Predictive current control (PCC) applied on permanent magnet synchronous motors (PMSMs) has been developed into mainly three methods: the conventional finite-control-set PCC, the double voltage vectors PCC, and deadbeat PCC. However, each approach has its particular calculation way for voltage vectors selection and respective execution duration. This paper, based on the deadbeat idea, presents a unified predictive current control scheme of PMSMs. Under this scheme, the prior three classes are able to be clearly unified into one frame with lower calculation effort. Furthermore, to cope with problem of parameter mismatch in dq-axis current predictive model, a integrated identification method is proposed. Firstly, data selectors are designed to reject abnormal data of sampling signals, and then the interval-varying multi-innovation least squares algorithm is combined with forgetting factor (V-FF-MILS) to approximate the error terms caused by electromagnetic parameters error. The estimated results are online fed to the model of PMSM to enhance its accuracy. Finally, the processor in loop (PIL) simulation results verify that the proposed integrated scheme has advantages in current control of PMSMs with large-scale parameter uncertainty.

In order to meet the maneuverability requirements of spacesuits for future manned planetary exploration, the concept of an active spacesuit based on the joint-assisted exoskeleton technology is presented. First, by studying the kinematic characteristics of the operator wearing the simulated spacesuit in different gravity environments, we developed a prototype of the upper limb exoskeleton. Then, the resistance moment of the simulated spacesuit was roughly obtained to match the operator’s motion range, being utilized to design the resistance moment model. Considering the unknown resistance moment effects in the active spacesuit and the uncertainties of the exoskeleton’s dynamics model, an adaptive neural network control with fuzzy compensation was developed to drive the upper limb exoskeleton’s tracking desired trajectories. Experimental studies were carried out using an upper limb exoskeleton to illustrate that the proposed method has excellent trajectory tracking performance and good adaptive ability to the gravity environment changes.

To eliminate the influence of spacesuits joint resistant torque on the operation of astronauts, an active spacesuit scheme based on the joint-assisted exoskeleton technology is proposed. Firstly, we develop a prototype of the upper limb exoskeleton robot and theoretically analyse the prototype to match astronauts motion behavior. Then, the Jiles-Atherton model is adopted to describe the hysteretic characteristic of joint resistant torque. Considering the parameter identification effects in the Jiles-Atherton model and the local optimum problem of the basic PSO (particle swarm optimization) algorithm, a SA- (simulated annealing-) PSO algorithm is proposed to identify the Jiles-Atherton model parameters. Compared with the modified PSO algorithm, the convergence rate of the designed SA-PSO algorithm is advanced by 6.25 and 20.29, and the fitting accuracy is improved by 14.45 and 46.5 for upper limb joint model. Simulation results show that the identified J-A model can show good agreements with the measured experimental data and well predict the unknown joint resistance torque.