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Summary: This paper investigates the problem of adaptive dynamic surface control for pure‐feedback time‐varying state constrained nonaffine nonlinear system. A continuous and semibounded condition is proposed of nonaffine function to ensure that the system can be controlled, and the invariant set is introduced for this mild condition. By employing the dynamic surface control technique, the "complexity explosion" problem caused by backstepping technique is averted in developed control method. Robust compensators are devised to weaken poor effect of disturbances and uncertainties. The time‐varying barrier Lyapunov functions are adopted to dispose the problem of time‐varying state constrains. Moreover, it is proved that the whole closed‐loop signals are bounded, the tracking error can converge to a small neighborhood of zero, and the system states are insured to maintain in the predefined compact sets. Finally, some simulation results are provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: This paper is concerned with the neural‐based decentralized adaptive control for interconnected nonlinear systems with prescribed performance and unknown dead zone outputs. In the controller design procedure, neural networks are employed to identify unknown auxiliary functions, and the control design obstacle caused by the output nonlinearity is resolved via introducing Nussbaum function. Then, a reliable neural decentralized adaptive control is developed through incorporating the backstepping method and the prescribed performance technique. In the light of Lyapunov stability theory, it is verified that the proposed control scheme can ensure that all the closed‐loop signals are bounded, and can also guarantee that the tracking errors remain within a small enough compact set with the prescribed performance bounds. Finally, some simulation results are given to illustrate the feasibility of the devised control strategy. [ABSTRACT FROM AUTHOR]

Summary: In this paper, an adaptive output‐feedback control problem is investigated for nonlinear strict‐feedback stochastic systems with input saturation and output constraint. A barrier Lyapunov function is used to solve the problem of output constraint. Then, fuzzy logic systems are used to approximate the unknown nonlinear functions, and a fuzzy state observer is designed to estimate the unmeasured states. To overcome the difficulties in designing the control signal in the saturation, we introduce an auxiliary signal in the n + 1th step in the deduction. By combining Nussbaum technique and the adaptive backstepping technique, an adaptive output‐feedback control method is developed. The proposed control method not only overcomes the problem of the compensation for the nonlinear term from the input saturation but also overcomes the problem of unavailable state measurements. It is proved that all the signals of the closed‐loop system are semiglobally uniformly ultimately bounded. Finally, the effectiveness of the proposed method is verified by the simulation results. [ABSTRACT FROM AUTHOR]

This paper considers the output synchronization for nonidentical second-order integral dynamics with external disturbance. The leader’s signal can only be received by parts of the followers, and all the followers have different dynamics. An adaptive distributed compensator is utilized to estimate the leader’s signal and system matrix. Since the states’ information of the agents can not be measured by the others, a novel distributed reduced-dimension observer is designed to estimate the unmeasurable states. Based on the adaptive distributed compensator and reduced-dimension observer, a dynamic output feedback control law is designed to solve the output synchronization problem. Finally, an example is given to illustrate the validity of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper investigates the consensus tracking problem of nonlinear multi-agent systems with state constraints and unknown disturbances. An observer is presented for the case that the states of each follower and its neighbors are unmeasurable. State constraint for multi-agent systems is a challenging problem. Barrier Lyapunov functions are applied in this paper to deal with this difficulty. Then based on adaptive back-stepping control approach and dynamic surface control technique, an adaptive fuzzy distributed controller is proposed to guarantee that the tracking errors between all followers and the leader converge to a small neighborhood of the origin. Moreover, it is proved that all the signals in the multi-agent systems are semi-globally uniformly ultimately bounded (SUUB). Finally, some numerical simulation results are presented to testify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper considers the finite-time tracking control problem for the strict-feedback nonlinear continuous systems involving input saturation and output constraints. A sequence of desired and auxiliary virtual control signals and real control input is designed to derive a representation of the system estimation errors and stabilize the system. The proposed approach is further developed via a finite-time stability theory, barrier Lyapunov function, and neural network approximation scheme to achieve an expected performance of the considered system. According to the proposed scheme, we solve the finite-time tracking control problem of the nonlinear systems with input saturation. Then, a theorem is provided to address that all the signals and system states are bounded, and the system output is driven to track the reference signal in a finite time to a small neighborhood of zero and remains in the predefined compact sets. The effectiveness of the proposed scheme is confirmed via two simulation examples. [ABSTRACT FROM AUTHOR]

