Singularity-Free Adaptive Controller for Uncertain Hysteresis Suspension Using Magnetorheological Elastomer-Based Absorber

The magnetorheological elastomer (MRE) is a smart material widely used in recent vibration systems. A system using these materials often faces difficulties designing the controller such as unknown parameters, hysteresis state, and input constraints. First, a model is designed for the MRE-based absorber to portray the behavior of MRE and predict the appropriate electric current supplied. The conventional adaptive controller often suffers from so-called control singularities. The singularity-free adaptive controller is proposed to eliminate the singularity with parametric uncertainty. The proposed controller consists of four components: an adaptive linearizing controller, a deputy adaptive neural network controller, an auxiliary part designed for the controller to overcome the input constraint problem, and a smooth switching algorithm used to exchange the takeover rights of the two controllers. Moreover, the controller is designed to obtain the stabilization of hysteretic state estimation for the vibration system. The adaptive algorithms are proposed to update the unknown system parameters and to observe the unmeasurable hysteretic state. Meanwhile, closed-loop system stability is comprehensively assessed. Finally, the simulation performed on a quarter-car suspension with an MRE-based absorber shows the proposed controller's efficiency.

Robust fault-tolerant attitude control of spacecraft using hybrid actuators

Purpose This paper aims to address the spacecraft attitude control problem using hybrid actuators in the presence of actuator saturation, uncertainties and faults, inertia uncertainties and external disturbances. Design/methodology/approach A hybrid actuator configuration is used where thrusters are engaged for rapid attitude maneuvers, while reaction wheels (RWs) are used for fine pointing. Findings The key advantages are two-fold: a finite-time high-level controller is designed to produce the three-axis virtual control torques; an online robust control allocation (RobCA) scheme is proposed to redistribute virtual control signals to the actuators with taking into account the actuator saturation, uncertainties and faults; and the RobCA scheme allows a smooth switch between thrusters and RWs, which handles the inaccuracy problem of thrusters and saturation problem of RWs. Practical implications An online RobCA algorithm is designed that maps the total control demands onto individual actuator settings and allows a smooth switch between thrusters and RWs. Simulation results show the effectiveness of the proposed control strategy. Originality/value This work may be used on modern space missions, which impose higher requirements on smooth switching of spacecraft thrusters and RWs.

Superheated steam temperature system of thermal power control engineering based on neural network local multi-model prediction

The superheated steam temperature object of thermal power plant has the characteristics of time lag, inertia and time-varying parameters. The control quality of the conventional proportional integral derivate controller with fixed parameters will decrease after the object characteristics change. The generalized predictive control strategy of superheated steam temperature based on neural network local multi-model switching can achieve the goal of designing sub-controllers for fixed models under several typical operating conditions. When the system operating conditions change, the effective switching strategy is timely and accurate. Switch to the most suitable controller. The paper proposes a new smooth switching method, which can effectively suppress the large disturbance phenomenon of the object when switching. The simulation results verify the effectiveness of the control strategy.