This paper focuses on the leader-following consensus control problem of stochastic multi-agent systems with hysteresis inputs and nonlinear dynamics. A leader-following consensus scheme is presented for stochastic multi-agent systems directions under directed graphs, which can achieve predefined synchronisation error bounds. By mainly activating an auxiliary robust control component for pulling back the transient escaped from the neural active region, a multi-switching robust neuro adaptive controller in the neural approximation domain, which can achieve globally uniformly ultimately bounded tracking stability of multi-agent systems recently. A specific Nussbaum-type function is introduced to solve the problem of unknown control directions. Using a dynamic surface control technique, distributed consensus controllers are developed to guarantee that the outputs of all followers synchronise with that of the leader with prescribed performance. Based on Lyapunov stability theory, it is proved that all signals in closed-loop systems are uniformly ultimately bounded and all the follower agents can keep consensus with the leader. Two simulation examples are provided to illustrate the effectiveness and advantage of the proposed control scheme.

It is still an open and challenging issue to the typical position control problems of the three‐axis electrical‐optical gyro‐stabilized platform systems (TEOGSP), due to inherent characteristics, for example, measurement noise, input saturation, parametric uncertainties, largely unknown load disturbance. To solve this problem, a saturated adaptive robust feedback controller using an adaptive cascaded extended state observer (SAFCESO) is proposed for compromising between the measurement noise effect and the sensitivity to disturbances. Firstly, the matched and mismatched disturbances existing in the TEOGSP system are estimated and rejected by the cascaded adaptive extended state observers (CESO). Secondly, the parametric uncertainties are evaluated by the adaptive control, and the match disturbances are attenuated by the robust control. Moreover, the adaptive robust control law does not require the velocity measurement signal and internal dynamics information of the system, which is practical to implement. Hence, all various uncertainties could be mainly compensated. Then, the improved auxiliary systems governed by smooth switching functions are developed and incorporated into the control design to compensate for the effect of the input saturation. Finally, the command filters are introduced to limit the magnitude of the virtual control and to calculate the derivative of the virtual control, respectively. The extensive comparative experimental results in the TEOGSP systems showed that the proposed SAFCESO method had superiorities in terms of high‐precision tracking accuracy, robustness, and noise reduction.