• A novel peroxygenase from agaricus bisporus var. bisporus (Abvb UPO) was identified by database mining. • Abvb UPO was recombinatly expressed and purificated in aspergillus niger initially characertized. • Abvb UPO catalyzes the H 2 O 2 -driven oxyfunctionalisation of alkanes, alkenes, alcohols and thioethers. • Abvb UPO catalyzes the H 2 O 2 -driven hydroxylation of linear saturated fatty acids. Unspecific peroxygenases (UPOs) represent an emerging class of catalysts for the selective oxyfunctionalisation of C H- and C = C groups. Until now, only a few UPOs have been characterised. In this study, we report a new peroxygenase identified from the Unspecific Peroxygenase Database. The UPO from Agaricus bisporus var. bisporus (Abvb UPO) has been heterologously expressed in Aspergillus niger and initially characterised with respect to its basic biochemical features. Furthermore, its catalytic properties were evaluated with enzymatic cascade reactions of choline oxidase (AnChOx) and Abvb UPO, which the AnChOx provided H 2 O 2 necessary via reductive activation of oxygen in situ. Three types of oxyfunctionalizations, such as hydroxylation of ethylbenzene, epoxidations of styrene and cyclohexene, sulfoxidations of methyl phenyl sulfide and phenyl vinyl sulfide, were successfully achieved. We also investigated the activity of Abvb UPO on fatty acids in some more detail. The experimental results show that Under the above conditions, Abvb UPO had the higher activity for cyclohexene epoxidation and sulfonation of sulfide substrates. The concentration of epoxy cyclohexane was 2.91 mM, and the concentration of methyl phenyl sulfoxide was 3.69 mM. The regioselectivity of Abvb UPO was ω-1 bonds position of linear saturated fatty acid. All in all, Abvb UPO exhibits some interesting differences which may put the basis for further understanding of the factors determining peroxygenase selectivity. [Display omitted] [ABSTRACT FROM AUTHOR]

In this study, we resolve the optimal control problem for nonlinear continuous systems with unknown internal dynamics using policy iteration (PI)-based fuzzy adaptive dynamic programming, where the cost functional is approximated by fuzzy basis functions (FBFs), which are convenient for solving the nonlinear Hamilton–Jacobi–Bellman equation. The cost functional is described in a discrete form and the weighted residuals method in the least squares sense is then employed to update the weights of the FBFs using the PI algorithm. The iteration process terminates when the weights converge and the optimal controller can then be obtained in a simple manner. It should be noted that FBFs are used widely in real applications due to their satisfactory performance at approximating the nonlinear functional. A numerical example is given to illustrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

In this paper, the inverse optimal approach is employed to design distributed consensus protocols that guarantee consensus and global optimality with respect to some quadratic performance indexes for identical linear systems on a directed graph. The inverse optimal theory is developed by introducing the notion of partial stability. As a result, the necessary and sufficient conditions for inverse optimality are proposed. By means of the developed inverse optimal theory, the necessary and sufficient conditions are established for globally optimal cooperative control problems on directed graphs. Basic optimal cooperative design procedures are given based on asymptotic properties of the resulting optimal distributed consensus protocols, and the multiagent systems can reach desired consensus performance (convergence rate and damping rate) asymptotically. Finally, two examples are given to illustrate the effectiveness of the proposed methods. [ABSTRACT FROM PUBLISHER]

