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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. [ABSTRACT FROM AUTHOR]

Redundancies in modern systems, including multiple channels, processes, and storages, are often exploited to ensure robust operation. Similarly, a Networked Control System (NCS) may utilize multiple channels to facilitate reliable information transfer in case of channel failure. To enhance the performance of Linear Quadratic Gaussian (LQG) control in environments with multiple channels, delays, and packet errors, we propose channel-switching algorithms. Leveraging the encoder and decoder structure for channel modeling, we derive the decoder estimation error covariance matrix, characterizing LQG control performance with respect to delay. Based on this insight, we develop two threshold-based channel-switching algorithms, proven to ensure finite total decoder estimation error variance under certain conditions. Specific conditions are also identified where the proposed algorithms offer improved probabilistic stability. Numerical simulations confirm the superior performance of the proposed algorithms compared to conventional methods across diverse channel environments. Notably, the proposed algorithms demonstrate near-optimal performance in a practical operational scenario involving multiple channels, specifically 5G cellular link and Starlink. [ABSTRACT FROM AUTHOR]

This paper proposes a Linear Quadratic Gaussian (LQG) control design for a grid-connected and Islanded mode Microgrid composed of a single-network feeding and forming converter with one local load. The LQG controller was designed for two different Microgrid modes: Grid-connected mode and islanded mode. A separate LQG controller was designed for each mode and a comparative analysis was made. The LQG controller was designed using the State-Space variables determined by linearizing the model. The controller consists of the optimal gain 'K', optimal Linear Quadratic Regulator (LQR), and the Kalman Filter. In both Microgrid modes, the LQG eliminates disturbance and noise in the system and makes the system optimally controlled. The Microgrid system also consists of another control system that comprises the subsequent control subsystem, i.e., Alpha–Beta control, Power and Current loop, and Space Vector Modulation. The steady-state response of the Microgrid system, noise, and disturbance present in Grid-connected and islanded modes was rectified by the LQG controller. The design environment used for developing the Microgrid and LQG controller was the MATLAB/Simulink platform. The effective simulations have permitted and determined results that convey the optimal control and stable performance of the proposed system. [ABSTRACT FROM AUTHOR]

In recent years, the development of hydraulic variable displacement axial piston machines has been focused in two main directions: improvement of their construction and improvement of their displacement control methods. The goal of both directions is to increase the efficiency of the machines. Increasing their efficiency is key to improving the efficiency of the entire hydraulic system, whether they are used as pumps or hydraulic motors. This motivates the present work, which essentially contains a developed embedded control system designed for a known type of open circuit axial piston pump. The developed solution is implemented on a laboratory test rig. A detailed description of the hydraulic system in the context of pump displacement control is presented, as well as the developed system architecture for its control. The control system is based on a linear-quadratic Gaussian (LQG) controller. The controller is synthesized on the basis of a model obtained by means of identification based on experimental data. The designed controller is validated through experimental studies, enabling the analysis of its performance. [ABSTRACT FROM AUTHOR]

This paper studies the agent identity privacy problem in the scalar linear quadratic Gaussian (LQG) control system. The agent identity is a binary hypothesis: Agent A or Agent B. An eavesdropper is assumed to make a hypothesis testing the agent identity based on the intercepted environment state sequence. The privacy risk is measured by the Kullback–Leibler divergence between the probability distributions of state sequences under two hypotheses. By taking into account both the accumulative control reward and privacy risk, an optimization problem of the policy of Agent B is formulated. This paper shows that the optimal deterministic privacy-preserving LQG policy of Agent B is a linear mapping. A sufficient condition is given to guarantee that the optimal deterministic privacy-preserving policy is time-invariant in the asymptotic regime. It is also shown that adding an independent Gaussian random process noise to the linear mapping of the optimal deterministic privacy-preserving policy cannot improve the performance of Agent B. The numerical experiments justify the theoretic results and illustrate the reward–privacy trade-off. [ABSTRACT FROM AUTHOR]

In the paper, the Linear–Quadratic–Gaussian (LQG) control strategy in regulatory mode (disturbance attenuation, zero value of the reference signal) in single-loop control is used to stabilize the system equipped in a non-integer order plant. The influence of the optimal controller design sophistication on control quality in terms of output variance is examined. It has been shown that the optimal implementation length of fractional-order difference is relatively low (several dozen in considered examples). Therefore, further increasing the controller's complexity in terms of approximation length does not improve the control performance. Furthermore, it is presented that, under bounded control signal variance, the optimal fractional order of the controller may be significantly different from the actual fractional order of the plant (in the examples, the difference is up to 0.66). [ABSTRACT FROM AUTHOR]

