A switched extremum seeking control without steady-state oscillation using gradient-based adaptive-amplitude perturbation.

To enhance the steady-state and dynamic performance of the extremum seeking control (ESC), a switched ESC scheme is proposed in this paper. Firstly, to solve nonconvex optimization problems, a novel adaptive law for perturbation is established utilizing the hybrid gradient information to accomplish a trade-off between the dynamic and steady-state performance. It is shown that the adaptive-amplitude ESC achieves global optimization despite the existence of local extrema.

Adaptive 2-bits-triggered neural control for uncertain nonlinear multi-agent systems with full state constraints.

This paper investigates an adaptive 2-bits-triggered neural control for a class of uncertain nonlinear multi-agent systems (MASs) with full state constraints. Considering the limitations of practical physical devices and operating conditions, MASs may suffer performance degradation or even crash while the system states are not restricted. With this in mind, combined with barrier Lyapunov function (BLF), an adaptive neural consensus control is developed to guarantee that the state constraints of all followers are not violated.

Event-triggered prescribed settling time consensus control of uncertain nonlinear multiagent systems with given transient performance.

Multiagent systems (MASs) are usually used in unmanned aerial vehicle formations, multi-manipulator coordinated, traffic vehicle control and other fields, which have attracted a lot of attention from scholars. In this research, with the help of the designed performance function, the nonlinear transformation of synchronization error is realized. And the synchronization error of MASs with given transient performance could converge to the predefined interval.

Neural Adaptive Self-Triggered Control for Uncertain Nonlinear Systems With Input Hysteresis.

The issue of neural adaptive self-triggered tracking control for uncertain nonlinear systems with input hysteresis is considered. Combining radial basis function neural networks (RBFNNs) and adaptive backstepping technique, an adaptive self-triggered tracking control approach is developed, where the next trigger instant is determined by the current information. Compared with the event-triggered control mechanism, its biggest advantage is that it does not need to continuously monitor the trigger condition of the system, which is convenient for physical realization.

Spacecraft Angular Rates Estimation with Gyrowheel Based on Extended High Gain Observer.

A gyrowheel (GW) is a kind of electronic electric-mechanical servo system, which can be applied to a spacecraft attitude control system (ACS) as both an actuator and a sensor simultaneously. In order to solve the problem of two-dimensional spacecraft angular rate sensing as a GW outputting three-dimensional control torque, this paper proposed a method of an extended high gain observer (EHGO) with the derived GW mathematical model to implement the spacecraft angular rate estimation when the GW rotor is working at large angles. For this purpose, the GW dynamic equation is firstly derived with the second kind Lagrange method, and the relationship between the measurable and unmeasurable variables is built.

Input-output finite-time stabilization of linear systems with finite-time boundedness.

The paper presents linear system Input-Output Finite-Time Stabilization (IO-FTS) method under Finite-Time Boundedness (FTB) constraint. A state feedback controller is designed, via Linear Matrix Inequalities (LMIs), to guarantee the system both IO-FTS and FTB. The proposed methods are applied to the guidance design of a class of terminal guidance systems to suppress disturbances with IO-FTS method and FTB constraints simultaneously satisfied.