Hierarchical finite-time cooperative control for teleoperation of networked disturbed mobile manipulators.

This paper investigates the teleoperation problem of networked disturbed mobile manipulators (NDMMs), in which the human operator remotely controls multiple slave mobile manipulators through a master manipulator. Each individual of the slave ones consisted of a nonholonomic mobile platform and a holonomic constrained manipulator that is mounted on the nonholonomic mobile platform. The cooperative control objective of the considered teleoperation problem includes: (1) synchronizing the states of the slave manipulators to the human-controlled master one; (2) forcing the mobile platforms of the slave ones to form a user-defined formation; (3) controlling the geometric center of all the platforms to track a reference trajectory. We present a hierarchical finite-time cooperative control (HFTCC) framework to achieve the cooperative control goal in a finite time. The presented framework includes the distributed estimator, the weight regulator and the adaptive local controller, where the estimator generates the estimated states of the desired formation and trajectory, the regulator selects the slave robot that the master one needs to track, as well as the presented adaptive local controller guarantees the finite-time convergence of the controlled states with model uncertainties and disturbances. Additionally, for improving the telepresence, a novel super twisting observer is presented to reconstruct the interaction force between the salve mobile manipulators and the remote operating environment on the master (i.e., the human) side. Finally, the effectiveness of the proposed control framework is demonstrated by several simulation results.

FSG-based divinable time-varying formation tracking of multiple Lagrangian agents with unknown disturbances and directed graphs.

In this paper, a flexible shape generator (FSG) is designed to achieve the divinable transformation process of the time-varying formation, and consider the FSG-based time-varying formation tracking (TVFT) problem of multiple Lagrangian agents with unknown disturbances and directed graphs. A hierarchical control algorithm is newly designed to achieve the control goal without using the prior information of the system model and bounded disturbances, and the specific implementation of the proposed hierarchical algorithms is also provided. By using the Hurwitz criterion and adaptive system theory, the sufficient conditions are derived and the stability analysis show that the formation tracking errors of the considered system are uniform ultimate bounded. Several simulation examples are performed on five two-degree-of-freedom mechanical arms to show the effectiveness of theoretical results.

Event-Triggered Consensus Control for Networked Underactuated Robotic Systems.

In this article, the consensus of networked underactuated robotic systems subject to fixed and switched communication networks is discussed by developing some novel event-triggered control algorithms, which can synchronously guarantee the convergence of the active states, the boundedness of the velocities of passive actuators, and the exclusion of Zeno behaviors. In the cases of fixed networks, the sufficient criteria are established for the presented distributed event-triggered mechanisms with and without using neighbors' velocities, in order to achieve a better tradeoff between the communication load and system performance. Besides, in the situation of switched networks, the sufficient criterion is established by assuming that the union of the network has a spanning tree. A distributed sampled-data rule is constructed to decide when to update its own and neighbors' estimated positions, and thus further reduces the unnecessary control cost. Finally, by further extending the main results to three other sampled-data control algorithms, several examples with performance comparisons are provided to validate the efficiency and advantages of the theoretical results.

Lag-Bipartite Formation Tracking of Networked Robotic Systems Over Directed Matrix-Weighted Signed Graphs.

This article studies the lag-bipartite formation tracking (LBFT) problem of the networked robotic systems (NRSs) with directed matrix-weighted signed graphs. Unlike the traditional formation tracking problems with only cooperative interactions, solving the LBFT problem implies that: 1) the robots of the NRS are divided into two complementary subgroups according to the signed graph, describing the coexistence of cooperative and antagonistic interactions; 2) the states of each subgroup form a desired geometric pattern asymptotically in the local coordinate; and 3) the geometric center of each subgroup is forced to track the same leader trajectory with different plus-minus signs and a time lag. A new hierarchical control algorithm is designed to address this challenging problem. Based on the Lyapunov stability argument and the property of the matrix-weighted Laplacian, some sufficient criteria are derived for solving the LBFT problem. Finally, simulation examples are proposed to validate the effectiveness of the main results.

