Addressing the dual challenges of complex road conditions in mine tunnels and adverse communication environments, this article proposes a reliable gain-scheduling control for industrial mining truck active suspension systems (MTASSs) under fading channels. First, by integrating interval type-2 (IT-2) fuzzy models and Markov processes, a dynamic model is constructed that captures system intrinsic uncertainties and external time-varying disturbances. Second, a simulated fading channel is established using prior statistical information to quantify signal attenuation, and a measurement censoring mechanism integrating dynamic monitoring and adaptive filtering is designed to enhance transmission reliability. On this basis, a time-sequence enhancement switching-type gain-scheduling (TESG) observer-assisted fuzzy control law with homogeneous polynomially parameter-dependent properties is developed. It incorporates a time-sequence enhancement using normalized fuzzy weighted membership functions (NFWMFs) from both current and historical moments, alongside a switching mechanism that exploits the implicit algebraic properties of these NFWMFs for more flexible control gain allocation, thereby reducing design conservatism. Experimental results demonstrate that the proposed approach improves the system’s anti-vibration capability and saves energy.

This article explores the optimized tracking control problem of autonomous aerial vehicle (AAV) and autonomous ground vehicle (AGV) swarm with mismatched disturbances and Byzantine attacks. Unlike traditional AGV-AAV models with different orders, multiple internal and external disturbances within the physical structure are considered in a novel second-order AAV-AGV swarm, which constructs the mismatched disturbances. Since the external environment may induce the swarm to generate and propagate false signals (called Byzantine attacks) to its neighbors, this article reinterprets it as the management of unknown variables in the control inputs. Then, a reinforcement-learning-based approach is introduced to address the unknown control inputs generated by Byzantine attacks. In conjunction with the Hamilton-Jacobi–Bellman (HJB) equation, an adaptive actor-critic–identifier (ACI) structure is designed using the gradient descent method. With the unknown terms estimated by ACI, an optimal robust control strategy under mismatched disturbances is proposed, and a pivotal scaling technique is employed for formation stability analysis. Finally, simulations and experimental results are performed to demonstrate the efficacy of the proposed approach.

This paper presents a finite-time tracking control for PDE-ODE coupled systems with dead-zone input and actuator failures. To begin with, a novel finite-time performance function and error transformation are designed for a reaction-diffusion equation to improve the transient and steady-state performance for PDE-ODE systems. In addition, to overcome the difficulties generated by the uncertain actuator failures and dead-zone input for coupled systems, a finite-time tracking controller and the adaptive laws are designed to compensate for the drastic effect of unknown nonlinearities. By applying neural networks approximation technology, the computational complexity and difficulty of controller design are significantly reduced. Unlike the existing stability results in the PDE-ODE systems, the developed method can guarantee the given transient performance by adaptive parameter tuning and the tracking error converges to the specified region in finite time. Moreover, the boundedness and convergence of all the signals in the closed-loop coupled systems are proved. Finally, the simulation example exhibits the effectiveness of the proposed control scheme. Note to Practitioners—The PDE-ODE coupled systems in modern mechanical engineering scenarios require better transient and steady-state performance as well as more agility of adjustment method. Because of the special infinite-dimensional characteristics of coupled systems compared to single PDE or ODE systems, it is more complex to design the adaptive finite-time controller for PDE-ODE systems. The finite-time performance function and error transformation designed in this paper aim to improve the stability of the coupled systems and adjust the preassigned performance more flexible. The settling time is independent and free of the parameters and initial values of the coupled systems which can be chosen by users according to actual requirements. Meanwhile, the constraints of actuator failures and dead-zone nonlinearity are compensated simultaneously for PDE-ODE systems via the presented adaptive neural networks finite-time control scheme.

This article investigates the cybersecurity problem of active suspension systems (ASSs) subject to random denial-of-service (DoS) attacks. Consider the nonlinearity and uncertainty of ASSs, the Takagi–Sugeno (T-S) fuzzy theory is applied to address these issues. In order to model various potential operating behaviors of the system, a high-order multiswitching-mode (HOMSM) T-S fuzzy control scheme is constructed, in which a series of free-weighted matrix sets are developed for different switching modes, such that the conservatism of controller design is reduced to a certain extent. By designing the HOMSM control method, a certain level of resistance to randomly activated DoS attacks can be achieved. With the help of homogeneous polynomial technology, several time-varying balance matrices are constructed for extracting the properties of different switching modes. Then, the exponential stability conditions of ASSs under DoS attacks can be derived, and the$H_{\infty }$performance criterion is guaranteed. Finally, the theoretical results are validated by the hardware-in-the-loop experiments.

