Extended state observer-based control with an adjustable parameter for a large ground-based telescope

The high-precision requirements will always be constrained due to the complicated operating conditions of the ground-based telescope. Owing to various internal and external disturbances, it is necessary to study a control method, which should have a good ability on disturbance rejection and a good adaptability on system parameter variation. The traditional proportional-integral (PI) controller has the advantage of simple and easy adjustment, but it cannot deal with the disturbances well in different situations. This paper proposes a simplified active disturbance rejection control law, whose debugging is as simple as the PI controller, and with better disturbance rejection ability and parameter adaptability. It adopts a simplified second-order extended state observer (ESO) with an adjustable parameter to accommodate the significant variation of the inertia during the different design stages of the telescope. The gain parameter of the ESO can be adjusted online with a recursive least square estimating method once the system parameter has changed significantly. Thus, the ESO can estimate the total disturbances timely and the controller will compensate them accordingly. With the adjustable parameter of the ESO, the controller can always achieve better performance in different applications of the telescope. The simulation and experimental verification of the control law was conducted on a 1.2-meter ground based telescope. The results verify the necessity of adjusting the parameter of the ESO, and demonstrate better disturbance rejection ability in a large range of speed variations during the design stages of the telescope.

Extended state observer based fractional order controller design for integer high order systems

In this paper, based on the extended state observer (ESO) and on a fractional order controller (FOC), composed of an integer order PID cascaded with a fractional order filter (FOF), a new control scheme for an n th order integer plant is proposed. The ESO is used to estimate and cancel the unknown internal dynamics and the external disturbance. Afterwards, an FOC is designed to resolve the set-point tracking problem. An analytical and systematic method is proposed to design the FOC. This method is based on the Internal Model Control (IMC) and the Bode’s Ideal Transfer Function (BITF). Therefore, the proposed control structure improves the robustness and performance of the traditional linear active disturbance rejection control (LADRC), especially for the open-loop gain variation. In addition, since the system be controlled is an n th order, a general form of the BITF is also proposed. Numerical simulations on a nonlinear model and experimental results on a cart-pendulum system design illustrate the effectiveness of the suggested ESO-PID-FOF scheme for the disturbance rejection, the set-point tracking and robustness. A comparison with the results obtained using the standard LADRC is also presented.

Improved Nonlinear Extended State Observer-Based Sliding-Mode Rotary Control for the Rotation System of a Hydraulic Roofbolter

This paper develops a sliding-mode control with an improved nonlinear extended state observer (SMC-INESO) for the rotation system of a hydraulic roofbolter with dead-zones, uncertain gain, and disturbances, with the purpose of improving tracking performance. Firstly, the rotation system is modeled to compensate for dead-zone nonlinearity. Then, we present an improved nonlinear extended state observer to estimate disturbances of the rotation system in real time. Moreover, a proportional-integral-differential sliding-mode surface is introduced and an improved sliding-mode reaching law is designed. Based on this, a sliding-mode control law is developed. In order to eliminate the influence of estimation error and uncertain gain, we design two adaptation laws based on the sliding-mode surface and the estimated states. Finally, the effectiveness of the proposed SMC-INESO is verified through comparative simulation studies.