This article solves the decentralized optimal control problem for It & ocirc; stochastic systems where two controllers can access different information sets due to transmission delay. In contrast to the existing literature, the dynamics of the system involves two controllers with different information structure, which poses challenges in solving the optimal control strategy. In this paper, by adopting variational methods, the necessary and sufficient solvability conditions in terms of delayed forward and backward stochastic differential equations (D-FBSDEs) are derived for the problem considered. Furthermore, by decoupling these D-FBSDEs, a novel decentralized control strategy is derived. Finally, a numerical example demonstrates the effectiveness of the proposed methods and results.

A pursuit-evasion problem for unmanned underwater vehicles (UUVs) under a nested asymmetric information structure is investigated in this paper within a finite-horizon non-zero-sum linear quadratic Gaussian (LQG) framework. In the considered scenario, the pursuer has access to more complete state information than the evader, which makes the derivation of Nash equilibrium strategies more challenging than in the conventional symmetric-information setting. To formulate the problem, the nonlinear UUV dynamics are linearized and discretized, and the interaction between the pursuer and the evader is modeled as a discrete-time stochastic dynamic game with distinct information filtrations. Based on the stochastic maximum principle, the necessary and sufficient conditions for the existence of a Nash equilibrium are established in the form of forward-backward stochastic difference equations (FBSDEs). To address the structural coupling caused by the nested information pattern, an orthogonal decomposition method is proposed, through which the coupled equations can be decoupled and explicit affine-feedback equilibrium strategies can be derived. Numerical simulations based on the REMUS 100 UUV model are provided to demonstrate the effectiveness of the proposed method.

Precise trajectory tracking control is regarded as critical for the autonomous operation of unmanned underwater vehicles (UUVs). However, performance degradation is often observed in traditional model-based control strategies due to the difficulty in obtaining accurate hydrodynamic coefficients and the presence of complex environmental disturbances. To address this issue, a direct data-driven control method based on data-enabled policy optimization (DeePO) is adopted to solve the linear-quadratic (LQ) optimal trajectory tracking problem for UUVs. First, the nonlinear kinematic and dynamic models of the UUV are established and linearized around the operating point to obtain the error dynamics equations. Subsequently, the system behavior is characterized directly using pre-collected offline input-output data trajectories, by which the process of explicit system identification is bypassed. The LQ optimal control problem is then reformulated as a direct optimization of the control policy within the data space. Finally, the validity of the proposed scheme is verified through simulation. It is shown that high-precision trajectory tracking is achieved by the DeePO-based controller, and superior convergence smoothness is exhibited in comparison with indirect data-driven control strategies.

In this paper, a data-driven system identification framework is presented for a custom-made micro unmanned underwater vehicle (UUV). To obtain accurate data, outdoor lake experiments were conducted. An unmanned aerial vehicle (UAV) was used to record the UUV from above. The ground truth data were obtained using PWM navigation data and position information from visual trajectory tracking based on Kernelized Correlation Filters (KCF). Based on the collected dataset, a method using reproducing kernel Hilbert space (RKHS) is proposed. The identification task is formulated as a robust optimization problem. It is then transformed into a convex semidefinite programming (SDP) problem. To ensure the stability of the linear system, the stable spline (SS) kernel is applied. Finally, the eigensystem realization algorithm (ERA) is used to convert the impulse responses into a finite-dimensional linear state-space model. The linear dynamic and kinematic equations of the UUV are successfully derived and verified by the experiments.

We address a noncooperative game problem in multi-controller system under delayed and asymmetric information structure. Under these conditions, the classical separation principle fails as estimation and control design become strongly coupled, complicating the derivation of an explicit Nash equilibrium. To resolve this, we employ a common-private information decomposition approach, effectively decoupling control inputs and state estimation to obtain a closed-form Nash equilibrium. By applying a forward iterative method, we establish the convergence of the coupled Riccati and estimation error covariance recursions, yielding both the steady-state Kalman filter and the Nash equilibrium. Furthermore, we quantify the impact of asymmetric information, proving that a richer information set reduces the costs for the corresponding player. Finally, numerical examples are provided to demonstrate the effectiveness of the results.

This paper investigates the trajectory tracking control problem for underactuated unmanned surface vessels (USVs) considering disturbances and model uncertainties. A neural non-singular fast terminal sliding mode control (NFTSMC) is proposed. The proposed method consist of an improved NFTSMC under a backstepping structure, which overcomes the underactuated issue while promising a finite converging time, and a disturbance observer (DO) based on radial basis function (RBF) neural network that adaptively adjusts the control parameters. The proposed method could steer underactuated USVs to precisely trace predefined trajectories under perturbations and disturbance, while guaranteeing convergence within a finite time period. Compared to the existing method for underactuated USVs control, (1) the proposed NFTSMC robustly controls the USVs trajectory imposing a finite-time converging time period and an improved chattering issue; and (2) the response of the closed-loop system is reduced and the tracking accuracy is improved by adaptively adjusting the control parameters via a fully data-driven neural DO without requiring prior knowledge about the stochastic disturbance. The proposed method offers a significant approach for precisely and fast controlling underactuated USVs in uncertain marine environments.

