In this paper, a fuzzy disturbance observer and a high-gain disturbance observer based on a variable structure controller are applied to deal with imprecise multi-shaft with web materials linkage systems taking into account the variation of the moment of inertia. Specifically, a high-gain disturbance observer and an adaptive fuzzy algorithm are separately applied to estimate system uncertainties and external disturbances. The high-gain disturbance observer is designed with auxiliary variables to avoid the amplification of the measurement disturbance, and the fuzzy disturbance observer has the advantage that it does not depend on model information. The convergence properties of the tracking error are analytically proven using Lyapunov’s theory. The obtained numerical results demonstrate the validity and the adaptive performance of the proposed control law in case the system is exposed to uncertainties and disturbances. Important remarks on the design process and performance benchmarks of the two observers are also demonstrated.

Active magnetic bearings (AMBs) are electromagnetic mechanism systems in which non-contact bearings support a rotating shaft using attractive forces generated by electromagnets through closed-loop control. For complete support of a five degree of freedom (DOF) rotor system, most AMB structures include two radial actuators and one for the axial direction. Conical active magnetic bearings (CAMBs) is one of the development directions of conventional magnetic bearings in which the requirement of the axial bearing can be eliminated. In this paper, we propose a structure with a CAMB integrated into a canned motor pump to eliminate the need for mechanical bearings and shaft seals. However, this system necessitates a more complicated control strategy due to a significant coupling effect between rotor motion and hydrodynamic disturbances. This paper presents a fractional order active disturbance rejection control (FOADRC) including a fractional order extend state observer (FOESO) and a proportional derivative controller (PD) to track and reject lumped disturbances actively. The proposed controller achieves better performance than the integer-type ADRC and traditional PID controller. The control performance of the proposed FOADRC is illustrated in terms of very good disturbance rejection capability that is demonstrated through MATLAB/Simulink simulation results.

Human–robot co-carrying tasks reveal their potential in both industrial and everyday applications by leveraging the strengths of both parties. Effective control of robots in these tasks requires managing the energy level in the closed-loop systems to prevent potential dangers while also minimizing motion errors to complete the shared tasks. The collaborative tasks pose numerous challenges due to varied human intentions in adapting to workspace characteristics, leading to human–robot conflicts. In this paper, we develop a cooperation control framework for human–robot co-carrying tasks constructed by utilizing reference generator and low-level controller to aim to achieve safe interaction and synchronized human–robot movement. Firstly, the human motion predictions are corrected in the event of prediction errors based on the conflicts measured by the interaction forces through admittance control, thereby mitigating conflict levels. Low-level controller using an energy-compensation passive velocity field control approach allows encoding the corrected motion to produce control torques for the robot. In this manner, the closed-loop robotic system is passive when the energy level exceeds the predetermined threshold, and otherwise. Furthermore, the proposed control approach ensures that the system’s kinetic energy is compensated within a finite time interval. The passivity, stability, convergence rate of energy, and power flow regulation are analyzed from theoretical viewpoints. Human-in-the-loop experiments involving 18 participants have demonstrated that the proposed method significantly enhances task performance and reduces human workload, as evidenced by both objective metrics and subjective evaluations, with improvements confirmed by statistical tests (p < 0.05) relative to baseline methods.