• A novel peroxygenase from Galerina marginata (GmaUPO) was identified by database mining • GmaUPO was recombinatly expressed in Pichia pastoris initially characertized • GmaUPO catalyzes the H2O2-driven oxyfunctionalisation of alkanes, alkenes, alcohols and thioethers • Molecular modelling puts the basis for a molecular understanding of factors influencing GmaUPO's activity and selectivity Unspecific peroxygenases (UPOs, EC 1.11.2.1) are promising oxyfunctionalization catalysts because of their unique stereoselectivity. However, so far only a few UPOs have been reported. In this study, gene mining was used to identify a gene from Galerina marginata that coded for a novel UPO (Gma UPO). Gma UPO was expressed in Pichia pastoris X-33 by scale-up fermentation (the UPO activity of the culture supernatant was 118 U/L). Gma UPO exhibited a molecular weight of 40 kDa and exhibited highest activity at 35°C and pH 9, respectively. Furthermore, Gma UPO was demonstrated to catalyze the epoxidation, sulfoxidation, and hydroxylation of common substrates, particularly fatty acids such as tridecanoic acid. The molecular basis for Gma UPO regioselectivity for fatty acid hydroxylation was explored by molecular modelling. The regioselectivity was mostly governed by the architecture of the enzyme's active site. [Display omitted] [ABSTRACT FROM AUTHOR]

Summary: In this paper, a secure cooperative control strategy which integrates the data‐driven control method and the backstepping framework is proposed for a class of discrete‐time nonlinear multiagent systems with denial‐of‐service (DoS) attacks. First, by designing special index functions, we overcome the algebraic loop problem in the backstepping framework to propose a data‐driven backstepping control method which does not use the Lyapunov theory to design the controller. Second, the problem of DoS attacks in communication channels is solved by switching topology strategy. Then, an event‐triggered mechanism based on synchronization error is used to save communication resources, which does not have a great negative impact on the control performance. Moreover, an event‐triggered cooperative strategy is constructed to ensure that all signals and tracking errors of the closed‐loop system are bounded. Finally, two simulation examples verify the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

• The problem of the adaptive fuzzy tracking control for high-order stochastic MASs with unknown dead-zone input has been solved. • This paper extends the reduced-order observer to the high-order stochastic nonlinear MASs, which not only solves the problem of unmeasurable states but also reduces the computational burden • In the controller design process, a new design scheme is constructed to avoid the limitation that the observer can not contain unknown coefficients. This paper studies observer-based adaptive fuzzy leader-following tracking control for high-order stochastic nonlinear multi-agent systems with unknown dead-zone input. First, in order to deal with unmeasurable state variables, a reduced-order observer is established for each follower, which could make the computational burden of the systems reduced. Second, the fuzzy logic systems are introduced to approximate the stochastic disturbances and the unknown functions. Then, an adaptive fuzzy tracking controller is designed for high-order multi-agent systems with unknown dead-zone input by using the backstepping approach. Furthermore, it can be proved that all the closed-loop signals are bounded in probability, and the consensus error of the stochastic multi-agent systems can converge to a small region of origin under the Lyapunov stability theory. Finally, a simulation example is given to show the effectiveness of the designed algorithm. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of adaptive prescribed performance control for a category of uncertain nonlinear systems with fuzzy dead-zone input. Through employing the neural networks (NNs) and designing a state observer, the unknown functions and unmeasured system states are approximated, respectively. The problem of fuzzification caused from dead-zone input is handled via using the center-of-gravity theorem. Then, an integrated fuzzy controller is constructed by adopting integrated design approach. It is proved that the proposed integrated fuzzy controller can ensure that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a prescribed domain. Finally, some simulation results are shown to demonstrate feasibility of the presented method. [ABSTRACT FROM AUTHOR]