Stochastic quasi-synchronization of heterogeneous delayed impulsive dynamical networks via single impulsive control.

This paper investigates the quasi-synchronization problem of the stochastic heterogeneous complex dynamical networks with impulsive couplings and multiple time-varying delays. It is shown that this kind of dynamical networks can achieve exponential quasi-synchronization by exerting impulsive control added on only one chosen pinning node. By employing the Lyapunov stability theory, some sufficient criteria on quasi-synchronization for this dynamical network are established, revealing the relationship between the quasi-synchronization performance and the stochastic perturbations as well as the frequency and strength of impulsive coupling. Finally, some numerical examples are used to illustrate the effectiveness of the main results.

Model-Independent Formation Tracking of Multiple Euler-Lagrange Systems via Bounded Inputs.

This article addresses two kinds of formation tracking problems, namely: 1) the practical formation tracking (PFT) problem and 2) the zero-error formation tracking (ZEFT) problem for multiple Euler-Lagrange systems with input disturbances and unknown models. In these problems, the bounded input constraint, which can be possibly caused by actuator saturation and power limitations, is taken into consideration. Then, the two classes of model-independent distributed control approaches, in which the prior information (i.e., the structures and features) of the system model is not used, are proposed correspondingly. Based on the nonsmooth analysis and Lyapunov stability theory, several novel criteria for achieving PFT and ZEFT of multiple Euler-Lagrange systems are derived. Finally, numerical simulations and comparisons are presented to verify the validity and effectiveness of the proposed control approaches.

Output Multiformation Tracking of Networked Heterogeneous Robotic Systems via Finite-Time Hierarchical Control.

This article investigates the finite-time output multiformation tracking (OMFT) problem of networked heterogeneous robotic systems (NHRSs), where each robot model involves external disturbances, parametric uncertainties, and possible kinematic redundancy. Besides, the interactions among robotic systems are described as a directed graph with an acyclic partition. Then, several novel practical finite-time hierarchical control (FTHC) algorithms are designed. The convergence analysis of the closed-loop dynamics is extremely difficult due to the lack of effective analysis methods. Based on the mathematics induction and reductio ad absurdum, a new nonsmooth Lyapunov function is proposed to derive the sufficient conditions and settling time functions. Finally, numerical simulations are performed on the NHRS to verify the main results.

Second-order bipartite consensus for networked robotic systems with quantized-data interactions and time-varying transmission delays.

This paper investigates the second-order bipartite consensus (BC) problems for networked robotic systems (NRSs) subject to model uncertainties and external disturbances over signed directed graphs. We have newly presented two classes of hierarchical control algorithms (HCAs) to solve the BC problems for NRSs in a more practical and challenging case of quantized-data interactions (QDIs) and time-varying transmission delays (TVTDs). By using Hurwitz criterion and Lyapunov stability argument, several sufficient conditions on control parameters are obtained and the BC performance of the regulated system is analyzed. Three examples validate the presented theoretical findings.

Design of a 3DOF XYZ Bi-Directional Motion Platform Based on Z-Shaped Flexure Hinges.

This paper presents a design of a 3DOF XYZ bi-directional motion platform based on Z-shaped flexure hinges. In the presented platform, bridge-type mechanisms and Z-shaped flexure hinges are adopted to amplify its output displacement. Bi-direction motion along the X-axis and Y-axis follows the famous differential moving principle DMP, and the bi-directional motion along the Z-axis is realized by using the reverse arrangement of the Z-shaped flexure hinges along the X-axis and Y-axis. Statics analysis of the proposed platform is carried out by the energy method, compliance matrix method, and force balance principle. Meanwhile, the Lagrange method is used to analyze the dynamics of the platform. A series of simulations are conducted to demonstrate the effectiveness of the proposed design. The simulation results show that the average displacements of the platform in the XYZ-axis are ±125.58 µm, ±126.37 µm and ±568.45 µm, respectively.

Cloaking object on an optofluidic chip: its theory and demonstration.

