Search Results (416)

Developing an advanced simulation to control a swarm of 20 PocketQube nanosatellites using a linear quadratic regulator (LQR) involves several crucial steps that go beyond the initial scheme. A comprehensive approach requires a deep understanding of orbital mechanics and, in particular, the challenges presented by the nanosatellite platform. The inherent limitations in terms of nanosatellite power, propulsion, and communications systems necessitate careful orbital selection and maneuver planning to achieve mission objectives efficiently and reliably. This includes optimizing launch windows, understanding atmospheric drag effects in low Earth orbits (LEOs), and designing robust attitude control systems to maintain the desired pointing for scientific instruments or communications links. Our work focused on simulating the attitude control of PocketQube nanosatellites in a swarm using the R2022a release of the Matlab/Simulink environment. First, we provided a mathematical model for the relative coordinates of a nanosatellite swarm. Second, we developed a mathematical model of the linear quadratic regulator implementation in the relative navigation. Third, we simulated the attitude control of 20 PocketQube nanosatellites using the Matlab/Simulink environment. Finally, we provided the swarm scenario and attitude control system data. The simulation of an attitude control system for 20 PocketQube nanosatellites using an LQR controller in a swarm successfully demonstrated the stabilization capabilities essential for swarm operations in the space environment. A link to a video of the simulation is provided in the Results section. Full article

A second-order nonsingular terminal sliding mode control (SONTSMC) is proposed to solve the stabilization and tracking problems of an inverted pendulum. Although, a first-order sliding mode controller with the integral of the cart position can eliminate the offset in the cart position caused by incorrect calibration of the pendulum angle while balancing the pendulum at the upright equilibrium position, its control precision and chattering reduction can be improved by using a higher-order sliding mode controller. Therefore, the SONTSMC is designed by combining nonsingular sliding mode control and first-order sliding mode control to construct a second-order sliding mode controller that enhances tracking accuracy and reduces the chattering problems associated with sliding mode control. The performance of the proposed control is compared with that of the linear quadratic regulator sliding mode control (LQRSMC) and the integral linear quadratic regulator sliding mode control (ILQRSMC) for CIP’s stabilization and tracking. The results indicate that SONTSMC significantly increases the control performance of CIP while efficiently utilizing control energy. Full article

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

Helicopter vibration is an inherent characteristic of rotorcraft operations, arising from transmission dynamics and unsteady aerodynamic loading, posing challenges to flight control and longevity of structural components. Excessive vibration elevates pilot workload and accelerates fatigue damage in critical components. Leveraging advances in optimal control and microelectronics, the active vibration control methods offer superior adaptability compared to the passive techniques, which are limited by added weight and narrow bandwidth. In this study, a comprehensive vibration analysis and optimal control framework are developed for the Bo 105 helicopter rotor blade exhibiting flapping, lead-lag, and torsional (triply coupled) motions, where a Linear Quadratic Regulator (LQR) is employed to suppress vibratory responses. An analytical formulation is constructed to estimate the blade’s sectional properties, used to compute the coupled natural frequencies of vibration by the modified Galerkin method. An orthogonality condition for the coupled flap–lag–torsion dynamics is established to derive the corresponding state-space equations for both hovering and forward-flight conditions. The LQR controller is tuned through systematic variation of the weighting parameter Q, revealing an optimal range of 10²–10⁴ that balances vibration attenuation and control responsiveness. The predicted frequencies of the vibrating rotor blade are compared with the finite element modeling results and published experimental data. The proposed framework captures the triply coupled rotor blade dynamics with optimal control, achieves modal vibration reductions of approximately 60–90%, and provides a clear theoretical benchmark for future actuator-integrated computational and experimental studies. Full article

In this work, an effective control framework is proposed for a two-wheeled mobile robot (TWMR) operating under time-varying disturbances and uncertain system parameters. To enhance robustness against these uncertainties, an integral sliding mode control (ISMC) method is adopted. A mathematical model of the TWMR is obtained in the state form, and an ISMC law is designed. The proposed control law comprises two terms: a nominal term and a discontinuous term. The nominal term is designed based on the linearized model and optimal control to eliminate any steady-state error, while the discontinuous term is designed based on the sliding surface and the reaching law to force the system state onto the sliding surface under changing disturbances and parameter variations. These two terms are combined to constitute the overall control law. The performance and robustness of the proposed method are assessed through simulation under different uncertainty conditions of the TWMR. Full article

This paper proposes a comprehensive framework for control of an extended Morphing Aerial System (MAS) designed to achieve both mission flexibility and fault tolerance. The proposed quadrotor features a morphing configuration that integrates a two-dimensional planar folding structure with a tilt mechanism. This morphing capability offers structural simplicity and operational versatility, which enables stable flight in various established modes. The control strategy utilizes feedback linearization and a Linear Quadratic Regulator (LQR), adapted to the system’s nonlinear dynamics and capable of controlling the MAS across various configurations (X, H, and O modes). An Extended Kalman Filter (EKF) is also incorporated for state estimation. To ensure fault resilience, we introduce the Y-mode configuration and a corresponding Fault-Tolerant Control (FTC) architecture. Numerical simulations demonstrate that while a nominal controller fails immediately upon motor failure, the proposed FTC method successfully recovers flight stability, converging to the reference trajectory within 6.9 s. Furthermore, robustness analysis confirms that the system maintains operational integrity for fault detection latencies up to 0.40 s, demonstrating its feasibility under realistic sensing constraints. Full article