This article investigates the event-triggered leaderless consensus control problem for fractional-order multiagent systems (FOMASs), where both the agent-to-agent communication channel and the controller-to-actuator communication channel are based on the events. A filter is introduced to transform the original high-order system into a first-order one, greatly simplifying the complexity of controller design compared to the traditional backstepping. Further, the convergence of filtered output signals is proved to be consistent with that of the outputs of agents themselves. Superior to the traditional event-triggered scheme, two dynamic variables are designed for the triggering conditions of the communication among agents and the controller update, respectively. Via elaborately constructing the dynamic variables, zero-error leaderless consensus can be achieved instead of only ultimately uniformly bounded result. It is proved that the proposed control strategy can guarantee better control performance of leaderless consensus under limited communication resources, and Zeno behavior is excluded. Finally, two examples are provided to verify the effectiveness of our proposed control approach.

This article investigates the finite-time containment control problem for fractional-order nonlinear multiagent systems (FOMASs) with event-triggered inputs. First, a new Lyapunov stability lemma is developed, which provides a basic approach for the completely unknown nonlinear fractional-order system to realize finite-time convergence. Considering the containment control for FOMASs, the restricted assumption that the derivatives of leaders' trajectories are known to the followers is removed, and only the boundedness of derivatives is required in this article, whose upper bound need not be known. To reduce the burden of communication, an event-triggered condition consisting of the control input and a decreasing function related to the containment errors is devised, which can provide more design freedom to balance the system performance and communication resources. According to the proposed fractional-order finite-time convergence lemma, a distributed adaptive containment control scheme is developed, such that each follower can be steered to the convex hull spanned by the leaders in finite time. Simulation examples further demonstrate the effectiveness of our proposed method.

In this article, the constrained adaptive control strategy based on virotherapy is investigated for organism using the medicine dosage regulation mechanism (MDRM). First, the tumor-virus-immune interaction dynamics is established to model the relations among the tumor cells (TCs), virus particles, and the immune response. The adaptive dynamic programming (ADP) method is extended to approximately obtain the optimal strategy for the interaction system to reduce the populations of TCs. Due to the consideration of asymmetric control constraints, the nonquadratic functions are proposed to formulate the value function such that the corresponding Hamilton-Jacobi-Bellman equation (HJBE) is derived which can be deemed as the cornerstone of ADP algorithms. Then, the ADP method of a single-critic network architecture which integrates MDRM is proposed to obtain the approximate solutions of HJBE and eventually derive the optimal strategy. The design of MDRM makes it possible for the dosage of the agentia containing oncolytic virus particles to be regulated timely and necessarily. Furthermore, the uniform ultimate boundedness of the system states and critic weight estimation errors is validated by Lyapunov stability analysis. Finally, simulation results are given to show the effectiveness of the derived therapeutic strategy.

This paper investigates the dynamic memory eventtriggered (DMET) fuzzy optimized consensus control for nonlinear multi-agent systems (MASs) with quantized reference signal. To alleviate the communication burden, a dual communication channels DMET scheme is proposed, which encompasses eventdriven communication for interactions among followers and communication between controllers and actuators. In comparison to the traditional dynamic event-triggered (DET) scheme, the devised DMET scheme incorporates historical information of the dynamic variable, resulting in longer triggering time intervals. Note that the problem of non-differentiability in backstepping method is generated by the event-triggered communication and quantization. To address this challenge, a smooth signal generator is introduced to reconstruct the step signals into the differentiable new one. Meanwhile, a reinforcement learning (RL) approach is employed to optimize the controllers, which utilizes an identifiercritic-actor architecture with fuzzy logic system (FLS) approximations at each step of backstepping method. The effectiveness of the proposed control method is demonstrated through simulations, confirming its capabilities in achieving optimized consensus control while mitigating communication loads.