Recently, the design of metamaterial guided by transformation optics (TO) has emerged as an effective method to hide objects from optical detection, based on arranging a bended light beam to detour. However, this TO-based solution involves fabrication of material with complicated distribution of permittivity and permeability, and the device falls short of tunability after fabrication. In this work, we propose an optofluidic model employing the method of streamline tracing-based transformation optofluidics (STTOF) to hydrodynamically reconfigure light propagation in a given flow field for object-cloaking purposes. The proof-of-concept is demonstrated and tested on an optofluidic chip to validate our proposed theory. Experimental results show that our proposed STTOF method can be used to successfully detour the light path from the object under cloaking in a mathematically pre-defined manner.

Hierarchical Controller-Estimator for Coordination of Networked Euler-Lagrange Systems.

This paper proposes several hierarchical controller-estimator algorithms (HCEAs) to solve the coordination problem of networked Euler-Lagrange systems (NELSs) with sampled-data interactions and switching interaction topologies, where the cases with both discontinuous and continuous signals are successfully addressed in a unified framework. The HCEAs comprise two main layers (i.e., a control layer and an estimator layer) and one optional layer (i.e., a filter layer), in which the coordination problem is tackled in the main layers and the transient response can be optionally smoothed in the filter layer. For stabilizing the corresponding cascade closed-loop systems, several sufficient conditions on the upper bound of the aperiodic sampling intervals and the lower bound of the control parameters are established. In addition, the HCEAs are extended to address the task-space coordination problem of networked heterogeneous robotic systems, which shows the versatility of the HCEAs. Finally, comparison studies and simulation results are provided to demonstrate the effectiveness, significance, and advantages of the presented algorithms.

A Modified Duhem Model for Rate-Dependent Hysteresis Behaviors.

Hysteresis behaviors are inherent characteristics of piezoelectric ceramic actuators. The classical Duhem model (CDM) as a popular hysteresis model has been widely used, but cannot precisely describe rate-dependent hysteresis behaviors at high-frequency and high-amplitude excitations. To describe such behaviors more precisely, this paper presents a modified Duhem model (MDM) by introducing trigonometric functions based on the analysis of the existing experimental data. The MDM parameters are also identified by using the nonlinear least squares method. Six groups of experiments with different frequencies or amplitudes are conducted to evaluate the MDM performance. The research results demonstrate that the MDM can more precisely characterize the rate-dependent hysteresis behaviors comparing with the CDM at high-frequency and high-amplitude excitations.

Janus Gold Triangle-Mesoporous Silica Nanoplatforms for Hypoxia-Activated Radio-Chemo-Photothermal Therapy of Liver Cancer.

Radiation dosage constraints and hypoxia-associated resistance lead to the failure of radiotherapy (RT), especially in hypoxic liver cancer. Therefore, the intricate use of combined strategies for potentiating and complementing RT is especially important. In this work, we fabricated multifunctional Janus-structured gold triangle-mesoporous silica nanoparticles (NPs) as multifunctional platforms to deliver the hypoxia-activated prodrug tirapazamine (TPZ) for extrinsic radiosensitization, local photothermal therapy, and hypoxia-specific chemotherapy. The subsequent conjugation of folic acid-linked poly(ethylene glycol) provided the Janus nanoplatforms with liver cancer targeting and minimized opsonization properties. In vitro and in vivo experiments revealed the combined radiosensitive and photothermal antitumor effects of the Janus nanoplatforms. Importantly, the TPZ-loaded Janus nanoplatforms exhibited pH-responsive release behavior, which effectively improved the cellular internalization and therapeutic efficiency in hypoxic rather than normoxic liver cancer cells. Hypoxia-specific chemotherapy supplemented the ineffectiveness of radio-photothermal therapy in hypoxic tumor tissues, resulting in remarkable tumor growth inhibition without systematic toxicity. Therefore, our Janus nanoplatforms integrated radio-chemo-photothermal therapy in a hypoxia-activated manner, providing an efficient and safe strategy for treating liver cancer.

