This article is concerned with the sampled-data-based robust safety control (SDRSC) problem for switched systems. A novel sampled-data-based single barrier function (SDSBF) method is proposed to ensure the safety of switched systems with unknown disturbances under zero-order-hold (ZOH) controllers. The SDSBF method permits subsystems to lack safety over the whole safe set, which relaxes the traditional requirement that all subsystems are safe. Also, the influence on safety of switched systems caused by the difference of sampling instant and switching instant is analyzed, and a switching signal with dwell time is introduced to quantify and estimate this influence. Furthermore, both safety and stability of switched systems under ZOH controllers are obtained by the union of SDSBF and single Lyapunov function (SLF), without requiring the safety and stability for subsystems. To find SDSBF and SLF, an executable condition, including a switching law with dwell time, is established based on convex combination techniques, avoiding the Zeno phenomenon. Finally, an application on vehicle driving is provided to verify the proposed methods.

In this article, the problem of multiple event-triggering communications-based adaptive neural network (NN) output-feedback control is investigated for a class of switched uncertain nonlinear systems. In particular, the NNs with self-growing/pruning neurons are utilized to handle unknown nonlinearities of system. By developing backstepping, an event-triggered switched NN observer, event-triggered adaptive NN controllers of subsystems and three novel switching dynamic event-triggering mechanisms (ETMs) are constructed. Multiple event-triggering communications from sensor to controller and observer to controller and controller to actuator are thus achieved under arbitrary switchings. Naturally, more communication burdens can be reduced compared with those existing single or dual event-triggering communications methods for non-switched and switched systems. Note that one difficulty caused by dual asynchronous switchings among candidate subsystems and candidate controllers and candidate observers is overcome. Also, other difficulties caused by finding an adjustable positive lower bound of interexecution times for each ETM and the errors between continuous-time and sampled-data-based basis function vectors of NNs are overcome. A switched one-link robotic manipulator system is employed to illustrate the validity of the scheme developed.

This article investigates the problem of safe coverage for multiagent systems with parameter uncertainties. A novel distributed control strategy is proposed to simultaneously guarantee safety and coverage for multiagent systems based on adaptive artificial potential function (AAPF) and adaptive control barrier function (ACBF). In particular, a logic switching mechanism based on multiple identification models is integrated into a coverage controller while the transient performance of multiagent systems is effectively improved. Also, a framework for safe coverage in multiagent systems is presented in the context of parametric uncertainties. In this framework, a nominal controller is obtained by using the AAPF method. Furthermore, the nominal controller undergoes modification through the application of ACBF based on quadratic programs to achieve safe coverage. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control strategy.

In this article, the global event-triggered (ET) funnel tracking control problem is studied for a class of switched nonlinear systems with structural uncertainties, where the solvability of the control problem for each subsystem is not needed. A switching multiple Lyapunov functions (MLFs) method is established, where MLFs are designed to handle switched inverse dynamics, and a switching barrier Lyapunov function is constructed to address switched sampled errors that may compromise system stability. This is achieved alongside a new switching dynamic event-triggering mechanism (DETM). By combining this method with backstepping, a dwell-time state-dependent switching law and an ET funnel controller of each subsystem are constructed, effectively eliminating the issue of the "explosion of complexity" encountered in traditional backstepping without using dynamic surface control or command filters. Additionally, the designed switching DETM ensures that the tracking error always evolves within a performance funnel in any consecutive triggering interval, excluding Zeno behavior, and guaranteeing positive constant lower bounds for two consecutive triggering intervals and any switching interval, respectively. Finally, an example is provided to show the validity of the theoretical results.

A dual event-triggered adaptive fuzzy resilient control scheme for a class of switched nonlinear systems with vanishing control gains under mixed attacks is proposed in this article. The scheme proposed achieves dual triggering in the channels of sensor-to-controller and controller-to-actuator by designing two new switching dynamic event-triggering mechanisms (ETMs). An adjustable positive lower bound of interevent times for each ETM is found to preclude Zeno behavior. Meanwhile, mixed attacks, that is, deception attacks on sampled state and controller data and dual random denial-of-service attacks on sampled switching signal data, are handled by constructing event-triggered adaptive fuzzy resilient controllers of subsystems. Compared with the existing works for switched systems with only single triggering, more complex asynchronous switching caused by dual triggering and mixed attacks and subsystem switching is addressed. Further, the obstacle caused by vanishing control gains at some points is eliminated by proposing an event-triggered state-dependent switching law and introducing vanishing control gains into a switching dynamic ETM. Finally, a mass-spring-damper system and a switched RLC circuit system are applied to verify the obtained result.

