computing systems to retrieve the state dynamics, i.e., an inertial measurement unit and a monocular camera, to compute the estimated states through intelligent computer vision algorithms. The stability of the error signals of the closed-loop system was proved both mathematically and experimentally, using standard performance indices for ten trials. The proposed technique was compared against LQR and MPC strategies, showing 67% greater accuracy than the LQR approach and 53.9% greater accuracy than the MPC technique, while turning during the intersection. Moreover, the proposed QP controller showed significantly greater efficiency by reducing the control effort by 63.3% compared to the LQR, and by a substantial 78.4% compared to the MPC. These successful results proved that the proposed controller is an effective alternative for autonomously navigating within intersection scenarios. Full article

With the rapid development of transportation infrastructure, bridges increasingly face prominent issues of dynamic response and fatigue damage induced by vehicle–bridge interaction (VBI). To effectively suppress the coupled vibrations and enhance both vehicle ride comfort and bridge service life, this paper proposes an active inerter-spring-damper (ISD) suspension system based on Particle Swarm Optimization (PSO) algorithm and Linear Quadratic Regulator (LQR) control. By establishing a VBI model considering general boundary conditions and employing the modal superposition method to solve the system response, an LQR controller is designed for multi-objective optimization targeting the vehicle body acceleration, suspension dynamic travel, and tire dynamic load. To further improve control performance, the PSO algorithm is utilized to globally optimize the LQR weighting matrices. Numerical simulation results demonstrate that, compared to passive suspension and unoptimized LQR active suspension, the PSO-LQR control strategy significantly reduces vertical body acceleration and tire dynamic load, while also improving the convergence and stability of the suspension dynamic travel. This research provides a new insight into the control method for VBI systems, possessing both theoretical and practical engineering application value. Full article

In disturbance scenarios, wheeled bipedal robots (WBRs) require effective control algorithms to restore balance. To address the trade-off between computational burden and control precision, and to enhance anti-disturbance capability, this paper proposes a soft-constrained Model Predictive Control (MPC) algorithm with optimized horizon parameters tailored to the hardware of the WBR. A cost function is designed, and the Dung Beetle Optimizer (DBO) is employed to optimize the MPC’s prediction and control horizons. An experimental platform is built, and impact and load disturbance experiments are conducted. The experimental results show that, under impact disturbances, the pitch angle and displacement overshoot with optimized MPC are reduced by 58.57% and 42.20%, respectively, compared to unoptimized LQR. Under load disturbances, the pitch angle and displacement overshoot are reduced by 17.09% and 15.53%, respectively, with both disturbances converging to the equilibrium position. Full article

This paper addresses the challenge of stabilizing an unmanned aerial vehicle with an arm (UAM) on a pipeline with disturbance, where the disturbance factors include white noise, mass uncertainty, and wind disturbance. An anti-disturbance control method is proposed utilizing nonlinear model predictive control (NMPC). Initially, the natural wind field model is developed. Considering wind disturbance, the UAM dynamics are analyzed utilizing Newton–Euler theory. Subsequently, the no-slip constraints and the terminal constraints are defined to prevent UAM from destabilizing and falling. The NMPC-based algorithm is developed to ensure the stable control of UAM, transforming the optimization problem into a nonlinear programming problem. The terminal cost function and the inequality constraints for establishing the state variables using linear quadratic regulator (LQR) are meticulously studied. Finally, numerical simulations are carried out to further verify the proposed method, considering internal disturbance about physical parameters and external disturbance about wind. Simulation results show that the disturbance is well compensated, and the UAM tilt angle is less than 0.3 deg. Therefore, the proposed control method can comprehensively consider the input energy consumption and the realization of stability, and has a certain degree of anti-interference. Full article