Showing papers by "Zeng-Guang Hou published in 2016"

Containment Control of Multiagent Systems With Dynamic Leaders Based on a $PI^{n}$ -Type Approach

Abstract: This paper studies the containment control of multiagent systems (MASs) with multiple dynamic leaders in both continuous-time domain and discrete-time domain. The leaders’ motions are described by the ${n}$ th-order polynomial trajectories. This setting makes practical sense because given some critical points, the leaders’ trajectories are usually planned by the polynomial interpolations. In order to drive all followers into the convex hull spanned by the leaders, a PI $^{{n}}$ -type containment algorithm is proposed ( ${P}$ and ${I}$ are short for proportional and integral, respectively; ${I} ^{{n}}$ implies that the algorithm includes up to the ${n}$ th-order integral terms). It is theoretically proved that the PI $^{{n}}$ -type containment algorithm is able to solve the containment problem of MASs where the followers are described by any order integral dynamics. Compared to the previous results on the MASs with dynamic leaders, the distinguished features of this paper are that: 1) the containment problem is studied not only in the continuous-time domain but also in the discrete-time domain while most existing results only work in the continuous-time domain; 2) to deal with the leaders with the ${n}$ th-order polynomial trajectories, existing results require the follower’s dynamics to be the ( ${n}{+}$ 1)th-order integral while the followers considered in this paper can be described by any-order integral dynamics; 3) the “sign” function is not employed in the proposed algorithm, which avoids the chattering phenomenon; and 4) both disturbance and measurement noise are taken into account. Finally, some simulation examples are given to demonstrate the effectiveness of the proposed algorithm.

Reaching a consensus in networks of high-order integral agents under switching directed topologies

Abstract: Consensus problem of high-order integral multi-agent systems under switching directed topology is considered in this study. Depending on whether the agent’s full state is available or not, two distributed protocols are proposed to ensure that states of all agents can be convergent to a same stationary value. In the proposed protocols, the gain vector associated with the agent’s estimated state and the gain vector associated with the relative estimated states between agents are designed in a sophisticated way. By this particular design, the high-order integral multi-agent system can be transformed into a first-order integral multi-agent system. Also, the convergence of the transformed first-order integral agent’s state indicates the convergence of the original high-order integral agent’s state, if and only if all roots of the polynomial, whose coefficients are the entries of the gain vector associated with the relative estimated states between agents, are in the open left-half complex plane. Therefore, many analysis techniques in the first-order integral multi-agent system can be directly borrowed to solve the problems in the high-order integral multi-agent system. Due to this property, it is proved that to reach a consensus, the switching directed topology of multi-agent system is only required to be ‘uniformly jointly quasi-strongly connected’, which seems the mildest connectivity condition in the literature. In addition, the consensus problem of discrete-time high-order integral multi-agent systems is studied. The corresponding consensus protocol and performance analysis are presented. Finally, three simulation examples are provided to show the effectiveness of the proposed approach.

iLeg —A Lower Limb Rehabilitation Robot: A Proof of Concept

Abstract: In this paper, a robot, namely iLeg , is designed for the purpose of rehabilitation of patients with hemiplegia or paraplegia. The iLeg is composed of one reclining seat and two leg orthoses, and each leg orthosis has three degrees of freedom, which correspond to the hip, knee, and ankle. Based on this robotic system, two controllers, i.e., passive training controller and active training controller, are proposed. The former takes advantage of the proportional-integral control method to solve the trajectory tracking problem, and the latter employs the surface electromyography signals to achieve active training. Two simplified impedance controllers, i.e., damping-type velocity controller and spring-type position controller, are designed for active training. A perceptron neural network detects movement intentions. The performance of the controllers was investigated with one able-bodied male. The results showed that the leg orthosis tracked the predefined trajectory based on the passive training controller, with the error rates of $0.45\%$ , $0.44\%$ , and $0.27\%$ , respectively, for the hip, knee, and ankle. The active training controller whose loop rate is 6.67 Hz can move the leg orthosis smoothly, and the average recognition error of the perceptron neural network is less than $5\%$ .