Due to the crucial role in the development and utilization of ocean resources, research on autonomous underwater vehicles (AUVs) has received widespread attention from both academia and industry in recent years. Existing AUVs are typically expensive and have poor control accuracy, which significantly limits their development and use. Motivated by this, a low-cost, long-endurance, and high-precision AUV prototype "Sea Explorer-I" is independently developed by our laboratory. In this paper, a thorough research is conducted for "Sea-Explorer I", including its structural design, mathematical modeling, and output feedback linear quadratic tracking (LQT) control problems. The principal contributions of this paper are as follows: For "Sea-Explorer I", high-precision flow field simulations are conducted using computational fluid dynamics (CFD). This approach integrates dynamic and static grids with superimposed translational and rotational motions, enabling the calculation of the hydrodynamic coefficients required for mathematical modeling. Moreover, from the perspective of stochastic control, this paper describes the optimal trajectory tracking control problem of an AUV as an LQT problem, then the optimal LQT control strategy is derived, and the necessary and sufficient stabilization conditions are also presented. Finally, the effectiveness of the obtained results is validated by the simulation results.

In this paper, the decentralized estimation and linear quadratic (LQ) control problem for a leader-follower networked system (LFNS) is studied from the perspective of asymmetric information. Specifically, for a leader-follower network, the follower agent will be affected by the leader agent, while the follower agent will not affect the leader agent. Hence, the information sets accessed by the control variables of the leader agent and the follower agent are asymmetric, which will bring essential difficulties in finding the optimal control strategy. To this end, the orthogonal decomposition method is adopted to achieve the main results. The main contributions of this paper can be summarized as follows: Firstly, the optimal iterative estimation is derived using the conditional independence property established in this paper. Secondly, the optimal decentralized control strategy is derived by decoupling the forward-backward stochastic difference equations (FBSDEs), based on the derived optimal iterative estimation. Thirdly, the necessary and sufficient conditions for the feedback stabilization of the LFNS in infinite-horizon are derived. Finally, the proposed theoretical results are applied to solve the decentralized control problem of a leader-follower autonomous underwater vehicle (LF-AUV) system. The optimal control inputs for the AUVs are provided, and simulation results verify the effectiveness of the obtained results.

This paper investigates an olfactory-based navigation method that enables a Mars lander to target any time-varying methane plume during the powered descent, even if the source location is unknown a priori. The episodic methane plumes emanating from the Martian surface invite the possibility of direct access to the subsurface methane reservoir to acquire pristine organic material. Any descending lander adapted for plume localization is required to not only land safely, but it must also infer the location of the landing target - the plume source - autonomously during the descent. However, existing plume source localization methods, which are all discriminative model, are computationally complex and seriously overfitted, rendering them ineffective in Mars methane exploration missions. We propose a generative source localization method based on a novel Gaussian mixture plume model. (1) A Gaussian mixture model for continuous releasing plume the corresponding probabilistic graphical model are proposed. (2) A generative source localization method is developed. The proposed method first maximizes the posterior probability of plume capture events then calculate the source location by minimizing the residuals. The proposed method resolves the overfitting problem of the existing methods while reducing the computational complexity. (3) By integrating the proposed olfactory-based navigation method and the E-guidance law, a biomimetic powered descent navigation and guidance method for Mars methane exploration missions is developed. Simulations in turbulent environment developed via computational fluid dynamics determined that the proposed method could overcome the overfitting problem while reducing the computational complexity. Besides, the fuel consumption imposed by the proposed method is modest compared with the baseline scenario where the landing target is known a priori. We believe that the proposed method offers the prospect of targeting the methane vent sources for direct access to subsurface material prior to its ejection.

This brief presents a novel active fault-tolerant control (FTC) scheme for an unstable three-degree-of-freedom (3-DOF) helicopter subject to motor faults. The helicopter is instrumented only with angular position sensors and has no independent velocity sensors, which makes its FTC design more challenging. Although some works have developed FTC for helicopters using only angular information, they required stringent assumptions on the system. To circumvent this problem, a reduced-order sliding mode observer (SMO) is first introduced to obtain auxiliary signals, which become the outputs of an analytical system. Next, an interval observer (IO) is designed for the analytical system, and a fault-tolerant controller is established, and their parameters are jointly optimized to ensure that the helicopter performs at an acceptable level, whether there is a fault or not. The IOs use adaptive parameters, which provide tighter bounds, resulting in more accurate estimation of faults. Finally, simulations and experiments on a 3-DOF helicopter platform are conducted to demonstrate the efficacy of our proposed scheme.