This article solves the problem of distributed event-triggered fuzzy control for a class of heterogeneous switched nonlinear multiagent systems (MASs) under switching topologies. A distributed dynamic compensator is designed for addressing the consensus problem of switched MASs with unknown deception attacks. To mitigate the effect of deception attacks, an attack compensator and a new distributed event-triggered fuzzy control method are presented by exploring a common Lyapunov function method and backstepping. Also, if switching occurs between two adjacent triggering instants, then asynchronous switching between candidate controllers of subsystems and subsystems will occur. Furthermore, the asynchronous switching is addressed by constructing a new switching dynamic event-triggered mechanism and distributed event-triggered fuzzy controllers. Also, Zeno phenomenon is excluded by guaranteeing that a lower bound on interevent times is a positive constant. Finally, a practical example is proposed to illustrate the effectiveness of the developed method.

In this article, an event-triggered state-dependent switching method is proposed to address adaptive fuzzy control problem for a class of multi-input multi-output switched nonlinear systems with IOCs. IOCs mean that output constraints can occur in some intermittent time intervals rather than for all time. Unlike traditional continuously monitored state-dependent switching laws, an event-triggered state-dependent switching law is constructed to overcome the difficulty of the corresponding problem of subsystems being unsolvable, which is caused by unknown control gains of subsystems going through zero. Meanwhile, a positive lower bound of consecutive switching instants is derived. Also, it is achieved that adaptive fuzzy controller and switched update laws are event-triggered by designing several new switching event-triggering mechanisms, which results in the reduction of communication and computation loads. Furthermore, by developing some modified shifting functions and barrier functions, more general intermittent output constraints are handled. The effectiveness and applicability of the method proposed are verified by a mass-spring-damper system.

This article proposes a switching event-triggered (ET) adaptive neural network (NN) output-feedback control scheme for a family of networked switched nonlinear systems under hybrid deception and denial-of-service (DoS) attacks in sensor-to-controller channel. The concept of “effective” DoS attacks is introduced for removing the assumption of the time sequences of DoS off/on and on/off transitions being known. In the active and inactive intervals of effective DoS attacks, an ET dual-switched NN observer, a dual-switched update law, common coordinate transformations in backstepping and a switching adaptive NN controller of each subsystem are constructed. Then, hybrid attacks are coped with and the difficulty in stability analysis caused by different coordinate transformations is overcome. Moreover, by designing a new switching dynamic event-triggering mechanism and a new Lyapunov function dependent on the switching signal of controller and DoS attacks, asynchronous switching between candidate subsystems and candidate observers and controllers is handled, and the convergence of tracking error to a small neighborhood around the origin is proved under a new class of switching signals with average dwell time. The effectiveness and applicability of the scheme proposed are illustrated by a switched one-link robotic manipulator system.

In this article, a novel safety-critical model reference adaptive control approach is established to solve the safety control problem of switched uncertain nonlinear systems, where the safety of subsystems is unnecessary. The considered switched reference model consists of submodels possessing safe system behaviors that are governed by switching signals to achieve satisfactory performances. A state-dependent switching control technique based on the time-varying safe sets is proposed by utilizing the multiple Lyapunov functions method, which guarantees the state of the subsystem is within the corresponding safe set when the subsystem is activated. To deal with uncertainties, a switched adaptive controller with different update laws is constructed by resorting to the projection operator, which reduces the conservatism caused by the common update law adopted in all subsystems. Moreover, a sufficient condition is obtained by structuring a switched time-varying safety function, which ensures the safety of switched systems and the boundedness of error systems in the presence of uncertainties. As a special case, the safety control problem under arbitrary switching is considered and a corollary is deduced. Finally, a numerical example and a wing rock dynamics model are provided to verify the effectiveness of the developed approach.

