Mixed-Zero-Sum-Game-Based Memory Event-Triggered Cooperative Control of Heterogeneous MASs Against DoS Attacks.

This article studies the problem of memory event-triggered cooperative adaptive control of heterogeneous nonlinear multiagent systems (MASs) under denial-of-service (DoS) attacks based on the multiplayer mixed zero-sum (ZS) game strategy. First, a neural-network-based reinforcement learning scheme is structured to obtain the Nash equilibrium solution of the proposed multiplayer mixed ZS game scheme. Then, a memory-based event-triggered mechanism considering the historical data is proposed. This effectively avoids incorrect triggering information caused by unknown external factors. Moreover, thanks to the idea of switching topology, the mixed ZS game problem under the influence of node-based DoS attacks is solved efficiently. In accordance with the Lyapunov stability theory, it is proved that all signals of heterogeneous MASs are bounded, all heterogeneous followers can track the trajectory of the leader during the no-attack period, the attacked follower can achieve stabilization control during the attack period, and the remaining nonattacked followers can achieve cooperative control during the attack period. Finally, the effectiveness of the designed memory-event-triggered-based mixed ZS game cooperative control strategy is tested by the given simulation results.

Research progress on carotenoid production by Rhodosporidium toruloides.

Carotenoids are natural lipophilic pigments, which have been proven to provide significant health benefits to humans, relying on their capacity to efficiently scavenge singlet oxygen and peroxyl radicals as antioxidants. Strains belonging to the genus Rhodosporidium represent a heterogeneous group known for a number of phenotypic traits including accumulation of carotenoids and lipids and tolerance to heavy metals and oxidative stress. As a representative of these yeasts, Rhodosporidium toruloides naturally produces carotenoids with high antioxidant activity and grows on a wide variety of carbon sources. As a result, R. toruloides is a promising host for the efficient production of more value-added lipophilic compound carotenoids, e.g., torulene and torularhodin. This review provides a comprehensive summary of the research progress on carotenoid biosynthesis in R. toruloides, focusing on the understanding of biosynthetic pathways and the regulation of key enzymes and genes involved in the process. Moreover, the relationship between the accumulation of carotenoids and lipid biosynthesis, as well as the stress from diverse abiotic factors, has also been discussed for the first time. Finally, several feasible strategies have been proposed to promote carotenoid production by R. toruloides. It is possible that R. toruloides may become a critical strain in the production of carotenoids or high-value terpenoids by genetic technologies and optimal fermentation processes. KEY POINTS: • Biosynthetic pathway and its regulation of carotenoids in Rhodosporidium toruloides were concluded • Stimulation of abiotic factors for carotenoid biosynthesis in R. toruloides was summarized • Feasible strategies for increasing carotenoid production by R. toruloides were proposed.

SDO-Based Command Filtered Adaptive Neural Tracking Control for MIMO Nonlinear Systems With Time-Varying Constraints.

In this article, an adaptive neural tracking control based on saturation disturbance observer (SDO) and command filter is studied for multiple-input-multiple-output nonlinear systems with time-varying constraints and system uncertainties. By employing neural networks (NNs), the system uncertainties are approximated. The SDO is proposed to estimate the composited disturbances which consist of NN approximation errors and the external bounded disturbances. Compared with the traditional disturbance observer, the SDO can reduce the estimation error to some extent. The control requirements are achieved based on the multiconstraints which contain three layers: 1) prescribed performance functions (PPFs); 2) actual constraints; and 3) virtual constraints. The errors remain within the prescribed small neighborhood of zero by using the PPFs, the error constraints ensure that the time-varying constraints are never violated even if the PPFs are not available, and the virtual constraints are applied in a new time-varying barrier Lyapunov function (TVBLF) to design virtual controllers and controller to solve the singularity problem of the traditional TVBLF. In addition, the command filter is introduced to solve the problem of "explosion of complexity." Finally, a numerical simulation verifies the effectiveness of the proposed scheme for a flight control of unmanned aerial vehicle.

Adaptive Multigradient Recursive Reinforcement Learning Event-Triggered Tracking Control for Multiagent Systems.