This article investigates the privacy-preserving consensus control and containment control for strict-feedback multiagent systems (MASs). For the agents possessing sensitive state information that needs safeguarding, an output mask function is employed, which ensures that the true state value remains indiscernible to the other agents during the process of information interaction. However, the introduction of mask function increases the complexity of the cooperative control design for MASs, given the untrustworthiness of the received state information from other agents. To address this challenge, an adaptive backstepping-based control algorithm is proposed, relying on the masked states of neighboring agents. Simultaneously, a dynamic event-triggered control with the reset mechanism is introduced to save communication resources, in which the dynamic of the additional variable is determined by the preset conditions. Based on the proposed event-triggered privacy-preserving control method, it is ensured that the initial state value of each agent remains undisclosed, and the tracking errors can converge to a residual set around zero. Similar results are extendable to the privacy preservation containment control for MASs. Finally, the efficacy of the proposed control method is validated through two illustrative examples.

This article focuses on the adaptive bipartite containment control problem for the nonaffine fractional-order multi-agent systems (FOMASs) with disturbances and completely unknown high-order dynamics. Different from the existing finite-time theory of fractional-order system, a lemma is developed that can be applied to actualize the aim of finite-time bipartite containment for the considered FOMASs, in which the settling time and convergence accuracy can be estimated. Via applying the mean-value theorem, the difficulty of the controller design generated by the nonaffine nonlinear term is overcome. A neural network (NN) is employed to approximate the ideal input signal instead of the unknown nonaffine function, then a distributed adaptive NN bipartite containment control for the FOMASs is developed under the backstepping structure. It can be proved that the bipartite containment error under the proposed control scheme can achieve finite-time convergence even though the follower agents are subjected to completely unknown dynamic and disturbances. Finally, the feasibility and validity of the obtained results are exhibited by the simulation examples.

The article investigates full information control problem for switched neural networks subject to fault and disturbance. First, the main objective is realizing interval stability and zero tracking error under condition that neither of the neuron states’ vectors including the plant and reference models is available. Second, the desired full information controller and neural networks’ observer are designed to ensure observer-based dynamic error system mean-square exponentially stable with sufficient condition of strict weight$\mathcal {H}_{\infty } /\mathcal {H}_{-}$performance levels. Finally, we concentrate on stability analyses and fault tolerance for switched neural networks with fault accompanied by disturbance through linear matrix inequalities (LMIs), Lyapunov function, and average dwell time, discussing it according to different values of fault. Finally, simulation examples are listed to account for the availability and effectiveness of the research methodology.

This study focuses on dissipativity-based fault detection for multiple delayed uncertain switched Takagi–Sugeno fuzzy stochastic systems with intermittent faults and unmeasurable premise variables. Nonlinear dynamics, exogenous disturbances, and measurement noise are also considered. In contrast to the existing study works, there is a wider range of applications. An observer is explored to detect faults. A controller is studied to stabilize the considered system. A piecewise fuzzy Lyapunov function is collected to obtain delay-dependent sufficient conditions by means of linear matrix inequalities. The designed observer has less conservatism. In addition, the strict$(\mathfrak {Q},\mathfrak {S},\mathfrak {R})-{\epsilon }-$dissipativity performance is achieved in the residual dynamic. Besides, the elaborate$H_{\infty }$performance and the elaborate$H\_{}$performance are also acquired. Finally, the availability of the method in this study is verified through two simulation examples.

The cooperative bipartite containment control problem of linear multiagent systems is investigated based on the adaptive distributed observer in this article. The graph among the agents is structurally balanced. A novel distributed error term is designed to guarantee that some outputs of the followers converge to the convex hull spanned by the leaders, and the other followers' outputs converge to the symmetric convex hull. The matrices of the exosystems are not available for each follower. A general method is presented to verify the validity of a novel distributed adaptive observer rather than the previous approach. In other words, the definition of the M -matrix is not necessary in our result. Based on the distributed adaptive observer, an output-feedback control protocol is designed to solve the bipartite containment control problem. Finally, a numerical simulation is given to illustrate the effectiveness of the theoretical results.