This article is concerned with the problem of dynamic event-triggered adaptive neural network (NN) control for a class of switched strict-feedback uncertainty nonlinear systems. A novel switched command filter-based dynamic event-triggered adaptive NN control approach is set up by exploiting the backstepping and command filter and the common Lyapunov function method. Since adaptive controllers of subsystems are event triggered, then if the switching happens between any two consecutive triggering instants, asynchronous switching will arise between candidate controllers of subsystems and subsystems. Unlike the existing literature, where maximum asynchronous time is restricted, without any strict limitations on maximum asynchronous time being needed in this article, the asynchronous switching problem is directly handled by proposing a novel switching dynamic event-triggered mechanism (DETM) and event-triggered adaptive controllers of subsystems. Moreover, a piecewise constant variable is introduced into the switching DETM, which overcomes the difficulty of switched measurement error being discontinuous. Also, a strictly positive lower bound of interevent times is obtained. Finally, a continuous stirred tank reactor system and a numerical example are presented to demonstrate the effectiveness of the developed approach.

This article focuses on the problem of adaptive safe stabilization for a class of nonlinear control affine systems with parameter uncertainties. Two novel definitions of adaptive control Lyapunov function (ACLF) and adaptive control barrier function (ACBF) are first introduced, which use multiple identification models combined with a logic switching mechanism for parameter adaptation. The switching mechanism can improve the transient performance of nonlinear systems. Also, a multiunmanned aerial vehicle (multi-UAV) safe collaborative transportation framework based on ACLF and ACBF is provided in the presence of parametric uncertainties, in which a nominal controller is derived by using the proposed ACLF with a quadratic programming (QP) algorithm, and the nominal controller is further modified by the ACBF-QP algorithm for safety filter to achieve adaptive safe stabilization. Finally, simulation results for safe collaborative transportation of dual unmanned aerial vehicles in three-dimensional space are presented to demonstrate the effectiveness of the proposed design approach.

In this article, the problem of decentralized dynamic event-triggered (ET) adaptive fuzzy control for a class of strong interconnected switched uncertain nonlinear systems with unstable inverse dynamics and unknown control gains is studied. By utilizing the adaptive backstepping technique and new fuzzy logic systems, decentralized ET adaptive fuzzy controllers for subsystems and a new type of switching decentralized dynamic event-triggering mechanisms are constructed. Then, the asynchronous switching problem between candidate controllers and subsystems is addressed, and the upper bound of asynchronous time is not directly restricted. Meanwhile, by ensuring that the interexecution times are lower bounded by a positive constant, Zeno behavior is avoided. Furthermore, it is proved by the multiple Lyapunov functions method that all signals in the switched closed-loop system are bounded under a new class of slow switching signals. The validity of the derived result is illustrated using two examples which include a two inverted pendulum system.

This article addresses the event-triggered output-feedback funnel tracking control problem for a class of switched nonlinear systems. A novel switched-observer-based event-triggered adaptive fuzzy funnel control approach for solving the problem is proposed by exploiting the average dwell time method and backstepping. To estimate unmeasurable system states, a switched fuzzy observer and switched update laws for individual subsystems are designed. Compared with the existing funnel controllers designed in the framework of continuous-time feedback control, the proposed approach constructs event-triggered funnel controllers of subsystems and a switching event-triggered mechanism (ETM), which greatly alleviates the unnecessary waste of communication and computation resources, and, without any known limitation on maximum asynchronous time, handles the issue of asynchronous switching between the candidate controllers of subsystems and subsystems. Also, a strictly positive lower bound on interevent time is ensured by introducing a piecewise constant variable into the ETM, which overcomes the difficulty of switched measurement error being discontinuous. It is guaranteed that the tracking error evolves within a prespecified performance funnel. Finally, two examples including a mass-spring-damper system are presented for illustrating the validity of the developed approach.

In this article, a collection of conditions to achieve asymptotic stability of switched nonlinear systems with guaranteed safety via a state-dependent switching law are provided, where the asymptotic stability of individual subsystem is not assumed. Multiple barrier functions and multiple Lyapunov functions (MLFs) are merged as a tool for analyzing the asymptotic stability with guaranteed safety control problem of switched nonlinear systems. Meanwhile, the state-dependent switching law is designed to refrain zeno phenomenon. Moreover, bilinear sum of squares methodologies are employed to construct corresponding MLFs for switched nonlinear systems. Finally, three simulation examples are introduced to prove the effectiveness of the provided method.