This article proposes a fault-tolerant adaptive multigradient recursive reinforcement learning (RL) event-triggered tracking control scheme for strict-feedback discrete-time multiagent systems. The multigradient recursive RL algorithm is used to avoid the local optimal problem that may exist in the gradient descent scheme. Different from the existing event-triggered control results, a new lemma about the relative threshold event-triggered control strategy is proposed to handle the compensation error, which can improve the utilization of communication resources and weaken the negative impact on tracking accuracy and closed-loop system stability. To overcome the difficulty caused by sensor fault, a distributed control method is introduced by adopting the adaptive compensation technique, which can effectively decrease the number of online estimation parameters. Furthermore, by using the multigradient recursive RL algorithm with less learning parameters, the online estimation time can be effectively reduced. The stability of closed-loop multiagent systems is proved by using the Lyapunov stability theorem, and it is verified that all signals are semiglobally uniformly ultimately bounded. Finally, two simulation examples are given to show the availability of the presented control scheme.

Adaptive NN Tracking Control for Uncertain MIMO Nonlinear System With Time-Varying State Constraints and Disturbances.

In this article, an adaptive neural network (NN) tracking control scheme is proposed for uncertain multi-input-multi-output (MIMO) nonlinear system in strict-feedback form subject to system uncertainties, time-varying state constraints, and bounded disturbances. The radial basis function NNs (RBFNNs) are adopted to approximate the system uncertainties. By constructing the intermediate variables, the external disturbances that cannot be directly measured are approximated by the disturbance observers. The time-varying barrier Lyapunov function (TVBLF) is constructed to guarantee the boundedness of the errors lie in the sets. To overcome the potential singularity problem that the denominator of the barrier function term approaches zero in controller design, the adaptive NN tracking control scheme with time-varying state constraints is proposed. Based on the TVBLF, the controller will be designed to guarantee tracking performance without violating the appropriate error constraints. The analysis of TVBLF shows that all closed-loop signals remain semiglobally uniformly ultimately bounded (SGUUB). The simulation results are performed to validate the validity of the proposed scheme.

Flight and Vibration Control of Flexible Air-Breathing Hypersonic Vehicles Under Actuator Faults.

The issue of modeling and fault-tolerant control (FTC) design for a class of flexible air-breathing hypersonic vehicles (FAHVs) with actuator faults is investigated in this article. Different from previous research, the shear deformation of the fuselage is considered, and an ordinary differential equations-partial differential equations (ODEs-PDEs) coupled model is established for the FAHVs. A feedback control is proposed to ensure flight stable and an adaptive FTC method is designed to deal with actuator faults while suppressing the system's vibrations. Besides, the stability analysis of the closed-loop system is given via the Lyapunov direct method and an algorithm that transfers the bilinear matrix inequalities (BMIs) feasibility problem to the linear matrix inequalities (LMIs) feasibility problem is provided for determining the control gains. Finally, the numerical simulation results show that the proposed controller can stabilize the flight states and suppresses the vibration of the fuselage efficiently.

Event-Triggered Fractional-Order Tracking Control for an Uncertain Nonlinear System With Output Saturation and Disturbances.

In this article, an event-triggered (ET) fractional-order adaptive tracking control scheme (ATCS) is studied for the uncertain nonlinear system with the output saturation and the external disturbances by using the nonlinear disturbance observer (NDO) and the neural networks (NNs). Based on NNs, the system uncertainties are approximated. An NN-based NDO is designed to estimate the bounded disturbances. Combining the NNs, the output of the designed NDO, the fractional-order theory, and the ET mechanism, an ATCS is proposed under the output saturation. According to the stability analysis, all the closed-loop signals are semiglobally uniformly ultimately bounded based on the investigative ATCS. The simulation results and the comparative experiment verifications are shown to indicate the viability of the developed control scheme.

Event-Triggering-Learning-Based ADP Control for Post-Stall Pitching Maneuver of Aircraft.