Toward Patients’ Motion Intention Recognition: Dynamics Modeling and Identification of iLeg—An LLRR Under Motion Constraints

Abstract: In order to implement model-based recognition of human motion intention, dynamics modeling and identification of a lower limb rehabilitation robot named iLeg is investigated. Due to the relatively strong motion constraints, the traditional identification methods become insufficient for iLeg in three aspects: 1) the coupling factors among joints have not been considered in the traditional joint friction models, which makes the structural error and the torque estimation errors relatively large; 2) because of the small and complicated feasible region caused by the motion constraints, the traditional initialization strategy, for searching the valid initial solutions of the optimization problem for the exciting trajectories, becomes very inefficient; and 3) the condition number of the observation matrix, calculated from the preliminary dynamic model and the associated optimized exciting trajectory, is too large for the identification, and, however, further reduction of the condition number has not been considered in the literature. Therefore, corresponding contributions are presented to overcome the limitation. First, the coupling factors among joints are considered in the joint friction model by using the Palmgren empirical formulation and a polynomial fitting method. Then, an indirectly generating strategy is designed, by which the valid initial solutions of the optimization problem can be found with good efficiency. Moreover, a recursive optimization method based on the optimization of the dynamic model and the exciting trajectories, is proposed to further reduce the condition number. Finally, the performance of the proposed methods is demonstrated by several experiments.

Distributed Tracking Control of Uncertain Multiple Manipulators Under Switching Topologies Using Neural Networks

Abstract: The distributed tracking control of a group of manipulators under switching directed topologies is studied. Each manipulator is modeled by the Euler-Lagrange dynamics which includes uncertainties and external disturbances. The proposed controller has the neural network approximation unit for compensating uncertainties and the robust term for counteracting external disturbances. It can be proved that when the communication topology switches among a set of graphes which have a spanning tree and have no loop structure, the final tracking error can be reduced as small as possible.

sEMG-based prediction of human lower extremity movements by using a dynamic recurrent neural network

Abstract: In this paper, a novel robust nonlinear model is proposed to predict human lower extremity motion based on the multi-channel surface electromyography (sEMG) signals. The prediction model is established by a data-driven dynamic recurrent neural network. The sEMG signals acquired from human lower extremity muscles are used as the inputs of the prediction model. The outputs of the model are joint angles of hip, knee and ankle. Different from the traditional feedforward network structure, this model has several feedback loops, thus it can take advantage of the output feedback information. To validate the effectiveness of the proposed method, five able-bodied people participated in the cycling exercises and relevant data were recorded in real time. The performance of the proposed prediction model is compared to those of the feedforward neural network with augmented inputs (FFNNAI) for the motion prediction accuracy and robustness. The results show that the proposed method provides acceptable performance which is clearly better than the FFNNAI-based approach under different experimental schemes.

An sEMG-driven neuromusculoskeletal model of upper limb for rehabilitation robot control

Abstract: This study proposes a new method for modeling the complicated dynamics between motor neural signal (surface electromyography, sEMG) and corresponding joint torque of muscle contraction (sEMG-driven neuromusculoskeletal model), which has potential to be used for rehabilitation robot control and neuromuscular evaluation after stroke, etc. In this model, muscle activation dynamics and contraction dynamics are built based on Hill-type muscle model, which has many parameters to be determined using optimization methods, and training samples of sEMG, joint angles, and joint torques are acquired with the aid of an upper-limb rehabilitation robot. Subject-specific parameters are initialized with scaled Standford VA model data by subjects' weight, limb length, etc., and the model is optimized using the genetic algorithm (GA). Based on this study, characteristics of a certain single muscle during voluntary movements can be obtained by measuring their sEMG signals, and the motor commands of brain are decoded in some degree.