Berberine-loaded Janus gold mesoporous silica nanocarriers for chemo/radio/photothermal therapy of liver cancer and radiation-induced injury inhibition.

Background: The combination of chemotherapy with radiotherapy serves as a common therapeutic strategy in clinics. However, it is unsatisfactory due to its poor therapeutic efficiency and severe side-effects originating from chemotherapy-exerted systemic toxicity as well as radiation-induced injury. Purpose: Hence, Berberine (Ber), an isoquinolin alkaloid with low toxicity and protective effects against radiotherapy, was used as a novel chemotherapeutic agent for chemo-radiotherapy of liver cancer. Patients and methods: We preloaded Ber into folic acid targeting Janus gold mesoporous silica nanocarriers (FA-JGMSNs) for overcoming the poor bioavailability of Ber. Furthermore, FA-JGMSNs were not only employed as radiosensitizers for expanding radiotherapeutic effect, but also used as photothermal agents for supplementing chemo-radiotherapeutic effect by local photothermal therapy. Results: In vitro and in vivo experiemtal results demonstrated the highly efficient anti-tumor effect, good biosafety as well as the effective protection of normal tissue of this nanoplatform. Conclusion: Based on its superb performance, we believe our work provided a feasible strategy for triple-therapies of liver cancer.

A Hybrid Multi-Objective Optimization Model for Vibration Tendency Prediction of Hydropower Generators.

The hydropower generator unit (HGU) is a vital piece of equipment for frequency and peaking modulation in the power grid. Its vibration signal contains a wealth of information and status characteristics. Therefore, it is important to predict the vibration tendency of HGUs using collected real-time data, and achieve predictive maintenance as well. In previous studies, most prediction methods have only focused on enhancing the stability or accuracy. However, it is insufficient to consider only one criterion (stability or accuracy) in vibration tendency prediction. In this paper, an intelligence vibration tendency prediction method is proposed to simultaneously achieve strong stability and high accuracy, where vibration signal preprocessing, feature selection and prediction methods are integrated in a multi-objective optimization framework. Firstly, raw sensor signals are decomposed into several modes by empirical wavelet transform (EWT). Subsequently, the refactored modes can be obtained by the sample entropy-based reconstruction strategy. Then, important input features are selected using the Gram-Schmidt orthogonal (GSO) process. Later, the refactored modes are predicted through kernel extreme learning machine (KELM). Finally, the parameters of GSO and KELM are synchronously optimized by the multi-objective salp swarm algorithm. A case study and analysis of the mixed-flow HGU data in China was conducted, and the results show that the proposed model performs better in terms of predicting stability and accuracy.

Global stabilization analysis of inertial memristive recurrent neural networks with discrete and distributed delays.

This paper deals with the stabilization problem of memristive recurrent neural networks with inertial items, discrete delays, bounded and unbounded distributed delays. First, for inertial memristive recurrent neural networks (IMRNNs) with second-order derivatives of states, an appropriate variable substitution method is invoked to transfer IMRNNs into a first-order differential form. Then, based on nonsmooth analysis theory, several algebraic criteria are established for the global stabilizability of IMRNNs under proposed feedback control, where the cases with both bounded and unbounded distributed delays are successfully addressed. Finally, the theoretical results are illustrated via the numerical simulations.

Optimal modified tracking performance for MIMO networked control systems with communication constraints.

This paper investigates the optimal modified tracking performance of multi-input multi-output (MIMO) networked control systems (NCSs) with packet dropouts and bandwidth constraints. Some explicit expressions are obtained by using co-prime factorization and the spectral decomposition technique. The obtained results show that the optimal modified tracking performance is related to the intrinsic properties of a given plant such as non-minimum phase (NMP) zeros, unstable poles, and their directions. Furthermore, the modified factor, packet dropouts probability and bandwidth also impact the optimal modified tracking performance of the NCSs. The optimal modified tracking performance with channel input power constraint is obtained by searching through all stabilizing two-parameter compensator. Finally, some typical examples are given to illustrate the effectiveness of the theoretical results.