Precise kinematic modeling and control of soft continuum robots are challenged by their inherent deformability and complex interactions with the environment. This paper proposes an adaptive kinematic modeling framework based on a Deep Belief Network with Event-Driven Incremental Learning, designed for artificial intelligence-enabled control of soft robotic systems. The deep belief network is first pre-trained using simulated data to establish an initial kinematic model, requiring only approximately 600 real-world samples for deployment. An event-driven incremental learning mechanism is then introduced to adapt the model online. This mechanism is guided by a spike intensity metric, which evaluates prediction errors and selectively triggers either fine-tuning of network parameters or the integration of new radial basis function nodes to compensate for unmodeled kinematic effects and environmental disturbances. The adaptive kinematic model is embedded within a closed-loop controller to achieve accurate trajectory tracking. Experimental validation is conducted on a tendon-driven soft origami manipulator, covering trajectory tracking under varying payloads and deformation constraints, disturbance rejection, and teleoperation tasks. The proposed framework achieves sub-millimeter average trajectory tracking accuracy under confined conditions and outperforms baseline deep belief network models and piecewise constant curvature approaches. The results demonstrate that the proposed artificial intelligence-based adaptive kinematic modeling method provides a data-efficient and robust solution for precise control of soft continuum robots in applications such as medical robotics and flexible manufacturing.

In order to meet the mobility and physical activity needs of people with impaired limbs function, a novel limbs-free variable structure wheelchair system controled by face-computer interface (FCI) was developed in this study. FCI used facial electromyography (fEMG) as a human intention recognition method from 6 facial movements, and the accuracy of intent recognition reached 97.6% under a series of offline optimization including channel optimization based on the Hilbert transform to obtain the envelope of fEMG, features optimization, and channel-independent model optimization. A collection of finite state machines (FSM) was used to control the movement and structural changes of the wheelchair. A shared control strategy called “ Keep Action after Take Over (KAaTO) “ that can reduce user fatigue while increasing safety was used in long-distance movement control of wheelchair. To test the performance of the system, in the braking distance test experiment, the result of 0.429m under KAaTO was better than the EMG-based discrete command control and speech command control method. Finally, an outdoor long-distance control pilot experiment proved the superior performance of the developed system.

Traditional rehabilitation techniques have limited effects on the recovery of patients with tetraplegia. A brain–computer interface (BCI) provides an interactive channel that does not depend on the normal output of peripheral nerves and muscles. In this paper, an integrated framework of a noninvasive electroencephalogram (EEG)-based BCI with a noninvasive functional electrical stimulation (FES) is established, which can potentially enable the upper limbs to achieve more effective motor rehabilitation. The EEG signals based on steady-state visual evoked potential are used in the BCI. Their frequency domain characteristics identified by the pattern recognition method are utilized to recognize intentions of five subjects with average accuracy of 73.9%. Furthermore the movement intentions are transformed into instructions to trigger FES, which is controlled with iterative learning control method, to stimulate the relevant muscles of upper limbs tracking desired velocity and position. It is a useful technology with potential to restore, reinforce or replace lost motor function of patients with neurological injuries. Experiments with five healthy subjects demonstrate the feasibility of BCI integrated with upper extremity FES toward improved function restoration for an individual with upper limb disabilities, especially for patients with tetraplegia.