Soft actuators are of great interests in recent years due to inherent compliance and adaptability. However, most of current studies focus on the physical actuation phenomenon and their fabrication, few results can be found on the modeling and closed-loop control due to their complicated elastic deformation. This paper studies physical modeling, parameter identification, and model-based nonlinear robust control for fluidic soft bending actuators governed by high-speed on–off solenoid valves. With the dynamic complexity and characteristics of fluidic soft actuators, the system has been described by two parts: motion dynamics and air dynamics. A second-order transfer function is used for the motion dynamics which describes the behavior from driving pneumatic pressure to soft actuator bending angle, and the model parameters are identified by with different operating frequencies. The nonlinear physical modeling of air dynamics which describes the behavior from control voltage to driving pneumatic pressure is also built and its parameters are identified by using the least square method. Based on the identified high-order model, a nonlinear robust control algorithm is developed for soft actuator by backstepping design to deal with nonlinearities and uncertainties. Experimental results show that the closed-loop stability is guaranteed and the good tracking performance is achieved by the proposed method.

Mobile hydraulic systems are developed toward better control performance and higher energy efficiency. Pressure compensators, which govern the pressure drops over the control orifices, are widely used in multiactuator systems to improve their operability. However, additional energy is also dissipated in the compensators especially under the overrunning load conditions. This paper presents a novel energy-saving system, where the compensator is designed as a regeneration device consisting of a hydraulic motor and an electric generator. Then, hydraulic energy can be regenerated and pressure compensation function is realized by adapting the electromagnetic torque of the generator to the load as well. The close-loop control of the generator is designed by effective pressure drop estimation. The proposed energy-saving system and controller are implemented on an experimental platform of hybrid excavators which is equipped with an electric energy storage device. The experimental results show both good control performance and significant energy-saving effect.

A marine autonomous surface vehicle (ASV) is a kind of autonomous marine robot with intelligent and flexible use advantages. They are mainly divided into two categories: unmanned vessels and unmanned sailboats. Marine ASVs are essential in marine science, industry, environmental protection, and national defense. One of the primary challenges faced by marine ASVs is autonomously planning paths in an intricate marine environment. Numerous research findings have surfaced in recent years, including the combination with popular machine learning. However, a systematic literature review is still lacking, primarily a comprehensive comparison of two types of ASV path planning methods. This review first introduces the problem and evaluation indicators of path planning for ASVs. Then, aiming at unmanned vessels and sailboats, respectively, it sorts out various path planning algorithms proposed in the existing literature, including the advantages and limitations of both kinds of ASVs, and discusses them in combination with evaluation indicators. Also, this paper explores how marine environmental factors affect path planning and its corresponding treatment methods. Finally, this review summarizes the challenges of unmanned ship path planning, proposes potential technical solutions and future development directions, and aims to provide references for further development in this field.