A closed-form H2 approach of a nonlinear trajectory tracking design and practical implementation of a swarm of wheeled mobile robots (WMRs) is presented in this paper. For the nonlinear trajectory tracking problem of a swarm of WMRs, the design purpose is to point out a closed-form H2 nonlinear control method that analytically fulfills the H2 control performance index. The key and primary contribution of this research is a closed-form solution with a simple control structure for the trajectory tracking design of a swarm of WMRs is an absolute achievement and practical implementation. Generally, it is challenging to solve and find out the closed-form solution for this nonlinear trajectory tracking problem of a swarm of WMRs. Fortunately, through a sequence of mathematical operations for the trajectory tracking error dynamics between the control of a swarm of WMRs and desired trajectories, this H2 trajectory tracking problem is equal to solve the nonlinear time-varying Riccati-like equation. Additionally, the closed-form solution of this nonlinear time-varying Riccati-like equation will be acquired with a straightforward form. Finally, for simulation-controlled performance of this H2 proposed method, two testing scenarios, circular and S type reference trajectories, were applied to performance verification. [ABSTRACT FROM AUTHOR]

Large-scale binary integer programs occur frequently in many real-world applications. For some binary integer problems, finding an optimal solution or even a feasible solution is computationally expensive. In this paper, we develop a discrete meta-control procedure to approximately solve large-scale binary integer programs efficiently. The key idea is to map the vector of n binary decision variables into a scalar function defined over a time interval [0; n] and construct a linear quadratic tracking (LQT) problem that can be solved efficiently. We prove that an LQT formulation has an optimal binary solution, analogous to a classical bang-bang control in continuous time. Our LQT approach can provide advantages in reducing computation while generating a good approximate solution. Numerical examples are presented to demonstrate the usefulness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper addresses an H 2 optimal control problem for a class of discrete-time stochastic systems with Markov jump parameter and multiplicative noises. The involved Markov jump parameter is a uniform ergodic Markov chain taking values in a Borel-measurable set. In the presence of exogenous white noise disturbance, Gramian characterization is derived for the H 2 norm, which quantifies the stationary variance of output response for the considered systems. Moreover, under the condition that full information of the system state is accessible to measurement, an H 2 dynamic optimal control problem is shown to be solved by a zero-order stabilizing feedback controller, which can be represented in terms of the stabilizing solution to a set of coupled stochastic algebraic Riccati equations. Finally, an iterative algorithm is provided to get the approximate solution of the obtained Riccati equations, and a numerical example illustrates the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents the application of an linear quadratic gaussian (LQG) control strategy for concrete caisson deployment for marine structures. Currently these maneuvers are carried out manually with the risk that this entails. Control systems for these operations with classical regulators have begun to be implemented. They try to reduce risks, but they still need to be optimized due to the complexity of the dynamics involved during the sinking process and the contact with the sea bed. A linear approximation of the dynamic model of the caisson is obtained and an LQG control strategy is implemented based on the Kalman filter (KF). The results of the proposed LQG control strategy are compared to the ones given by a classic controller. It is noted that the proposed system is positioned with greater precision and accuracy, as shown in the different simulations and in the Monte Carlo study. Furthermore, the control efforts are less than with classical regulators. For all the reasons cited above, it is concluded that there is a clear improvement in performance with the control system proposed. [ABSTRACT FROM AUTHOR]

In order to further improve driving comfort, this paper takes the semi-vehicle active suspension as the research object. Furthermore, combined with a 5-DOF driver-seat model, a new 9-DOF driver seat-active suspension model is proposed. The adaptive Kalman filter combined with L2 feedback control algorithm is used to improve the controller. First, a discrete 9-DOF driver seat-active suspension model is established. Then, the L2 feedback algorithm is used to solve the optimal feedback matrix of the model, and the adaptive Kalman filter algorithm is used to replace the linear Kalman filter. Finally, the improved active suspension model and algorithm are verified through simulation and test. The results show that the new algorithm and model not only significantly improve the driver comfort, but also comprehensively optimize the other performance of the vehicle. Compared with the traditional LQG control algorithm, the RMS value of the acceleration experienced by the driver's limb are, respectively, decreased by 10.9%, 15.9%, 6.4%, and 7.5%. The RMS value of pitch angle acceleration experienced by the driver decreased by 6.4%, and the RMS value of the dynamic tire deflection of front and rear tire decreased by 32.6% and 12.1%, respectively. [ABSTRACT FROM AUTHOR]

The problem of reliable control for variable fault systems under linear quadratic Gaussian (LQG) framework is studied in this paper. Firstly, a cluster of models is used to cover the dynamic behaviors of different fault modes of a system and, for each model, LQG control is implemented. By using the a posteriori probability of model innovation as the weight information, a multi-model reliable control (MMRC) is proposed. Secondly, it is proved that MMRC can enable the controller to learn the real operating mode of the system. When the controller is in a deadlock state, a deadlock avoidance strategy is given and its convergence of the a posteriori probability is proved. Finally, the validity of MMRC is verified by an example simulation of the lateral-directional control system of an aircraft. Simulation results show that MMRC guarantees an acceptable performance of the closed-loop system. In addition, since the controller fuses the control law of each model according to the weight information, when the system model is switched, the controller implements a soft switching, which avoids the jitter caused by frequent hard switching to the system. [ABSTRACT FROM AUTHOR]

