This article proposes a unified suboptimal controller design method for unknown general nonlinear systems subject to multiple constraints, including state, input, and output constraints. All inequality constraints are transformed into equality constraints using slack functions and Pade approximation. An unconstrained augmented system is then defined to describe the dynamics of original system and the equality constraints, where the optimal controller of the augmented system can be viewed as a suboptimal controller for the original system. Furthermore, considering the unmodeled dynamics of the original system, neural networks (NNs) are utilized and a data-based solution strategy of integral reinforcement learning (IRL) is presented for the augmented system. Ultimately, the simulation results are given to reflect the effectiveness of the unified design method.

In this paper, the dual spacecraft pursuit-evasion game problem under incomplete information is investigated, and a strategy-solving method for the incomplete information pursuit-evasion game based on particle swarm optimization and unscented particle filter (PSO-UPF) estimation is proposed. The completeness of the information available about the target’s cost function, which is determined by the weighting information, has a significant impact on the success of the pursuing strategy. For the cost function is unknown in incomplete information scenarios, a research framework of the pursuit-evasion game based on following observation and one-sided pursuit two stages is established. Besides, to describe the more accurate motion of the spacecraft, a Schweighart-Sedwick (SS) dynamic model is introduced that considers the effect ofJ2perturbation. Firstly, an equilibrium strategy for the SS model-based pursuit-evasion problem is derived under complete information. Next, for the incomplete information scenarios, an estimation method based on PSO-UPF of weight matrix information is established, which allows the cost function to be determined by the estimation method in the observation stage. Then, the pursuit strategy is re-designed in the one-sided pursuit stage based on the estimated cost function. Finally, the performance of the proposed method is validated by simulation. The results demonstrate that the approach can achieve good performance by efficiently estimating the weight information in the opponent’s cost function.

This article proposes a subspace-aided distributed monitoring and control performance optimization integrated framework and the corresponding distributed monitoring and optimization approaches equivalent to centralized designs. It effectively realizes the online global control performance optimization and solves the predesigned controller parameter adjustment limitation. The main contributions of this article are as follows. First, the proposed distributed monitoring and optimization modules can cooperate to establish a subspace-aided distributed integrated framework. The framework effectively addresses the issue of separate design in monitoring and optimization, achieving modularization that facilitates the expansion and maintenance of interconnected systems. Second, the proposed subspace-aided control performance optimization approach breaks the limitations of existing methods that require predesigned controller parameter adjustments, which can achieve distributed control performance optimization while ensuring closed-loop stability of interconnected systems. Third, the proposed optimization approach can automatically adjust the iterative step size, avoiding the disadvantage of manually setting the step size in the traditional optimization algorithm. It shortens the optimization time and reduces the design difficulty. The new methodologies have been evaluated against the current techniques and validated using an interconnected dc motor system, which holds significant engineering importance.

High-accuracy fault diagnosis is a crucial way to improve the reliability of electro-hydrostatic actuator (EHA) in transport airplane. Due to the limitation of aircraft structure, it is extremely difficult to obtain EHA fault data and to accurately assess the type of fault occurrence, so a process methodology is proposed for test signal excitation in an EHA simulation environment to obtain multisource fault data. Based on two EHA fault dataset, a multisequence fusion network (MSFN) is proposed for end-to-end fault diagnosis. MSFN has the advantages of light weight, high efficiency, and the ease of expansion, utilizes multiscale wide kernel convolutional neural network (CNN), deep dilated CNN, and long short-term memory network for parallel feature extraction, and the improved fusion channel and spatial attention mechanism for cross-fusion of different sequence features. Experimental results show that MSFN has high prediction accuracy and robustness under different intensity noise, achieving 98.56% average accuracy when SNR = 20, can effectively realize the rapid fault diagnosis of EHA under multiple complex working conditions.