In this article, an improved event-triggering-learning (ETL)-based adaptive dynamic programming (ADP) method for the post-stall pitching maneuver of aircraft is proposed to achieve the robust optimal control and reduce the computational cost. First, a feedforward control with the nonlinear disturbance observer (NDO) technique is designed to attenuate the adverse effects caused by the unsteady aerodynamic disturbances. Subsequently, the ADP method with a critic neural network which is constructed to approximate the value function in the Hamilton-Jacobi-Bellman equation is employed to conduct the optimal control of aircraft. In addition, to reduce the computational cost of learning, the event-triggering (ET) mechanism with an improved ET condition is applied. The Lyapunov stability theory is utilized to prove that all signals in the closed-loop control system are uniformly ultimately bounded. Finally, simulation results are presented to illustrate the effectiveness of the proposed ETL-based ADP method.

Microbiologic risk factors of recurrent choledocholithiasis post-endoscopic sphincterotomy.

BACKGROUND: Choledocholithiasis is a severe disorder that affects a significant portion of the world's population. Treatment using endoscopic sphincterotomy (EST) has become widespread; however, recurrence post-EST is relatively common. The bile microbiome has a profound influence on the recurrence of choledocholithiasis in patients after EST; however, the key pathogens and their functions in the biliary tract remain unclear. AIM: To investigate the biliary microbial characteristics of patients with recurrent choledocholithiasis post-EST, using next-generation sequencing. METHODS: This cohort study included 43 patients, who presented with choledocholithiasis at the Guangdong Second Provincial General Hospital between May and June 2020. The patients had undergone EST or endoscopic papillary balloon dilation and were followed up for over a year. They were divided into either the stable or recurrent groups. We collected bile samples and extracted microbial DNA for analysis through next-generation sequencing. Resulting sequences were analyzed for core microbiome and statistical differences between the diagnosis groups; they were examined using the Kyoto Encyclopedia of Genes and Genomes pathway hierarchy level using analysis of variance. Correlation between the key genera and metabolic pathways in bile, were analyzed using Pearson's correlation test. RESULTS: The results revealed distinct clustering of biliary microbiota in recurrent choledocholithiasis. Higher relative abundances (RAs) of Fusobacterium and Neisseria (56.61% ± 14.81% vs 3.47% ± 1.10%, 8.95% ± 3.42% vs 0.69% ± 0.32%, respectively) and the absence of Lactobacillus were observed in the bile of patients with recurrent disease, compared to that in stable patients. Construction of a microbiological co-occurrence network revealed a mutual relationship among Fusobacterium, Neisseria, and Leptotrichia, and an antagonistic relationship among Lactobacillales, Fusobacteriales, and Clostridiales. Functional prediction of biliary microbiome revealed that the loss of transcription and metabolic abilities may lead to recurrent choledocholithiasis. Furthermore, the prediction model based on the RA of Lactobacillales in the bile was effective in identifying the risk of recurrent choledocholithiasis (P = 0.03). CONCLUSION: We demonstrated differences in the bile microbiome of patients with recurrent choledocholithiasis compared to that in patients with stable disease, thereby adding to the current knowledge on its microbiologic etiology.

Flexible Performance-Based Control for Nonlinear Systems Under Strong External Disturbances.

Addressing external disturbances has been a critical issue for control design to ensure reliable operation of systems. This article investigates the tracking control problem for the uncertain nonlinear systems with the strong external disturbance and the prescribed performance. The flexible performance-based control scheme is developed by introducing an external disturbance criterion into the prescribed performance. It is capable of guaranteeing the prescribed performance if the external disturbance is less than a specified threshold and degrading that in light of the user-appointed rule otherwise. Particularly, the disturbance interval observer is synthesized to generate the boundaries of the external disturbances and realize the judgment of that criterion. With the generated boundaries, the interval-type auxiliary system is designed to provide the modified performance functions (MPFs) that characterize performance requirement and degradation rule simultaneously. Based on the positive system theory and the Lyapunov method, it is theoretically shown that the system output can always track the reference signal and satisfy the constraints of MPFs. Finally, both the numerical simulation and the application of flight control design verify that the results are effective and valid.

Adaptive Neural Safe Tracking Control Design for a Class of Uncertain Nonlinear Systems With Output Constraints and Disturbances.