In this paper, an observer-based adaptive fixed-time control strategy is put forward for uncertain nonstrict-feedback nonlinear systems subject to prescribed performance, full-state constraints and event-triggered mechanism. The difficulty of control design is to use the state observer to estimate unmeasurable states for nonstrict-feedback form in the fixed-time convergence setting. Neural networks are implemented to model the unknown nonlinearities of system. Via introducing fixed-time theory and asymmetric barrier Lyapunov function (ABLF), the fixed-time control problem of the full-state contrained nonlinear system with prescribed performance is solved. Meanwhile, the problem of "explosion of complexity" caused by backstepping technique is averted by utilizing the dynamic surface control technique. Furthermore, an event-triggered controller is devised, which can significantly save communication resources. Moreover, it is concluded that all signals involved are bounded, full-state constraints are not transgressed, tracking error remains within a prescribed domain and Zeno phenomenon is completely circumvented. Finally, the effectiveness of the proposed algorithm is verified by some simulation results. [ABSTRACT FROM AUTHOR]

This paper presents the output-constrained control algorithm for non-affine multi-agent systems (MASs) with actuator faults and unknown dead zones. The error transformation method is employed to keep initial connectivity patterns in the non-affine MASs for consensus tracking control. The radial basis function neural networks are utilized to estimate the unknown nonlinear functions. Furthermore, the Nussbaum function is used to overcome partially unknown control direction problem. To address the problem of the constrained control, a state transformation technique is presented. In addition, the fault-tolerant consensus tracking protocol is designed to reduce the effects of actuator faults and dead zones. Furthermore, it is shown that the consensus tracking errors are cooperatively semi-globally uniformly ultimately bounded. Finally, the effectiveness of the proposed approach is illustrated by some simulation results. [ABSTRACT FROM AUTHOR]

This paper presents the event-triggered consensus control problem for a class of multi-agent systems (MASs) in the non-affine form. An error-transformation-based design method is presented in the non-affine MASs. The radial basis function neural networks (RBFNNs) are used to approximate the unknown nonlinear functions. The dynamic surface control (DSC) technique is adopted to obtain parameters of the virtual controller through a first-order filter, which can make the calculation easier. An event-triggered control strategy is proposed to reduce communication frequency. Furthermore, it is further shown that the consensus tracking errors are semi-globally uniformly ultimately bounded (SGUUB). Finally, simulation results illustrate the effectiveness of the designed controller. [ABSTRACT FROM AUTHOR]

This paper considers the finite-time containment control problem for nonstrict-feedback nonlinear multi-agent systems (MASs) in the presence of unmodeled dynamics. A new variable separation approach is developed to overcome the difficulty of unknown functions with nonstrict-feedback structure in the backstepping design process. The second-order tracking differentiator is extended to the nonstrict-feedback nonlinear MASs for avoiding the problem of "explosion of complexity". Neural networks are used to approximate the unknown nonlinear functions. With introducing a dynamic signal, the difficulty from the unmodeled dynamics is successfully solved. And, by introducing a finite-time stability criterion, an new adaptive finite-time containment control scheme is designed, which ensures all signals are bounded. Moreover, the output signals of all followers converge to a convex hull spanned by the outputs of the leaders. Finally, a simulation example illustrates the effectiveness of the developed control scheme. [ABSTRACT FROM AUTHOR]

In view of the input dead‐zone, unknown control direction and difficulty in satisfying the prescribed performance that suffered in practical systems, an improved prescribed performance‐based adaptive control scheme is stressed for uncertain nonlinear systems in this paper. Firstly, by adopting a characteristic function, the input dead‐zone is linearized to a model with bounded perturbation. To settle the "computation complexity" issue, an adaptive controller is built via command filter design method, where the fuzzy logic systems are introduced to approximate the unknown nonlinearities. Meanwhile, the Nussbaum function is brought in controller design to counter the hardship of unknown control direction. Besides, the tracking error can be restricted in the prescribed boundary in finite time with the improved performance function. The presented control approach can not only ensure the finite‐time convergence property of tracking error and the boundedness of all signals in the closed‐loop system, but also easily implement in engineering. Finally, the simulation examples confirm the validity of the designed control scheme. [ABSTRACT FROM AUTHOR]