Search Results (79)

This paper explores the optimal control issue for a linear–quadratic–Gaussian (LQG) system under the conditions of imperfect feedback and constraints related to energy harvesting. The system is equipped with various energy options, which allow it to gather energy for information transmission while also receiving imperfect feedback from an auxiliary filter that estimates packet loss. The primary goal of this study is to jointly design the energy selector and the controller to achieve an optimal balance between transmission costs and control performance. Initially, we separate the controller’s synthesis task from the energy selection task. The subproblem of optimal controller synthesis is characterized by a Riccati equation that takes continuous packet loss into account. Simultaneously, the energy selection task, influenced by imperfect feedback and constraints on energy costs, is reformulated as a Markov decision process (MDP) that operates with perfect acknowledgments through iterative updates of state information. Ultimately, the optimal energy selection policy that guarantees filtering performance is derived by solving a Bellman equation. The effectiveness of the proposed approach is confirmed through simulation results. Full article

This paper presents the design and evaluation of a Linear Time-Varying Linear Quadratic Gaussian (LTV-LQG) controller for an energy efficient electric vehicle, using a predetermined driving strategy as the reference trajectory. The proposed approach begins with the development of a structured nonlinear vehicle model based on relevant subsystems, enabling accurate energy consumption estimation with a deviation of less than 2% from experimental measurements. This model serves as the basis for computing a near-optimal driving trajectory. The nonlinear model is linearized along the predefined trajectory to support control design. A time-varying control structure is then developed, integrating a Kalman filter that estimates unmeasured external disturbances, such as wind, and enhances feedback performance. The proposed control strategy is evaluated through simulations and compared to a rule-based switching controller that replicates human-like driving behavior. The simulation results demonstrate that the LTV-LQG controller consistently satisfies the time constraints in both headwind- and tailwind-dominant scenarios, where the switching controller tends to exceed the time limit. Moreover, in tailwind-dominant cases, the LTV-LQG controller achieves lower energy consumption (up to 15.4%). The proposed framework represents a computationally efficient and practically feasible control solution for electric vehicles operating under realistic disturbance conditions. Full article

Electromagneticsoft actuator arrays (ESAAs) combine compliance with fast, controllable actuation and scalability, providing a promising foundation for the development of interconnected soft actuator arrays inspired by the structure and function of biological muscles. In this work, we present a control framework and an actuator selection strategy for an artificial soft muscle composed of ESAAs to enable accurate reference tracking. Since directly measuring the states of each ESA is often impractical in real-world applications, we first design a Kalman filter-based observer to estimate all system states from available observations. Using these estimates, we develop a Linear Quadratic Gaussian (LQG) controller to achieve reference tracking. Since thermal buildup from constant use can damage the actuators, we consider whether switching between different subsets of active actuators could offer thermal relief. While actuator switching intuitively suggests reduced heating by providing resting periods, our investigation reveals that this strategy can lead to higher thermal accumulation compared to the continuous mode. This is because we need substantially larger control effort when we have fewer active actuators in the switching mode, which, in the absence of effective active cooling, fail to provide sufficient heat dissipation during operation. Simulation results are presented to demonstrate the effectiveness of the proposed method in achieving the trajectory objective and to explore how switching affects the system’s thermal profile, revealing a trade-off between tracking performance and heat generation. Full article

The active or adjustable journal bearings are designed with unique mechanisms to reduce the rotor-bearing system lateral vibrations by adjusting their damping and stiffness. The article provides a comprehensive review of the literature, outlining the structure and findings of studies on active bearings. Over the years, various kinds of adjustable bearing designs have been developed with unique operational mechanisms. Such bearing designs include adjustable pad sectors, externally adjustable pads, active oil injection through pad openings, and flexible deformable sleeves. These modifications enhance the turbine shaft line’s performance by increasing the system’s overall stability. The detailed review in this paper highlights the characteristics of bearings, along with the key advantages, limitations, and potential offered by active control across different bearing types. The efficiency of any rotor system can be greatly enhanced by optimally selecting the adjustable bearing parameters. These adjustable bearings have demonstrated a unique capability to modify the hydrodynamic operation within the bearing clearances. Experimental studies and simulation approaches were also utilized to optimize bearing geometries, lubrication regimes, and control mechanisms. The use of advanced controllers like PID, LQG, and Deep Q networks further refined the stability. The concluding section of the article explores potential avenues for the future development of active bearings. Full article