This paper presents the design of a linear quadratic (LQ) optimal controller for a spar-type floating offshore wind turbine (FOWT). The FOWT is exposed to different sea states and constant wind turbulence intensity above rated wind speed. A new LQ control objective is specified for the floater-turbine coupled control, in accordance with standard requirements, to reduce both rotor speed fluctuations and floater pitch motion in each relevant sea state compared with a baseline proportional-integral (PI) controller. The LQ weighting matrices are selected using time series of the wind/wave disturbances generated for the relevant sea states. A linearized state-space model is developed, including the floater surge/pitch motions, rotor speed, collective blade pitch actuation, and unmeasured environmental disturbances. The wind disturbance modeling is based on the Kaimal spectrum and aerodynamic thrust/torque coefficients. The wave disturbance modeling is based on the Pierson–Moskowitz spectrum and linearized Morison equation. A high-fidelity FOWT simulator is used to verify the control-oriented model. The simulation results for the OC3-Hywind FOWT subjected to turbulent wind show that a single LQ controller can yield both rotor speed fluctuation reduction of 32–72% and floater pitch motion reduction of 22–44% in moderate to very rough sea states compared with the baseline PI controller. [ABSTRACT FROM AUTHOR]

An adaptive ship steering system along a preset track is an example of an intelligent system. An optimal linear quadratic regulator (LQR) regulator with a symmetric indicator of control quality was adopted as the control algorithm. The model identification was based on the continuous version of the least squares method. A significant part of the article presents the proof of the stability of the proposed system. The results of the calculation experiments are provided to confirm the effective and correct working of the system. [ABSTRACT FROM AUTHOR]

With the increasing proportion of renewable energy in microgrids (MGs), its stochastic fluctuation of output power has posed challenges to system safety and operation, especially frequency stability. Virtual synchronous generator (VSG) technology, as one effective method, was used to smoothen frequency fluctuation and improve the system's dynamic performance, which can simulate the inertia and damping of the traditional synchronous generator. This study outlines the integration of VSG-controlled energy storage systems (ESSs) and traditional synchronous generators so they jointly participate in secondary frequency regulation in an independent MG. Firstly, a new uncertain state-space model for secondary frequency control is established, considering the measurement noises and modelling error. Then, an improved linear quadratic Gaussian (LQG) controller is designed based on stochastic optimal control theory, in which the dynamic performance index weighting matrices are optimized by combining loop transfer recovery (LTR) technology and the distribution estimation algorithm. On the issue of secondary frequency devices' output power allocation, the dynamic participation factors based on the ESS's current state of charge (SOC) are proposed to prevent the batteries' overcharging and overdischarging problems. The energy storage devices' service life can be prolonged and OPEX (operational expenditure) decreased. Multiple experimental scenarios with real parameters of MGs are employed to evaluate the performance of the proposed algorithm. The results show that, compared with the lead-compensated-proportional-integral-derivative (LC-PID) control and robust μ-control algorithms, the proposed stochastic optimal control method has a faster dynamic response and is more robust, and the fluctuations from renewable energy and power loads can be smoothened more effectively. [ABSTRACT FROM AUTHOR]

Featured Application: Submersed obstacle avoidance in unknown ocean currents via guidance, navigation, and control for autonomous underwater vehicles. A considerable volume of research has recently blossomed in the literature on autonomous underwater vehicles accepting recent developments in mathematical modeling and system identification; pitch control; information filtering and active sensing, including inductive sensors of ELF emissions and also optical sensor arrays for position, velocity, and orientation detection; grid navigation algorithms; and dynamic obstacle avoidance, amongst others. In light of these modern developments, this article develops and compares integrative guidance, navigation, and control methodologies for the Naval Postgraduate School's Phoenix submerged autonomous vehicle, where these methods are assumed available. The measure of merit reveals how well each of several proposed methodologies cope with known and unknown disturbances, such as currents that can be constant or harmonic, while maintaining a safe passage distance from underwater obstacles, in this case submerged mines. Classical pole-placement designs establish nominal baseline behaviors and are subsequently compared to performance of designs that are optimized to satisfy linear quadratic cost functions in regulators as well as linear-quadratic Gaussian designs. Feed-forward architectures and integral control designs are also evaluated. A noteworthy contribution is a very simple method to mimic optimal results with a "rule of thumb" criteria based on the design's time constant. Since the rule-of-thumb method uses the assumed system model for computation of the control, it is particularly generic. Cited references each contain methods for online system parameter identification (with a motivation of use in the finding the control signal), permitting the rule of thumb's generic applicability, since it is expressed in terms of the system parameters. This proposed method permits control design at sea where significant computation abilities are not available. Very simple waypoint guidance is also introduced to guide a vehicle along a preplanned path through a field of obstacles placed at random locations. The linear-quadratic Gaussian design proves best when augmented with integral control, and works well with reduced-order equations, while the "rule of thumb" design is seen to closely mimic the optimal performance. Feed-forward augmentation proves particularly efficient at rejecting constant disturbances, while augmentation with integral control is necessary to counter periodic disturbances, where the augmentations are also optimized in the linear-quadratic Gaussian procedures, yet can be closely mimicked by the proposed "rule of thumb" technique. [ABSTRACT FROM AUTHOR]