In this paper, the optimal local and remote linear quadratic (LQ) control problem is studied for a networked control system (NCS) which consists of multiple subsystems and each of which is described by a general multiplicative noise stochastic system with one local controller and one remote controller. Due to the unreliable uplink channels, the remote controller can only access unreliable state information of all subsystems, while the downlink channels from the remote controller to the local controllers are perfect. The difficulties of the LQ control problem for such a system arise from the different information structures of the local controllers and the remote controller. By developing the Pontyagin maximum principle, the necessary and sufficient solvability conditions are derived, which are based on the solution to a group of forward and backward difference equations (G-FBSDEs). Furthermore, by proposing a new method to decouple the G-FBSDEs and introducing new coupled Riccati equations (CREs), the optimal control strategies are derived where we verify that the separation principle holds for the multiplicative noise NCSs with packet dropouts. This paper can be seen as an important contribution to the optimal control problem with asymmetric information structures.

In this paper, a backstepping-based trajectory tracking control strategy is presented for a self-developed torpedoshaped autonomous underwater vehicle (AUV). First, the mechanical structure of the AUV is systematically described, and hydrodynamic coefficients obtained through computational fluid dynamics (CFD) simulations are briefly incorporated into its dynamic modeling. Subsequently, a five-degree-of-freedom (5-DOF) trajectory tracking controller is designed using the backstepping method, and the stability analysis is conducted to derive sufficient conditions for the convergence of the tracking error dynamics. Finally, the numerical simulations are performed to validate the effectiveness of the proposed control methods and results.

This paper investigates an olfactory-based powered descent guidance method that enable a lander to autonomously locate and target the vent source of any methane plume in long-time-average wind environment on Mars. The episodic methane plumes emanating from the Martian surface invite the possibility of direct access to the subsurface methane reservoir to acquire pristine organic materials. Constrained by the insufficient knowledge of the accurate plume vent location, Mars landers must infer the target location the plume source autonomously during the powered descent. However, existing powered descent guidance methods require the target location as a terminal constraint, rendering them ineffective in plume exploration missions. We propose an olfactory-based powered descent guidance method that could steer a lander to target the methane vent source by tracking the gradient of methane concentration in long-time- average wind environment, imposing sub-optimal fuel consumption. A novel olfactory navigation method based on tracking the negative logarithmic concentration gradient, and the corresponding gradient measurement method, is proposed. By implementing the negative logarithmic gradient field, gradient-dependent dynamic equations for the powered descending lander are proposed. The gradient- dependent equations provide a way to transform the original optimal guidance problem, which is non-convex and terminal-free, into a convex and terminal-fixed problem. A suboptimal guidance law, similar to E-guidance, is then derived by solving this problem. The proposed guidance could target the vent source of any methane plume in long-time-average wind environment, imposing modest fuel consumption by feeding back the concentration gradient. Numerical simulations illustrate that, in comparison to the terminal-fixed E-guidance and the open-loop fuel-optimal guidance, the lander imposes additional fuel consumption of 2.57% and 12.15% respectively, using the proposed guidance law. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

In this paper, we mainly investigate the information fusion problem for underwater vehicles in the presence of acoustic communication delays and packet losses. Due to time delays and packet losses in acoustic communication in underwater environments, different sensors on underwater vehicles may have varying measurement information, making the design of an information fusion strategy challenging. To solve this problem, we first develop a local optimal estimator for each sensor on the underwater vehicle. Furthermore, the estimation error cross-covariance matrices between any two local estimators are derived, and the global weighted fusion filter is given to minimize the variance of the overall state estimation. Moreover, the stability conditions of the global weighted fusion filter are proposed. Finally, a numerical example is presented to demonstrate the effectiveness of the information fusion algorithm.

In this article, the local and remote stochastic nonzero-sum game for a multiplicative noise system with inconsistent information is investigated, in which the multiplicative noise can cause nonlinear characteristics of linear systems, making it difficult to solve the optimal linear feedback Nash equilibrium. For the considered local and remote stochastic nonzero-sum game, the local player and the remote player obtain different information sets, which leads to inconsistent information between the two players. The goal is that each player is desired to minimize their own cost function. Our approach is based on a combination of orthogonal decomposition and completing square techniques, which allow us to derive a set of coupled Riccati equations that characterize the optimal feedback explicit (closed-loop) Nash equilibrium. The contributions of this article are summarized as follows. First, the optimal open-loop Nash equilibrium is obtained in terms of the forward and backward stochastic difference equations (FBSDEs) by adopting the Pontryagin maximum principle. Second, the closed-loop Nash equilibrium of this local and remote stochastic nonzero-sum game for a multiplicative noise system with inconsistent information is obtained by using the orthogonal decomposition methods. Finally, a simulation example is given to illustrate the validity of theoretical results and discuss potential extensions to more complex systems. In this paper, the local and remote stochastic nonzero-sum game for a multiplicative noise system with inconsistent information is investigated, in which the multiplicative noise can cause nonlinear characteristics of linear systems. For the considered local and remote stochastic nonzero-sum game, the local player and the remote player obtain different information sets. The optimal open-loop Nash equilibrium and feedback explicit (closed-loop) Nash equilibrium are derived by using the maximum principle and the orthogonal decomposition method.image

