Control and Intelligent Systems
Output Reachable Set-Based Leader-Following Consensus of Positive Agents Over Switching Networks
A scheme of output reachable set-based leader-following consensus is developed for multiple positive agents over directed dwell-time switching networks. Two types of non-negative disturbances, namely, 1) L1-norm bounded disturbances and 2) L∞,1-norm bounded disturbances are studied. A class of directed dwell-time switching networks for modelling time-varying communication protocols is investigated. To deal with the presence of disturbances, an output-feedback control protocol is developed to achieve a robust consensus with positivity preserved based on the output reachable set. By exploiting the positive characteristics, switched linear copositive Lyapunov functions are adopted to establish output reachable set-based consensus conditions. These conditions can facilitate the control protocol design by solving a bilinear programming problem, and also generate hyperpyramidal regions to confine the output consensus error. A particle swarm optimization-based (PSO-based) algorithm is applied to compute the controller gains and optimize the volume of the hyperpyramids. Numerical studies are conducted to show the effectiveness of the developed output reachable set-based consensus results.
A Fast Soft Robotic Laser Sweeping System Using Data-Driven Modeling Approach
A simple, compact two-segment soft robot for flexible laser ablation is proposed. The proximal hydraulic-driven segment can offer omnidirectional bending so as to navigate toward lesions. The distal segment driven by tendons enables precise, fast steering of laser collimator for laser sweeping on lesion targets. The dynamics of such mechanical steering motion can be enhanced with a metal spring backbone integrated along the collimator, thus facilitating the control with certain linearity and responsiveness. A soft robot modeling and control scheme based on Koopman operators is proposed. We also design a disturbance observer so as to incorporate the controller feedback with real-time fiber optic shape sensing. Experimental validation is conducted on simulated or ex-vivo laser ablation tasks, thus evaluating our control strategies in laser path following across various contours/patterns. As a result, such a simple compact laser manipulation can perform up to 6 Hz sweeping with the precision of path following errors below 1 mm. Such modeling and control scheme could also be used on an endoscopic laser ablation robot with an unsymmetric mechanism driven by two tendons.
Learning-Based Visual-Strain Sensing Fusion and Hybrid Control System for Soft Robots
We fuse visual information with the sparse strain data collected from a single-core fiber inscribed with fiber Bragg gratings (FBGs) to facilitate continuum robot pose estimation. An improved extreme learning machine algorithm with selective training data updates is implemented to establish and refine the FBG-empowered (Femp) pose estimator online. The integration of F-emp pose estimation can improve sensing robustness by reducing the number of times that visual tracking is lost given moving visual obstacles and varying lighting. In particular, this integration solves pose estimation failures under full occlusion of the tracked features or complete darkness. Utilizing the fused pose feedback, a hybrid controller incorporating kinematics and data-driven algorithms is proposed to accomplish fast convergence with high accuracy. The online-learning error compensator can improve the target tracking performance with a 52.3%–90.1% error reduction compared with constant-curvature model based control, without requiring fine model-parameter tuning and prior data acquisition.
Stability and L1-Gain Analysis of Periodic Piecewise Positive Systems with Constant Time Delay
The stability and L1- gain analysis of periodic piecewise positive systems with constant time delay is developed. λ-exponential stability, which is applied to characterize the decay rates of the considered systems, is investigated first. A copositive Lyapunov–Krasovskii functional is used to obtain a sufficient stability condition. The stability condition characterizes the convergent speed of the state by the system matrices and the size of the time delay. One can also apply the Lyapunov–Krasovskii functional to characterize the L1-gain of the systems. By taking advantage of the periodic property of the system, linear inequalities are employed to characterize the L1-gain, and an unweighted upper bound of the L1-gain of the system is given. By introducing more degrees of freedom in the functional, the conservativeness of the systems is reduced.