This work presents a semi-classical, quantum-corrected model of gravitational collapse for a charged, spherically symmetric dust cloud, extending the classical Oppenheimer–Snyder (OS) framework through loop quantum gravity effects. Our goal is to study phenomenological quantum modifications to geometry, without necessarily embedding them within full loop quantum gravity (LQG). Building upon the quantum Oppenheimer–Snyder (qOS) model, which replaces the classical singularity with a nonsingular bounce via a modified Friedmann equation, we introduce electric and magnetic charges concentrated on a massive thin shell at the boundary of the dust ball. The resulting exterior spacetime generalizes the Schwarzschild solution to a charged, regular black hole geometry akin to a quantum-corrected Reissner–Nordström metric. The Israel junction conditions are applied to match the interior APS (Ashtekar–Pawlowski–Singh) cosmological solution to the charged exterior, yielding constraints on the shell’s mass, pressure, and energy. Stability conditions are derived, including a minimum radius preventing full collapse and ensuring positivity of energy density. This study also examines the geodesic structure around the black hole, focusing on null circular orbits and effective potentials, with implications for the observational signatures of such quantum-corrected compact objects. Full article

High-precision time–frequency systems are essential for low Earth orbit (LEO) navigation satellites to achieve real-time (RT) centimeter-level positioning services. However, subject to stringent size, power, and cost constraints, LEO satellites are typically equipped with oven-controlled crystal oscillators (OCXOs) as the system clock. The inherent long-term stability of OCXOs leads to rapid clock error accumulation, severely degrading positioning accuracy. To simultaneously balance multi-dimensional requirements such as clock bias accuracy, and frequency stability and phase continuity, this study proposes a linear quadratic Gaussian (LQG) frequency precision steering method that integrates a four-dimensional constraint integrated (FDCI) model and hierarchical weight optimization. An improved system error model is refined to quantify the covariance components (Σ₁₁, Σ₂₂) of the LQG closed-loop control system. Then, based on the FDCI model that explicitly incorporates quantization noise, frequency adjustment, frequency stability, and clock bias variance, a priority-driven collaborative optimization mechanism systematically determines the weight matrices, ensuring a robust tradeoff among multiple performance criteria. Experiments on OCXO payload products, with micro-step actuation, demonstrate that the proposed method reduces the clock error RMS to 0.14 ns and achieves multi-timescale stability enhancement. The short-to-long-term frequency stability reaches 9.38 × 10⁻¹³ at 100 s, and long-term frequency stability is 4.22 × 10⁻¹⁴ at 10,000 s, representing three orders of magnitude enhancement over a free-running OCXO. Compared to conventional PID control (clock bias RMS 0.38 ns) and pure Kalman filtering (stability 6.1 × 10⁻¹³ at 10,000 s), the proposed method reduces clock bias by 37% and improves stability by 93%. The impact of quantization noise on short-term stability (1–40 s) is contained within 13%. The principal novelty arises from the systematic integration of theoretical constraints and performance optimization within a unified framework. This approach comprehensively enhances the time–frequency performance of OCXOs, providing a low-cost, high-precision timing–frequency reference solution for LEO satellites. Full article

We investigated the thermodynamic topology of quantum-corrected AdS-Reissner-Nordström black holes in Kiselev spacetime using non-extensive entropy formulation derived from Loop Quantum Gravity (LQG). Through systematic analysis, we examined how the Tsallis parameter $\lambda$ influences topological charge classification with respect to various equation of state parameters. Our findings revealed a consistent pattern of topological transitions: for $\lambda =0.1$, the system exhibited a single topological charge $(\omega =-1)$ with total charge $W=-1$, as $\lambda$ increased to 0.8, the system transitioned to a configuration with two topological charges $(\omega =+1,-1)$ and total charge $W=0$. When $\lambda =1$, corresponding to the Bekenstein–Hawking entropy limit, the system displayed a single topological charge $(\omega =+1)$ with $W=+1$, signifying thermodynamic stability. The persistence of this pattern across different fluid compositions—from exotic negative pressure environments to radiation—demonstrates the universal nature of quantum gravitational effects on black hole topology. Full article

This paper aims to solve the problems of poor mobility, passability, and stability in heavy-duty vehicles, and proposes an active suspension system control strategy based on position–force feedback optimal control to coordinately enhance vehicle passability and wheel grounding performance. Firstly, a two-degrees-of-freedom one-sixth vehicle active suspension model and a valve-controlled hydraulic actuator system model are constructed, and the advantages of impedance control in robot compliance control are integrated to analyze their applicability in hydraulic active suspension. Next, a position feedback controller and force feedback LQG optimal controller for fuzzy PID control are designed, the fuzzy PID-LQG (FPL) integrated method is applied to the hydraulic active suspension system, and the dynamic load of the wheel is tracked by impedance control to obtain the spring mass displacement correction. Then, a suspension system model under the excitation of a C-class road surface and a 0.11 m raised road surface is constructed, and the dynamic simulation and comparison of active/passive suspension systems are carried out. The results show that, compared with PS and LQR control, the body vertical acceleration, suspension dynamic deflection, and wheel dynamic load root-mean-square value of the proposed FPL integrated control active suspension are reduced, which can effectively reduce the body vibration and wheel dynamic load and meet the design objectives proposed in this paper, effectively improving vehicle ride comfort, handling stability, passability, and wheel grounding performance. Full article