In this article, an adaptive neural safe tracking control scheme is studied for a class of uncertain nonlinear systems with output constraints and unknown external disturbances. To allow the output to stay in the desired output constraints, a boundary protection approach is developed and utilized in the output constrained problem. Since the generated output constraint trajectory is piecewise differentiable, a dynamic surface method is utilized to handle it. For the purpose of approximating the system uncertainties, a radial basis function neural network (RBFNN) is adopted. Under the output of the RBFNN, the disturbance observer technology is employed to estimate the unknown compound disturbances of the system. Finally, the Lyapunov function method is utilized to analyze the convergence of the tracking error. Taking a two-link manipulator system, as an example, the simulation results are presented to illustrate the feasibility of the proposed control scheme.

Path Planning of Unmanned Autonomous Helicopter Based on Human-Computer Hybrid Augmented Intelligence.

Unmanned autonomous helicopter (UAH) path planning problem is an important component of the UAH mission planning system. The performance of the automatic path planner determines the quality of the UAH flight path. Aiming to produce a high-quality flight path, a path planning system is designed based on human-computer hybrid augmented intelligence framework for the UAH in this paper. Firstly, an improved artificial bee colony (I-ABC) algorithm is proposed based on the dynamic evaluation selection strategy and the complex optimization method. In the I-ABC algorithm, the following way of on-looker bees and the update strategy of nectar source are optimized to accelerate the convergence rate and retain the exploration ability of the population. In addition, a space clipping operation is proposed based on the attention mechanism for constructing a new spatial search area. The search time can be further reduced by the space clipping operation under the path planning result within acceptable changes. Moreover, the entire optimization process and results can be feeded back to the knowledge database by the human-computer hybrid augmented intelligence framework to guide subsequent path planning issues. Finally, the simulation results confirm that a feasible and effective flight path can be quickly generated by the UAH path planning system based on human-computer hybrid augmented intelligence.

Distributed Fault Estimation and Fault-Tolerant Control of Interconnected Systems.

This article studies the distributed fault estimation (DFE) and fault-tolerant control for continuous-time interconnected systems. Using associated information among subsystems to design the DFE observer can improve the accuracy of fault estimation of the interconnected systems. Based on the static output feedback (SOF), the global outputs of the interconnected systems are used to construct a distributed fault-tolerant control (DFTC). The multiconstrained methods are proposed to enhance the transient performance and ability to suppress the external disturbances simultaneously. The conditions of the presented design methods are expressed in terms of linear matrix inequalities. The simulation results are illustrated to show the feasibility of the presented approaches.

An Implicit Function-Based Adaptive Control Scheme for Noncanonical-Form Discrete-Time Neural-Network Systems.

This article proposes a new implicit function-based adaptive control scheme for the discrete-time neural-network systems in a general noncanonical form. Feedback linearization for such systems leads to the output dynamics nonlinear dependence on the system states, the control input, and uncertain parameters, which leads to the nonlinear parametrization problem, the implicit relative degree problem, and the difficulty to specify an analytical adaptive controller. To address these problems, we first develop a new adaptive parameter estimation strategy to deal with all uncertain parameters, especially, those of nonlinearly parameterized forms, in the output dynamics. Then, we construct a key implicit function equation using available signals and parameter estimates. By solving the equation, a unique adaptive control law is derived to ensure asymptotic output tracking and closed-loop stability. Alternatively, we design an iterative solution-based adaptive control law which is easy to implement and ensure output tracking and closed-loop stability. The simulation study is given to demonstrate the design procedure and verify the effectiveness of the proposed adaptive control scheme.

Adaptive Fault-Tolerant Tracking Control for Discrete-Time Multiagent Systems via Reinforcement Learning Algorithm.

This article investigates the adaptive fault-tolerant tracking control problem for a class of discrete-time multiagent systems via a reinforcement learning algorithm. The action neural networks (NNs) are used to approximate unknown and desired control input signals, and the critic NNs are employed to estimate the cost function in the design procedure. Furthermore, the direct adaptive optimal controllers are designed by combining the backstepping technique with the reinforcement learning algorithm. Comparing the existing reinforcement learning algorithm, the computational burden can be effectively reduced by using the method of less learning parameters. The adaptive auxiliary signals are established to compensate for the influence of the dead zones and actuator faults on the control performance. Based on the Lyapunov stability theory, it is proved that all signals of the closed-loop system are semiglobally uniformly ultimately bounded. Finally, some simulation results are presented to illustrate the effectiveness of the proposed approach.