A novel scheme of nonfragile controller design for periodic piecewise LTV systems
A novel nonfragile controller design scheme is developed for a class of periodic piecewise systems with linear time-varying subsystems. Two types of norm-bounded controller perturbations, including additive and multiplicative ones, are considered and partially characterized by periodic piecewise time-varying parameters. Using a new matrix polynomial lemma, the problems of nonfragile controller synthesis for periodic piecewise time-varying systems (PPTVSs) are made amenable to convex optimization based on the favorable property of a class of matrix polynomials. Depending on selectable divisions of subintervals, sufficient conditions of the stability and nonfragile controller design are proposed for PPTVSs. Case studies based on a multi-input multi-output PPTVS and a mass-spring-damper system show that the proposed control schemes can effectively guarantee the close-loop stability and accelerate the convergence under controller perturbations, with more flexible periodic time-varying controller gains than those obtained by the existing methods.
Development of an Open‐Access and Explainable Machine Learning Prediction System to Assess the Mortality and Recurrence Risk Factors of Clostridioides Difficile Infection Patients
Identifying Clostridioides difficile infection (CDI) patients at risk of mortality or recurrence will facilitate prevention, timely treatment and improve clinical outcomes. The aim of this paper is to establish an open-access web-based prediction system, which estimates CDI patients’ mortality and recurrence outcomes, and explains the machine learning prediction with patients’ characteristics. Prognostic models were developed using four various types of machine learning algorithms and statistical logistics regression model utilizing over 15,000 CDI patients from 41 hospitals in Hong Kong. The boosting-based machine learning algorithm Gradient Boosting Machine (Mortality AUC: 0.7878; Recurrence AUC: 0.7076) outperformed statistical models (Mortality AUC: 0.7573; Recurrence AUC: 0.6927) and other machine learning algorithms. As the difficulty to interpret complex machine learning results had limited their use in the medical area, we adopted Shapley additive explanations (SHAP) to identify which features are crucial to the machine learning models and associate them with clinical findings. SHAP analysis showed that older age, reduced albumin levels, higher creatinine levels, and higher white blood cell count are the most highly associated mortality features, which is consistent with existing clinical findings. The open-access prediction system for clinicians to assess and interpret the risk factors of CDI patients is now available at https://www.cdiml.care/.
Open-access prediction system for CDI patients: https://www.cdiml.care/
Workflow for machine learning model selection and deployment
 X. Xie, J. Lam, K.W. Kwok, “A novel scheme of nonfragile controller design for periodic piecewise LTV systems,” IEEE Transactions on Industrial Electronics, 67(12):10766-10775, 2020
 Y.L. Ng, C.K. Lo, K.H. Lee, X. Xie, T. N.Y. Kwong, M. Ip, L. Zhang, J. Yu, J. J.Y. Sung, W.K.K. Wu, S. H. Wong, K.W. Kwok, “Development of an open-access and explainable machine learning prediction system to assess the mortality and recurrence risk factors of Clostridioides difficile infection patients,” Advanced Intelligent Systems 3(1): 2000188, 2020 Detail
 X. Wang, J. Dai, H.S. Tong, K. Wang, G. Fang, X. Xie, Y.H. Liu, K.W.S. Au, K.W. Kwok, “Learning-based Visual-Strain Fusion for Eye-in-hand Continuum Robot Pose Estimation and Control,” IEEE Transactions on Robotics (TRO), 39(3), 2448-2467, 2023 Detail
 K. Wang, J.D.L. Ho, X. Wang, G. Fang, B. Zhu, R. Xie, Y.H. Liu, K.W.S. Au, J.Y.K. Chan, K.W. Kwok,“A Fast Soft Robotic Laser Sweeping System using Data-driven Modelling Approach,” IEEE Transactions on Robotics (TRO), 39(4), 3-43-3058, 2023 Detail
 B. Zhu, J. Lam, X. Xie, X. Song, K.W. Kwok, "Stability and L1-gain analysis of periodic piecewise positive systems with constant time delay," IEEE Transactions on Automatic Control (TAC), 67(5), 2655-2662, 2022 Detail
 C. Fan, J. Lam, K.F. Chu, X. Lu, K.W. Kwok, “Output Reachable Set-based Leader-following Consensus of Positive Agents over Switching Networks,” IEEE Transactions on Cybernetics (TCYB) (Early Access) Detail