PCA-based muscle selection for interventional manipulation recognition

Abstract: Vascular interventional robot is becoming increasingly popular in assisting doctors for the treatment of cardiovascular diseases (CVDs). However, natural manipulations and motion patterns of surgeons in percutaneous coronary intervention (PCI), including finger motion and hand motion, are potentially altered more or less through this solution. Since clinical success is highly dependent on the skills and dexterous manipulation strategies of the surgeons, but these skills and experience remains an underused resource in robot-assisted intervention. In this paper, surface electromyography (sEMG) of surgeons' hand and arm muscles, a feature of manipulation skills in intervention, is acquired to recognize six interventional manipulations with Back-Propagation (BP) neural network. A modified principal component analysis (PCA) is presented to select a sensitive and principal muscle subset for improving the fiexibility of subjects' manipulations. Experimental results show that the sensitive muscle subset with the recognition rate over 90% performs better than the insensitive one, and the sEMG-based BP neural network has the ability to recognize interventional manipulations with a high accuracy.

Catheter or guide wire manipulating device with two-point-clamping for vascular intervention

Abstract: A catheter or guide wire manipulating device with two-point-clamping for vascular intervention is provided, comprising a thumb component (3), a forefinger component (4), a driving component (1) and a catheter/guide wire support component (2); the thumb component (3) comprises a pair of rollers (9, 10) configured to advance or retreat the catheter/guide wire; the thumb component (3) is configured to drive the catheter/guide wire to rotate clockwise or counterclockwise through a combination motion of the components; the forefinger component (4) is configured to implement the rotation and the advancement of the catheter/guide wire by moving manually away from the thumb component (3), and returning by a pull force of a spring (27) after being released; the driving component (2) is configured to drive the thumb component (3) and the forefinger component (4); the catheter/guide wire support component comprises a Y adapter fixation configured to install a Y adapter and an entry support configured to support and guide the catheter/guide wire into a mechanism.

Convergence rate of leader-following consensus of networks of discrete-time linear agents in noisy environments

Abstract: A mean square leader-following consensus protocol is proposed for discrete-time linear multi-agent systems with communication noises. To attenuate the noise's effect, a specific class of time-varying consensus gains are applied to the noise-corrupted relative states between agents. The distinguished feature of the proposed protocol is that each agent can have its own noise-attenuation gain. Both the steady-state performance and the transient performance of the closed-loop multi-agent system are analyzed. The convergence rates of the mathematical expectation and the second-moment of the leader-following consensus error are explicitly presented.

A vessel contour detection and estimation method for robot assisted endovascular surgery

Abstract: X-ray angiography is a common method for image-guided navigation comprising surgical devices and vessel contours detection which can offer visual feedback to physicians. However, for the sake of both physicians' and patients' health, the contrast agent is injected intermittently and in a low dosage, so the X-ray images are not always of adequate quality for visual examination. So the navigation, which is to achieve the real-time positions of vessels and devices, is a challenging task. On one hand, the vessels and devices are always affected by other tissues. On the other hand, when the contrast agent is absent, vessels are invisible, so it is impossible to detect vessel contours. Aimed at the above problems, this paper proposes a vessel detection and estimation method. The paper has two main contributions. First, the paper uses the multiple scales top-hat enhancement method to improve image contrast rate, which is benefit for the vessel contours detection. Second, the paper proposes a method to estimate the vessel contours when the contrast agent is absent, it is based on the fact that vessel moves in a small range and the vessel contours are estimated by using the contour detection results of the adjacent frames. The experiment has been tested on 18 sequences images and the average accuracy is 92.6%. The subjective evaluation and the experiment results demonstrate the effectiveness and robust of the proposed method.

Tracking natural guidewire manipulations with an improved data glove

Abstract: Natural guidewire manipulations of surgeons in percutaneous coronary intervention (PCI), rotating and translating a guidewire with finger and hand motion, are important and useful references to build a knowledge base (KB) for robotic autonomous intervention. In this paper, guidewire manipulation models are proposed using an improved data glove, in which a 6-DOF position/orientation sensor is installed. The tracking functions between the surgeon's manipulations and guidewire motions are established according to the models. The performance of proposed method is analyzed with tracking errors and the statistical significance of tracking trajectories is evaluated with the Kruskal-Wallis test. The results show that radial rotation tracking error is 20.24 ± 8.38 (°) and axial advancement tracking error is 0.99 ± 0.81 (mm) at a medium delivery speed.