Background: The sense of agency (SoA)—the subjective experience of controlling one’s own actions and their consequences—is a fundamental aspect of human cognition, volition, and motor control. Understanding how the SoA arises and is disrupted in neuropsychiatric disorders has significant implications for human–machine interface (HMI) design for neurorehabilitation. Traditional cognitive models of agency often fail to capture its full complexity, especially in dynamic and uncertain environments. Objective: This review synthesizes computational models—particularly predictive coding, Bayesian inference, and optimal control theories—to provide a unified framework for understanding the SoA in both healthy and dysfunctional brains. It aims to demonstrate how these models can inform the design of adaptive HMIs and therapeutic tools by aligning with the brain’s own inference and control mechanisms. Methods: I reviewed the foundational and contemporary literature on predictive coding, Kalman filtering, the Linear–Quadratic–Gaussian (LQG) control framework, and active inference. I explored their integration with neurophysiological mechanisms, focusing on the somato-cognitive action network (SCAN) and its role in sensorimotor integration, intention encoding, and the judgment of agency. Case studies, simulations, and XR-based rehabilitation paradigms using robotic haptics were used to illustrate theoretical concepts. Results: The SoA emerges from hierarchical inference processes that combine top–down motor intentions with bottom–up sensory feedback. Predictive coding frameworks, especially when implemented via Kalman filters and LQG control, provide a mechanistic basis for modeling motor learning, error correction, and adaptive control. Disruptions in these inference processes underlie symptoms in disorders such as functional movement disorder. XR-based interventions using robotic interfaces can restore the SoA by modulating sensory precision and motor predictions through adaptive feedback and suggestion. Computer simulations demonstrate how internal models, and hypnotic suggestions influence state estimation, motor execution, and the recovery of agency. Conclusions: Predictive coding and active inference offer a powerful computational framework for understanding and enhancing the SoA in health and disease. The SCAN system serves as a neural hub for integrating motor plans with cognitive and affective processes. Future work should explore the real-time modulation of agency via biofeedback, simulation, and SCAN-targeted non-invasive brain stimulation. Full article

This study investigates the effect of active control on a quarter-vehicle suspension system. The car suspension system was modeled using the Lagrange–Euler method. The linear quadratic Gaussian (LQG) and fuzzy linear quadratic Gaussian (FLQG) control methods were designed and used for active control to increase vehicle handling and passenger comfort, with the aim of reducing or eliminating vibrations by performing active control of passive suspension systems using these methods. The optimum values of the coefficients of the points where the membership functions of the LQG and Fuzzy LQG methods touch were obtained using the grey wolf optimization (GWO) algorithm. The success of the control performance rate of the applied methods was compared based on the passive suspension system. In addition, the obtained results were compared with each other and with other studies using the integral time-weighted absolute error (ITAE) performance criterion. The proposed control method yielded significant improvements in vehicle parameters compared with the passive suspension system. Vehicle body movement, vehicle acceleration, suspension deflection, and tire deflection improved by approximately 88.2%, 91.5%, 88%, and 89.4%, respectively. Thus, vehicle driving comfort was significantly enhanced based on the proposed system. Full article

Terrestrial and satellite communications, tactical data links, positioning, navigation, and timing (PNT), as well as distributed sensing will continue to require precise timing and the ability to synchronize and disseminate time effectively. However, the supply of space-qualified clocks that meet Global Navigation Satellite Systems (GNSS)-level performance standards is limited. As the awareness of potential disruptions to GNSS due to adversarial actions grows, the current reliance on GNSS-level timing appears costly and outdated. This is especially relevant given the benefits of developing robust and stable time scale references in orbit, especially as various alternatives to GNSS are being explored. The onboard realization of clock ensembles is particularly promising for applications such as those providing the on-demand dissemination of a reference time scale for navigation services via a proliferated Low-Earth Orbit (pLEO) constellation. This article investigates potential inter-satellite network architectures for coordinating time and frequency across pLEO platforms. These architectures dynamically allocate radio resources for clock data transport based on the requirements for pLEO time scale formations. Additionally, this work proposes a model-based control system for wireless networked timekeeping systems. It envisions the optimal placement of critical information concerning the implicit ensemble mean (IEM) estimation across a multi-platform clock ensemble, which can offer better stability than relying on any single ensemble member. This approach aims to reduce data traffic flexibly. By making the IEM estimation sensor more intelligent and running it on the anchor platform while also optimizing the steering of remote frequency standards on participating platforms, the networked control system can better predict the future behavior of local reference clocks paired with low-noise oscillators. This system would then send precise IEM estimation information at critical moments to ensure a common pLEO time scale is realized across all participating platforms. Clock steering is essential for establishing these time scales, and the effectiveness of the realization depends on the selected control intervals and steering techniques. To enhance performance reliability beyond what the existing Linear Quadratic Gaussian (LQG) control technique can provide, the minimal-cost-variance (MCV) control theory is proposed for clock steering operations. The steering process enabled by the MCV control technique significantly impacts the overall performance reliability of the time scale, which is generated by the onboard ensemble of compact, lightweight, and low-power clocks. This is achieved by minimizing the variance of the chi-squared random performance of LQG control while maintaining a constraint on its mean. Full article