In this article, the finite-time containment control problem of the nonlinear multiagent systems (MAS) is investigated, in which the follower agents are subjected to full state constraints. The fuzzy logic system is employed to approximate the uncertain dynamic of the nonlinear MAS. A new nonlinear transformation function (NTF) is proposed, which serves as a unified tool for coping with the systems subjected to state constraints and no constraint. By combing the variable transformation with the proposed NTF, the original systems with state constraints are converted into the equivalent free-constrained systems. Moreover, as long as the boundedness of the states in transformed systems is guaranteed, the states of the original MAS do not transgress the preassigned boundaries. Based on the finite-time stability theory, the adaptive fuzzy control scheme is constructed for the transformed systems. It is proved that the outputs of all the followers are driven to converge to the convex hull spanned by the leaders in a finite time, and all the signals in the closed-loop systems are bounded in sense of mean square. The full state constraints for the followers are not violated all the time. Especially, the control structure does not need to change even there is no constraint imposes on the state. Finally, simulation examples are given to verify the effectiveness of the proposed finite-time control scheme.

This article investigates the prescribed finite-time control for the nonlinear system with uncertain nonlinearity and unknown control directions. Different from the existing finite/fixed-time control methods on the basis of the fractional-order state feedback, the proposed finite-time control in this article only employs regular state feedback. The key is that a novel prescribed finite-time performance function is introduced such that the controller design is under the standard Lyapunov stability theoretical framework. A lemma is developed as a tool for coping with the coexistence of multiple Nussbaum gains for the stochastic system with unknown control directions. Fuzzy logic systems are used to approximate the uncertain nonlinear functions. Applying the backstepping structure, an adaptive fuzzy control scheme is developed. The salient characteristic of the proposed control is that the finite convergence time and the accuracy of the tracking error are independent of the initial states and design parameters, and that can be assigned in a prior. Two examples are given to demonstrate the feasibility and effectiveness of the proposed control scheme.

This article focuses on finite-time adaptive fuzzy output-feedback control for a class of nontriangular nonlinear systems with full-state constraints and unmeasurable states. Fuzzy-logic systems and the fuzzy state observer are employed to approximate uncertain nonlinear functions and estimate the unmeasured states, respectively. In order to solve the algebraic loop problem generated by the nontriangular structure, a variable separation approach based on the property of the fuzzy basis function is utilized. The barrier Lyapunov function is incorporated into each step of backstepping, and the condition of the state constraint is satisfied. The dynamic surface technique with an auxiliary first-order linear filter is applied to avoid the problem of an "explosion of complexity." Based on the finite-time stability theory, an adaptive fuzzy controller is constructed to guarantee that all signals in the closed-loop system are bounded, the tracking error converges to a small neighborhood of the origin in a finite time, and all states are ensured to remain in the predefined sets. Finally, the simulation results reveal the effectiveness of the proposed control design.

This article addresses an adaptive fuzzy control for the nonstrict-feedback nonlinear systems with asymmetric time-varying full state constraints. To prevent the state constraints being violated, the nonlinear state-dependent function (NSDF) is introduced instead of the tradition barrier lyapunov function (BLF). The constrained systems are transformed into a novel free-constrained systems based on the NSDF. A direct approach is given to cope with the state constraints. Fuzzy logic system is utilized to approximate the uncertain nonlinear system functions. The new affine variables are constructed for the transformed systems, and the prior knowledge of the control gains is not required necessarily. Under the designed control scheme, the boundedness of the signals in the closed-loop systems is guaranteed certainly, and the asymmetric time-varying constraints are maintained. This proposed method removes the feasibility condition on the virtual controller derived from the method of BLF. The effectiveness of the proposed control strategy is verified by two simulation examples.

This article addresses an adaptive fuzzy finite-time control for a class of uncertain strict-feedback nonlinear systems with backlashlike hysteresis and stochastic disturbances. At first, a novel criterion of semiglobally finite-time stability in probability (SGFSP) is established based on Lyapunov function method. Under the proposed stability criterion, an adaptive fuzzy finite-time control scheme is designed. In the design process of the controller, command filter technique is introduced to overcome the problems of “explosion of complexity” and “singularity” inhered in the traditional adaptive finite-time control based on the backstepping method. Meanwhile, via constructing the corresponding error compensating systems, the effect of errors generated by the command filters is reduced, such that the original systems have more better tracking performance. To cope with the influence of backlashlike hysteresis input, an auxiliary system is constructed, in which the output signal is applied to compensate the effect of the hysteresis. It is shown that the tracking error can converge to a small neighborhood of original in finite time, and the closed-loop system is SGFSP under the constructed controller. Finally, the effectiveness of the proposed control strategy is further verified by two simulation examples.