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In this paper, we present a sensorless admittance control scheme for robotic manipulators to interact with unknown environments in the presence of actuator saturation. The external environment are defined as linear models with unknown dynamics. Using admittance control, the robotic manipulator is controlled to be compliant to external torque from the environment. The external torque acted on the end-effector is estimated by using a disturbance observer based on generalized momentum. The model uncertainties are solved by using radial basis neural networks. To guarantee the tracking performance and tackle the effect of actuator saturation, an adaptive neural network (NN) controller integrating an auxiliary system is designed to handle the actuator saturation is proposed. By employing Lyapunov stability theory, the stability of the closed-loop system is achieved. The experiments on Baxter robot are implemented to verify the the effectiveness of the proposed method.

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In this paper, a force sensorless control scheme based on neural networks (NNs) is developed for interaction between robot manipulators and human arms in physical collision. In this scheme, the trajectory is generated by using geometry vector method with Kinect sensor. To comply with the external torque from the environment, this paper presents a sensorless admittance control approach in joint space based on an observer approach, which is used to estimate external torques applied by the operator. To deal with the tracking problem of the uncertain manipulator, an adaptive controller combined with the radial basis function NN (RBFNN) is designed. The RBFNN is used to compensate for uncertainties in the system. In order to achieve the prescribed tracking precision, an error transformation algorithm is integrated into the controller. The Lyapunov functions are used to analyze the stability of the control system. The experiments on the Baxter robot are carried out to demonstrate the effectiveness and correctness of the proposed control scheme.

For bilateral teleoperation, the haptic feedback demands the availability of accurate force information transmitted from the remote site. Nevertheless, due to the limitation of the size, the force sensor is usually attached outside of the patient's abdominal cavity for the surgical operation. Hence, it measures not only the interaction forces on the surgical tip but also the surgical tool dynamics. In this paper, a model-free based deep convolutional neural network (DCNN) structure is proposed for the tool dynamics identification, which features fast computation and noise robustness. After the tool dynamics identification using DCNN, the calibration is performed, and the bilateral teleoperation is demonstrated to verify the proposed method. The comparison results prove that the proposed DCNN model promises prominent performance than other methods. Low computational time (0.0031 seconds) is ensured by the rectified linear unit (ReLU) function, and the DCNN approach provides superior accuracy for predicting the noised dynamics force and enable its feasibility for bilateral teleoperation.

As a significant role in healthcare and sports applications, human activity recognition (HAR) techniques are capable of monitoring humans’ daily behavior. It has spurred the demand for intelligent sensors and has been giving rise to the explosive growth of wearable and mobile devices. They provide the most availability of human activity data (big data). Powerful algorithms are required to analyze these heterogeneous and high-dimension streaming data efficiently. This paper proposes a novel fast and robust deep convolutional neural network structure (FR-DCNN) for human activity recognition (HAR) using a smartphone. It enhances the effectiveness and extends the information of the collected raw data from the inertial measurement unit (IMU) sensors by integrating a series of signal processing algorithms and a signal selection module. It enables a fast computational method for building the DCNN classifier by adding a data compression module. Experimental results on the sampled 12 complex activities dataset show that the proposed FR-DCNN model is the best method for fast computation and high accuracy recognition. The FR-DCNN model only needs 0.0029 s to predict activity in an online way with 95.27% accuracy. Meanwhile, it only takes 88 s (average) to establish the DCNN classifier on the compressed dataset with less precision loss 94.18%.