This paper focuses on the problem of adaptive neural networks (NNs) tracking control for a class of completely nonaffine switched pure-feedback uncertain nonlinear systems with switched reference model. A sufficient and necessary condition for the control problem to be solvable is derived by exploiting the common Lyapunov function (CLF) method, backstepping, input-to-state stability analysis, and the small-gain technique. Also, a small-gain technique-based adaptive NN control scheme is provided to avoid the designed difficulty caused by the construction of an overall CLF for the switched closed-loop system, which is usually required when studying the switched pure-feedback system. Adaptive NN controllers of individual subsystems are constructed to guarantee that all of the signals in the closed-loop system are semi-globally uniformly ultimately bounded under arbitrary switchings, and the tracking error converges to a small neighborhood of the origin. Two examples, which include a continuously stirred tank reactor system, are presented to demonstrate the effectiveness of the proposed design approach.

In this paper, for a class of switched large-scale uncertain nonlinear systems with unknown control coefficients and unmeasurable states, a switched-dynamic-surface-based decentralized adaptive neural output-feedback control approach is developed. The approach proposed extends the classical dynamic surface control (DSC) technique for nonswitched version to switched version by designing switched first-order filters, which overcomes the problem of multiple "explosion of complexity." Also, a dual common coordinates transformation of all subsystems is exploited to avoid individual coordinate transformations for subsystems that are required when applying the backstepping recursive design scheme. Nussbaum-type functions are utilized to handle the unknown control coefficients, and a switched neural network observer is constructed to estimate the unmeasurable states. Combining with the average dwell time method and backstepping and the DSC technique, decentralized adaptive neural controllers of subsystems are explicitly designed. It is proved that the approach provided can guarantee the semiglobal uniformly ultimately boundedness for all the signals in the closed-loop system under a class of switching signals with average dwell time, and the tracking errors to a small neighborhood of the origin. A two inverted pendulums system is provided to demonstrate the effectiveness of the method proposed.

In this paper, the problem of adaptive neural output-feedback control is addressed for a class of multi-input multioutput (MIMO) switched uncertain nonlinear systems with unknown control gains. Neural networks (NNs) are used to approximate unknown nonlinear functions. In order to avoid the conservativeness caused by adoption of a common observer for all subsystems, an MIMO NN switched observer is designed to estimate unmeasurable states. A new switched observer-based adaptive neural control technique for the problem studied is then provided by exploiting the classical average dwell time (ADT) method and the backstepping method and the Nussbaum gain technique. It effectively handles the obstacle about the coexistence of multiple Nussbaum-type function terms, and improves the classical ADT method, since the exponential decline property of Lyapunov functions for individual subsystems is no longer satisfied. It is shown that the technique proposed is able to guarantee semiglobal uniformly ultimately boundedness of all the signals in the closed-loop system under a class of switching signals with ADT, and the tracking errors converge to a small neighborhood of the origin. The effectiveness of the approach proposed is illustrated by its application to a two inverted pendulum system.

In this paper, a decentralized adaptive fuzzy output-feedback control problem is discussed for a class of switched large-scale nonlinear systems without the measurements of the system states. Fuzzy logic systems are used to approximate the unknown nonlinear functions, and a switched observer is designed to avoid the conservativeness caused by adoption of a common observer for all subsystems. A common coordinate transformation of all subsystems is constructed to get rid of individual coordinate transformations for subsystems that are required when applying the backstepping recursive design scheme. Meanwhile, since the exponential decline property of Lyapunov functions for individual subsystems is no longer satisfied in this paper, the classical average dwell time method cannot be directly applied. Based on this, a switched observer-based decentralized adaptive fuzzy output-feedback control technique is set up by exploiting the average dwell time method and backstepping. It is proved that all the signals of the resulting closed-loop system are semiglobally uniformly ultimately bounded under a class of switching signals with average dwell time, while the tracking errors converge to a small neighborhood of the origin. The effectiveness of the proposed control scheme is illustrated by its applications to a two-inverted-pendulum system and a quadruple-tank process system.

This paper investigates the problem of adaptive neural tracking control via output-feedback for a class of switched uncertain nonlinear systems without the measurements of the system states. The unknown control signals are approximated directly by neural networks. A novel adaptive neural control technique for the problem studied is set up by exploiting the average dwell time method and backstepping. A switched filter and different update laws are designed to reduce the conservativeness caused by adoption of a common observer and a common update law for all subsystems. The proposed controllers of subsystems guarantee that all closed-loop signals remain bounded under a class of switching signals with average dwell time, while the output tracking error converges to a small neighborhood of the origin. As an application of the proposed design method, adaptive output feedback neural tracking controllers for a mass-spring-damper system are constructed.