The emergence of single-cell RNA sequencing (scRNA-seq) technology has revolutionized the identification of cell types and the study of cellular states at a single-cell level. Despite its significant potential, scRNA-seq data analysis is plagued by the issue of missing values. Many existing imputation methods rely on simplistic data distribution assumptions while ignoring the intrinsic gene expression distribution specific to cells. This work presents a novel deep-learning model, named scMultiGAN, for scRNA-seq imputation, which utilizes multiple collaborative generative adversarial networks (GAN). Unlike traditional GAN-based imputation methods that generate missing values based on random noises, scMultiGAN employs a two-stage training process and utilizes multiple GANs to achieve cell-specific imputation. Experimental results show the efficacy of scMultiGAN in imputation accuracy, cell clustering, differential gene expression analysis and trajectory analysis, significantly outperforming existing state-of-the-art techniques. Additionally, scMultiGAN is scalable to large scRNA-seq datasets and consistently performs well across sequencing platforms. The scMultiGAN code is freely available at https://github.com/Galaxy8172/scMultiGAN.

This article studies the distributed robust optimal control for discrete-time linear multiagent systems (MASs) with parametric uncertainties, where digraphs that only contain a directed spanning tree are allowed. Using the linear quadratic regulator approach, an optimal control protocol is presented. The presented controller is fully distributed, since the global information of graphs is unneeded for the design and implementation of the presented controller. The global performance index of MASs can be minimized by using the presented control protocol, and the optimal solution is independent with the information of parametric uncertainties. Finally, some simulated examples are provided to show the effectiveness of the proposed approaches.

The expression quantitative trait loci (eQTL) analysis has become important for understanding the regulatory function of genomic variants on gene expression in a tissuespecific manner and has been widely applied across species from microbes to mammals. Current eQTL studies mainly focus on the simple one-to-one regulation between variant and gene. Recent research have demonstrated there are also more complex regulatory patterns between eQTLs and genes. However, there is a lack of studies and relevant methods to systematically discover the regulatory patterns between multiple eQTLs and multiple genes. In this regard, this study has proposed a novel computational framework, called eQTLMotif, to discover regulation patterns of eQTLs in a many-to-many manner. This framework mainly consists of two steps: (1) construct a novel eQTL regulatory network by integrating bipartite eQTL network, eQTL mediation effects, and gene regulatory network; (2) perform motif mining through exactly enumerating frequently appeared eQTL regulatory structures. Based on this framework, we for the first time systematically investigated the eQTL regulatory patterns in the human frontal cortex based on a large cohort of postmortem human brains. Experiments have demonstrated that our framework can effectively reveal novel eQTL regulatory patterns. And some are in similar structure to the existing gene regulation patterns, such as feed-forward loop (FFL)-like motif, single input module (SIM)-like motif, and dense overlapping regulons (DOR)- like motif. Our method and findings will further enhance the understanding of regulatory mechanisms of eQTLs in multiple tissues and species.

The tracking control of hypersonic flight vehicle (HFV) is discussed in this paper, and the nonlinear model of HFV is assumed to be completely unknown. This problem is surely challenging because of the missing prior knowledge, but is more closer to reality since the exact mode of HFV is difficult to be obtained. A reinforcement learning (RL) based optimal controller is proposed for the tracking control of HFV. A model based RL algorithm is firstly proposed and then, based on this algorithm, a model free algorithm is constructed. For relaxing the environmental conditions, neural network (NN) is adopted for the approximation of Critic and Actor, and then a Greedy Policy based updated learning law for NN is derived. The presented RL based control strategy is carried on the nonlinear model of HFV to show its effectiveness.

This work investigates the fixed-time trajectory tracking control problem of a wheeled mobile robot (WMR) under external disturbances. Firstly, the dynamic error system of the WMR is transformed into a second-order attitude error subsystem and a third-order position error subsystem, respectively. Subsequently, the super-twisting-like algorithms are proposed for the trajectory tracking of the WMR, and the fixed-time stability of the two subsystems are guaranteed. Finally, the effectiveness of the proposed control schemes is demonstrated by simulation and experiment results.

The challenging problem on the design of a large-angle attitude tracking controller for rigid satellites without angular velocity measurements is investigated in this article. An efficient and practical angular velocity-free control strategy with a simple, yet efficient structure is proposed. The attitude tracking maneuver is accomplished with the desired attitude pointing accuracy ensured despite disturbances. Compared with the existing observer-based velocity-free schemes, no observer is embedded into the control scheme. The developed approach can be implemented online and in real time. It does not require expensive online computation, enabling its convenient application to practical large-angle attitude tracking maneuvers. The presented control solution is numerically and experimentally validated on a rigid satellite testbed.