We consider a dynamic game with asymmetric information where each player privately observes a noisy version of a (hidden) state of the world V, resulting in dependent private observations. We study the structured perfect Bayesian equilibria (PBEs) that use private beliefs in their strategies as sufficient statistics for summarizing their observation history. The main difficulty in finding the appropriate sufficient statistic (state) for the structured strategies arises from the fact that players need to construct (private) beliefs on other players’ private beliefs on V, which, in turn, would imply that one needs to construct an infinite hierarchy of beliefs, thus rendering the problem unsolvable. We show that this is not the case: each player’s belief on other players’ beliefs on V can be characterized by her own belief on V and some appropriately defined public belief. We then specialize this setting to the case of a Linear Quadratic Gaussian (LQG) non-zero-sum game, and we characterize structured PBEs with linear strategies that can be found through a backward/forward algorithm akin to dynamic programming for the standard LQG control problem. Unlike the standard LQG problem, however, some of the required quantities for the Kalman filter are observation-dependent and, thus, cannot be evaluated offline through a forward recursion. Full article

Hybrid energy storage systems (HESSs) are essential for adopting sustainable energy sources. HESSs combine complementary storage technologies, such as batteries and supercapacitors, to optimize efficiency, grid stability, and demand management. This work proposes a semi-active HESS formed by a battery connected to the DC bus and a supercapacitor managed by a Sepic/Zeta converter, which has the aim of avoiding high-frequency variations in the battery current on any operation condition. The converter control structure is formed by an LQG controller, an optimal state observer, and an adaptive strategy to ensure the correct controller operation in any condition: step-up, step-down, and unitary gain. This adaptive LQG controller consists of two control loops, an internal current loop and an external voltage loop, which use only two sensors. Compared with classical PI and LQG controllers, the adaptive LQG solution exhibits a better performance in all operation modes, up to 68% better than the LQG controller and up to 84% better than the PI controller. Therefore, the control strategy proposed for this HESS provides a fast-tracking of DC-bus current, driving the high-frequency component to the supercapacitor and the low-frequency component to the battery. Thus, fast changes in the battery power are avoided, reducing the degradation. Finally, the system adaptability to changes up to 67% in the operation range are experimentally tested, and the implementation of the control system using commercial hardware is verified. Full article

Unmanned aerial vehicles (UAVs) are a new, adaptable technology that has found its way into both military and civilian applications. Preserving the integrity of the UAV and its security during flight and, in particular, during the landing stage is essential for the performance of the assigned mission of the aircraft. This research examines a developed aircraft scheme. It was tested for static and dynamic stability in an XFLR5 virtual aerodynamic environment. The obtained results were transferred to MATLAB-Simulink, where the flight control algorithm was synthesized, the landing mode was set using an engineering flight plan, and an autonomous landing was simulated in the presence of wind disturbances with turbulence and noisy operation of the information measurement complex of the UAV. The algorithm for controlling the landing during the execution of the set flight trajectory, which contains a Kalman estimator and an optimal LQR controller combined in a general LQG control algorithm, is studied. Full article

This paper introduces a Linear Quadratic Gaussian (LQG) controller for a Single-Ended Primary Inductor Converter (SEPIC). The LQG design is based on merging an integral Linear Quadratic Regulator (LQR) with an offline Kalman Filter (commonly referred to as a Linear Quadratic Estimator (LQE)). The robustness of the LQG controller is guaranteed based on the separation principle. This manuscript addresses the need to use observer-based systems for the fourth-order SEPIC, which needs a sensor reduction as an essential requirement. This paper provides a comprehensive, yet systematic, approach to designing the LQG system. The work validates the convergences of the states in an LQG system to an actual value. Furthermore, it compares the performance of an LQG system with a benchmark Type-II industrial controller by means of a simulation of the switched converter model in the Simulink/MATLAB 2023a environment. Full article