This paper presents a discrete-time, centralized fractional-order linear quadratic regulator FOLQR for automatic generation control (AGC) of three-area interconnected nonreheat thermal systems. The AGC state explicitly includes the area control error (ACE) and tie-line power; a quadratic performance index penalizes ACE, its integral (IACE), and control effort. The continuous-time plant (governor–turbine dynamics and tie-line flows) is discretized at a fixed sampling interval, and a single centralized gain is obtained from the discrete algebraic Riccati equation; the fractional-order extension shapes memory in the feedback to temper rapid transients. Benchmark studies under 0.01 and 0.05 p.u. step-load disturbances show that FOLQR stabilizes the interconnection and consistently lowers peak excursions relative to a conventional discrete LQR (COQAGC) baseline—reducing frequency peaks by about 9–12% and tie-line peaks by 24–60% in the small-step case—while producing smoother actuator commands. Although FOLQR exhibits longer settling times, this trade-off is acceptable FOr multi-area AGC where limiting overshoot and tie-line excursions is operationally more critical than strict settling-time targets. The proposed controller retains a simple centralized, discrete-time structure with a modest computational burden, making it suitable FOr real-time AGC deployment in large interconnected grids and demonstrating for the first time, to our knowledge, a fractional-order LQR applied to a three-area thermal benchmark. Full article

This paper introduces a novel control archetype designed to mitigate high-altitude electromagnetic pulse (HEMP) $E_3$ disturbances on the power grid, as well as information on performance and specifications of different control laws for the controller archetype. This method of protection has been overlooked in the literature until now. A controlled voltage supply is placed on the load-side of a transformer, diverting unwanted power from the transformer core to prevent saturation. The controlled voltage source is modeled using four control laws: an integral controller (capacitor), Linear Quadratic Regulator (LQR), an energy storage minimized feedforward control law, and a Hamiltonian feedback law. Results show that the Hamiltonian feedback law and the energy storage minimization feedforward control law both flat-line magnetic flux with similar actuator requirements. The LQR approach requires less energy storage than the other two laws, depending on control tuning, as it allows greater exogenous current flow through the neutral path to ground. This leads to further optimization opportunities based on acceptable exogenous current levels. A sweep of different LQR gains revealed a reduction of approximately $32\%$ in minimum control effort, $47\%$ in minimum power to maintain saturation bounds, $20\%$ in energy storage requirements, and $59\%$ in required controller bandwidth. Voltage and bandwidth requirements of the load-side controller are comparable to neutral blocking requirements with energy and power requirements being higher for the load-side controller. This, however, comes with the benefit of being able to use pre-existing assets—neutral blocking devices have not been deployed. Additionally, the load-side blocking capacitor degrades transformer performance compared to the unmitigated system. Full article

Small flexible-wing aircraft are vulnerable to gusts due to their low inertia and operating regime at low-Reynolds-number regimes, compromising flight stability and mission reliability. This paper introduces a novel active gust alleviation device (AGAD) installed at the wingtip, which works in concert with the conventional tail-plane to form a multi-surface control system. To coordinate these surfaces optimally, a quasi-static aeroelastic aircraft model is established, and a linear–quadratic regulator (LQR) controller is designed. A key innovation is the integration of an extended state observer (ESO) to estimate the unmeasurable, gust-induced angle of attack in real time, allowing the LQR to effectively counteract unsteady disturbances. Comparative simulations against a baseline (tail-plane-only control) demonstrate the superiority of the combined AGAD-tail strategy: the peak gust responses in pitch angle and normal acceleration are reduced by over 57% and 20%, respectively, while structural loads at the wing root are also significantly attenuated. Furthermore, the AGAD enhances maneuverability, reducing climb time by 20% during a specified maneuver. This study confirms that the integrated AGAD and LQR-ESO framework provides a practical and effective solution for enhancing both the stability and agility of small flexible aircraft in gusty environments, with direct benefits for applications like precision inspection and monitoring. 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 addresses the problem of stabilizing an inverted pendulum actuated by a reaction wheel, a system relevant for robotic balancing platforms and aerospace applications. The study compares several state-space representations of the system and examines their implications for controller synthesis and parameter identification. A unified nonlinear model formulation is introduced, enabling a structural Lyapunov-based robustness analysis that reveals how variations in the gravitational gain affect closed-loop stability. Control strategies based on pole placement and Linear Quadratic Regulator (LQR) design are implemented and compared across the different representations. The analysis highlights a robustness–fidelity trade-off between model complexity and sensitivity to parameter uncertainty, providing insight that extends beyond the specific laboratory setup. Theoretical results are validated on a real laboratory platform. The controllers are evaluated in both upright and downward equilibrium configurations, and the influence of parameter shifts is assessed experimentally using global identification and performance indices. The work offers general modeling and robustness guidelines for reaction-wheel-based stabilization systems and related underactuated nonlinear mechanisms. Full article