This paper investigates the discrete-time linear quadratic (LQ) stochastic Stackelberg game, which has not been thoroughly addressed in previous literature. Firstly, we derive the maximum principle for the stochastic Stackelberg difference game using the variational method, and obtain the necessary and sufficient solvability conditions. However, due to the coupling between the two players and the presence of stochastic noise, obtaining explicit optimal leader and follower's strategies becomes challenging. Therefore, we present a feasible suboptimal control strategy instead. As a result, we derive a feasible suboptimal control strategy. To achieve this, we assume a linear homogeneous relationship to decouple the group of stochastic game forward-backward stochastic differential equations (SG-FBSDEs), which serves as a compromise for obtaining the optimal solution. With this approach, we derive a feasible solution to the stochastic Stackelberg difference game based on the solution to symmetric Riccati equations.

In this paper, the finite horizon asymmetric information linear quadratic (LQ) control problem is investigated for a discrete‐time mean field system. Different from previous works, multiple controllers with different information sets are involved in the mean field system dynamics. The coupling of different controllers makes it quite difficult in finding the optimal control strategy. Fortunately, by applying the Pontryagin's maximum principle, the corresponding decentralized control problem of the finite horizon is investigated. The contributions of this paper can be concluded as follows: For the first time, based on the solution of a group of mean‐field forward and backward stochastic difference equations (MF‐FBSDEs), the necessary and sufficient solvability conditions are derived for the asymmetric information LQ control for the mean field system with multiple controllers. Furthermore, by the use of an innovative orthogonal decomposition approach, the optimal decentralized control strategy is derived, which is based on the solution to a non‐symmetric Riccati‐type equation.

The self-excited oscillation of an electro-hydraulic servo valve is a crucial factor that affects the operational stability of both the servo valve and the entire servo system. As the intermediary for electromagnetic force, hydraulic force, and feedback force, the dynamic characteristics of the armature assembly play a pivotal role in the emergence of self-excited oscillations. In this article, a mathematical model of the armature assembly is developed using the finite element method (FEM), which establishes a connection between the input signal and the dynamic characteristics of the armature assembly. The structure of the parameter matrix and the recursive relationship of discrete time steps are derived, while the discrete method of the feedback rod and the discrete time format of the FEM model are also analyzed to enhance the prediction accuracy of the model. To validate the efficacy of the FEM model, a numerical simulation model, a classical mathematical model, and an experimental platform are constructed to simulate or measure the dynamic characteristics of the armature assembly. Through the comparison of the dynamic characteristics obtained by the abovementioned methods, it is confirmed that the FEM model effectively predicts both the static and dynamic characteristics of the armature assembly. To suppress the vibration magnitude within the natural frequency range of the armature assembly, the external load that can effectively mitigate the resonance amplitude is calculated based on the FEM model, and the aim of ensuring the stable functionality of the armature assembly throughout its operational range is achieved.

This paper investigates the adaptive model predictive control (MPC) for a class of constrained linear multiple-input multiple-output (MIMO) systems with constant parametric uncertainty and input delay. An adaptive update law based on time-varying updating rate is proposed, which enables the update of uncertain parameters in the presence of input delay. Consequently, to deal with constraints, we convert the optimization problem into a solvable simple structure, which originates from the min-max optimization. Furthermore, we theoretically show that the closed-loop system is asymptotically stable and the proposed adaptive MPC strategy is proved to be recursively feasible. Finally, numerical simulation and comparison are given to illustrate the efficacy of the proposed method.

The present paper considers the finite‐horizon indefinite linear quadratic (LQ) control problem for stochastic Takagi–Sugeno (T‐S) fuzzy systems with input delay. In this paper, we consider the presence of sensor data scheduling, which imposes a communication energy constraint and necessitates optimal state estimation for measurements. Then, by utilizing dynamic programming principles, the stochastic LQ problem under consideration can be solved, while the optimal control policy is developed in terms of the unique solutions to a set of coupled difference Riccati equations (CDREs). Specifically, for simple delay‐free case, the linear matrix inequalities based conditions are also proposed, whose feasibility is shown to be equivalent to the well‐posedness of the indefinite LQ control under consideration. As an application, our theoretic analysis is extended to study the intermittent observation model caused by random denial‐of‐service attack.