The attitude stabilization problem of rigid bodies with external disturbance is studied. A novel disturbance observer-based control scheme is presented. Comparing with the existing observers for external disturbance, the proposed observer releases the assumption that the external disturbance should be constant or with the minor rate of change. The closed-loop attitude stabilization system is governed by the controller to be exponentially stable with disturbance rejected. The observer error of disturbance, the attitude, and the angular velocity are exponentially stabilized to be zero or within a small set with an arbitrary radius. The key feature of this scheme is that it ensures the control performance to be more robust to any external disturbance. The controller has a simple structure and does not involve complicated computation. The effectiveness of the presented solution is validated by a rigid satellite example.

The rapid attitude stabilization problem of flexible spacecraft with uncertain inertia and disturbances is investigated. In this article, a sliding mode-based fixed-time control approach is presented with a new fixed-time surface ensuring a faster convergence rate incorporated. This surface has no singularity and can guarantee the settling time to be independent of initial states. An adaptive fixed-time attitude control law is then synthesized, which is continuous and chattering free. It is rigorously proved that the states of the spacecraft attitude system can converge into a small neighborhood after fixed time. A numerical example is presented to validate that the designed scheme is efficient to perform attitude stabilization maneuvers rapidly, whereas high control accuracy is still provided.

This paper addresses a long-standing yet well documented open problem on task-space trajectory tracking control of robotic manipulators subject to both uncertain dynamics and uncertain kinematics. The main contribution is to establish a theoretical framework for designing an observer-based controller to achieve exponential tracking control. Two observers are designed for precisely estimating the uncertain kinematics and dynamics. It is theoretically proved that the entire observer-controller system is proved to be globally exponentially stable. Both the estimation errors and the trajectory tracking error can globally exponentially converge to their stable equilibrium points, respectively. To the best knowledge of the author, this works may be the first result for robot exponential tracking control. The tracking performance is, therefore, more robust to system uncertainties. The settling time of the closed-loop tracking error system can be tuned to be small arbitrarily. Experimental tests are also conducted to validate the effectiveness of the designed control framework.

This paper investigates a difficult problem of reconstructing actuator faults for robotic manipulators. An intelligent approach with fast reconstruction property is developed. This is achieved by using observer technique. This scheme is capable of precisely reconstructing the actual actuator fault. It is shown by Lyapunov stability analysis that the reconstruction error can converge to zero after finite time. A perfect reconstruction performance including precise and fast properties can be provided for actuator fault. The most important feature of the scheme is that, it does not depend on control law, dynamic model of actuator, faults' type, and also their time-profile. This super reconstruction performance and capability of the proposed approach are further validated by simulation and experimental results.

This paper presents an adaptive quasi-optimal higher order sliding-mode control (HOSMC) scheme, which is able to avoid gain overestimation. The overall scheme consists of two elements: 1) a quasi-optimal control law that provides fast finite-time stabilization for a chain of integrators; and 2) an adaptive HOSMC with integration of the quasi-optimal control and the integral sliding-mode concept. The adaptation strategy solves the problem of gain tuning without overestimation and has the advantage of chattering reduction. Moreover, the bounds of the uncertainties are no longer needed in the controller design. Simulation results are provided to demonstrate the effectiveness of the proposed HOSMC algorithm.

This paper addresses a difficult problem of high-accuracy control for quadrotor unmanned aerial vehicles (UAVs) subject to external disturbance force and unknown disturbance torque. An observer-based full control scheme is presented. In the strategy, two observer-based estimators are first designed to estimate external disturbance force and torque, respectively. With the application of the precise estimation value, a nonlinear tracking controller is then proposed with compensated disturbance. It is shown by the Lyapunov stability analysis that the entire controller-observer system is asymptotically stable. The key feature of the scheme is that it not only has the superior capability to attenuate unknown external disturbance torque and external force generated by the wind, but also it is able to achieve full control (i.e., six degrees-of-freedom) of the quadrotor UAVs with position and attitude successfully controlled. The effectiveness of the approach is verified on a quadrotor UAV example.