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• IT2 fuzzy is first developed to hybrid grid-connected generation and it can represent more universal nonlinear systems for region fuzzy membership functions in comparison to subsistent literatures on type-2 fuzzy or T-S fuzzy. • The paper designs an IT2 fuzzy controller which can actually add substantially to the stability of the system, enhancing robustness to disturbance and dynamic performance in dealing with time delays. • We first apply switched model to simulate hybrid grid-connected generation system and it can approach the physical truth well, which makes the theoretical research with more practical significance. • The superiority of switched IT2 fuzzy control in ease of successfully coordinating the individual components of the hybrid grid-connected generation system and operating in a dynamically stable way subordinating to the given patterns. The paper concentrates on H ∞ control problem of switched IT2 fuzzy nonlinear systems with multiple time delays and disturbance. The problem exists extensively in hybrid grid-connected generation converter system, which can be felicitously described through switched interval type-2 (IT2) fuzzy model for bifurcation behavior, electromagnetic interference. The application of IT2 fuzzy switched descriptor in hybrid grid-connected generation is proposed for the first time avoiding bifurcation behavior effectively and successfully coordinates the individual components of the hybrid grid-connected generation system leading to operating in a dynamically stable way subordinating to the given patterns. To ensure the system stable subjecting to disturbance, methods of LMI and free-weighting matrices are employed to design the controller. Availability and validity are verified through a numerical simulation. [ABSTRACT FROM AUTHOR]

• The DSC technique is employed to deal with the "complexity explosion" problem for state-constrained non-affine nonlinear systems subject to output dead zone and input saturation, and the robust compensator is designed to reduce the influences of disturbances and uncertainties. • The unknown control coefficient derived from output nonlinearity is handled through adopting the property of Nussbaum function. • A series of barrier Lyapunov functions are designed, which effectively guarantees that prescriptive ranges of system states cannot be violated. This paper disposes of the problem of adaptive dynamic surface control for non-affine nonlinear systems. The full state constraints, output dead zone and input saturation are fully considered in the controlled system. Invariant sets are used for a continuous and semi-bounded condition of non-affine functions. The "complexity explosion" issue caused by backstepping procedure is avoided via the dynamic surface control method. A Nussbaum function is used to handle the unknown control coefficient derived from the output dead zone, and barrier Lyapunov functions are employed to handle full state constraints. To counteract the effects of disturbance and uncertainty, the robust compensator is constructed. In addition, it is proved that all signals in the system are bounded and all states satisfy their constraints. Finally, the simulation results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A predefined-time adaptive consensus control strategy is developed for a class of multi-agent systems containing unknown nonlinearity. The unknown dynamics and switching topologies are simultaneously considered to adapt to actual scenarios. The time required for tracking error convergence can be easily adjusted using the proposed time-varying decay functions. An efficient method is proposed to determine the expected convergence time. Subsequently, the predefined time is adjustable by regulating the parameters of the time-varying functions (TVFs). The neural network (NN) approximation technique is used to address the issue of unknown nonlinear dynamics through predefined-time consensus control. The Lyapunov stability theory testifies that the predefined-time tracking error signals are bounded and convergent. The feasibility and effectiveness of the proposed predefined-time consensus control scheme are demonstrated through the simulation results.

A pinning-based self-regulation intermediate event-triggered (ET) funnel tracking control strategy is proposed for uncertain nonlinear multiagent systems (MASs). Based on the backstepping framework, a pinning control strategy is designed to achieve the tracking control objective, which only uses the communication weight between the agents without additional feedback parameters. Moreover, by designing a self-regulation triggered condition based on the tracking error, the intermediate triggered signal is calculated to replace the continuous signal in the controller, so as to achieve the goal of discontinuous update of the controller signal, and this mechanism does not need to add additional compensation function to the controller signal. At the same time, the funnel method is adopted to restrict the error of step n and avoid the possible negative impact caused by control signal. Furthermore, the nonlinear noncontinuous faults are compensated by the disturbance observer. Then, the Lyapunov stability theorem is used to prove that all signals of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, some simulation results confirm the effectiveness of the proposed control scheme.

This article investigates the adaptive performance guaranteed tracking control problem for multiagent systems (MASs) with power integrators and measurement sensitivity. Different from the structural characteristics of existing results, the dynamic of each agent is a power exponential function. A method called adding a power integrator technique is introduced to guarantee that the consensus is achieved of the MASs with power integrators. Different from existing prescribed performance tracking control results for MASs, a new performance guaranteed control approach is proposed in this article, which can guarantee that the relative position error between neighboring agents can converge into the prescribed boundary within preassigned finite time. By utilizing the Nussbaum gain technique and neural networks, a novel control scheme is proposed to solve the unknown measurement sensitivity on the sensor, which successfully relaxes the restrictive condition that the unknown measurement sensitivity must be within a specific range. Based on the Lyapunov functional method, it is proven that the relative position error between neighboring agents can converge into the prescribed boundary within preassigned finite time. Finally, a simulation example is proposed to verify the availability of the control strategy.

This article is concerned with the dynamic event-triggered-based adaptive output-feedback tracking control problem of nonlinear multiagent systems with time-varying input delay. By utilizing the approximation capability of neural network (NN), a low-gain nonlinear observer is first established to estimate the immeasurable states. To mitigate the effect of time-varying input delay, an auxiliary system with communication information is designed to generate the compensation signals. Then, a distributed adaptive composite NN dynamic surface control (DSC) strategy is proposed to acquire the satisfactory tracking accuracy, where the filter errors are compensated by the introduced serial-parallel estimation model. Moreover, an effective switching dynamic event-triggered mechanism is developed to determine the communication instants and reduce the update frequency of the controller. It is proven that the consensus tracking error converges to a residual set of the origin. Finally, simulation results are presented to demonstrate the effectiveness of the proposed composite NN DSC scheme.