It is of great importance to improve the performance of soft bending actuators with simpler control laws. Considering that the velocity information of soft actuators cannot be measured by conventional sensors due to a contradiction between the soft body and the rigid state, this letter addresses a robust tracking controller without velocity observers to improve the tracking accuracy and simplify the control laws. First, a generalized dynamic model and related system properties are developed for the construction of controllers. Second, a novel robust tracking controller combined with an auxiliary state is utilized for soft bending actuators in the presence of system uncertainties. A key feature of the proposed controller is that it does not incorporate any information about velocities and observers. Finally, corresponding comparative simulations and experiments are performed to verify the effectiveness of the proposed controller.

In order to enhance the dynamic performance and anti-disturbance ability of speed control for a permanent magnet synchronous motor (PMSM), a sliding mode control method based on a PID sliding surface and an improved terminal sliding mode reaching law (ITSMRL) is proposed. Firstly, an ITSMRL is proposed to increase the reaching speed and reduce chattering; moreover, it has been verified that the reaching law (RL) can achieve a sliding mode surface in finite time. Then, based on the dynamic model of PMSMs with uncertainties, an extended state observer (ESO) is used to estimate the lumped disturbance, and it is proven that the estimated error is bounded. Finally, on the basis of the observed feedforward disturbance, to enhance the disturbance rejection ability of PMSMs, a controller that combines the PID sliding mode surface and the ITSMRL is proposed. Moreover, the stability of the closed-loop system is proven. The composite method has the characteristics of a fast reaching speed, small chattering and strong robustness, and is verified by experiments.

Inspired by nature, soft-bodied pneumatic network actuators (PNAs) composed of compliant materials have been successfully applied in the fields of industry and daily life because of large-amplitude motion and long life span. However, compliant materials simultaneously limit the output force, challenge the dynamic modeling and impede corresponding control. In this paper, we investigate the design, modeling and control of an enhanced PNA. First, an enhanced structure is proposed to improve the output force of PNAs with features of simplification of fabrication, lightweight and compliant material retentivity. Second, a dynamic model of the enhanced PNA is constructed based on the Euler–Lagrange (EL) method. Finally, an adaptive robust controller is addressed for PNAs in presence of system uncertainties without knowledge of its bounds in prior. Experiment results show that the output force of the enhanced PNA is four times greater than the actuator without enhanced structures, which affords to theoretical estimation. Moreover, the proposed controller is utilized and compared with previous works in humanoid finger experiments to illustrate the effectiveness.

Biomass units can play a certain role in peak summer and winter due to their advantages in terms of their environmental and short-term peak ability. To analyze the peak ability of biomass units, this paper focuses on the dynamic modeling of biomass unit peak ability. Firstly, the process of biomass feeding amount to power output is divided into a feed–heat module, heat–main steam pressure module and main steam pressure–power module. A two-input and two-output dynamic model is established where the feeding amount and turbine valve opening serve as inputs, and the main steam pressure and power serve as outputs. Then the effectiveness of the established model is validated by actual operation data of a 30 MW biomass unit. This dynamic model can provide a mechanistic model for analyzing the impact of fuel calorific value on the power output, and provide support for fuel management and scheduling strategies during the peak period of biomass units.