Quantized Adaptive Finite-Time Bipartite NN Tracking Control for Stochastic Multiagent Systems.

This article investigates the quantized adaptive finite-time bipartite tracking control problem for high-order stochastic pure-feedback nonlinear multiagent systems with sensor faults and Prandtl-Ishlinskii (PI) hysteresis. Different from the existing finite-time control results, the nonlinearity of each agent is totally unknown in this article. To overcome the difficulties caused by asymmetric hysteresis quantization and PI hysteresis, a new distributed control method is proposed by adopting the adaptive compensation technique without estimating the lower bounds of parameters. Radial basis function neural networks are employed to estimate unknown nonlinear functions and solve the problem of algebraic loop caused by the pure-feedback nonlinear systems. Then, an adaptive neural-network compensation control approach is proposed to tackle the problem of sensor faults. The problem of the "explosion of complexity" caused by repeated differentiations of the virtual controller is solved by using the dynamic surface control technique. Based on the Lyapunov stability theorem, it is proved that all signals of the closed-loop systems are semiglobal practical finite-time stable in probability, and the bipartite tracking control performance is achieved. Finally, the effectiveness of the proposed control strategy is verified by some simulation results.

Research advances in cartilage stem cells markers and induced differentiation / 华西口腔医学杂志

Cartilage stem cells (CSCs) are cells that self-proliferate, have surface antigen expression, and have multidirectional differentiation potential in the articular cartilage. CSCs, as an ideal source of stem cells, has a good application prospect in stem cell therapy. This article reviews the CSCs markers, cartilage differentiation signaling pathway, and clinical treatment of osteoarthritis.

Information Entropy-Based Intention Prediction of Aerial Targets under Uncertain and Incomplete Information.

To improve the effectiveness of air combat decision-making systems, target intention has been extensively studied. In general, aerial target intention is composed of attack, surveillance, penetration, feint, defense, reconnaissance, cover and electronic interference and it is related to the state of a target in air combat. Predicting the target intention is helpful to know the target actions in advance. Thus, intention prediction has contributed to lay a solid foundation for air combat decision-making. In this work, an intention prediction method is developed, which combines the advantages of the long short-term memory (LSTM) networks and decision tree. The future state information of a target is predicted based on LSTM networks from real-time series data, and the decision tree technology is utilized to extract rules from uncertain and incomplete priori knowledge. Then, the target intention is obtained from the predicted data by applying the built decision tree. With a simulation example, the results show that the proposed method is effective and feasible for state prediction and intention recognition of aerial targets under uncertain and incomplete information. Furthermore, the proposed method can make contributions in providing direction and aids for subsequent attack decision-making.

Relative Degrees and Implicit Function-Based Control of Discrete-Time Noncanonical Form Neural Network Systems.

This paper studies the relative degrees of discrete-time neural network systems in a general noncanonical form, and develops a new feedback control scheme for such systems, based on implicit function theory and feedback linearization. After time-advance operation on output of such systems, the output dynamics nonlinearly depends on the control input. To address this issue, we use implicit function theory to define the relative degrees, and to establish a normal form. Then, an implicit function equation solution-based control scheme and an iterative solution-based control scheme are proposed, which ensure not only the closed-loop stability but also the output tracking for the controlled plant. An adaptive control framework for the controlled plant with uncertainties is also presented to illustrate the basic design procedure. The simulation results are given to demonstrate the desired system performance.

Adaptive Discrete-Time Flight Control Using Disturbance Observer and Neural Networks.

This paper studies the adaptive neural control (ANC)-based tracking problem for discrete-time nonlinear dynamics of an unmanned aerial vehicle subject to system uncertainties, bounded time-varying disturbances, and input saturation by using a discrete-time disturbance observer (DTDO). Based on the approximation approach of neural network, system uncertainties are tackled approximately. To restrain the negative effects of bounded disturbances, a nonlinear DTDO is designed. Then, a backstepping technique-based ANC strategy is proposed by utilizing a constructed auxiliary system and a discrete-time tracking differentiator. The boundness of all signals is proven in the closed-loop system under the discrete-time Lyapunov analysis. Finally, the feasibility of the proposed ANC technique is further specified based on numerical simulation results.