Event-triggered adaptive control for nonlinear systems using time-receding horizons without initial dependence.

This paper investigates the full-state constraint event-triggered adaptive control for a class of uncertain strict-feedback systems. The lack of information on the coupling dynamics of virtual variables in backstepping increases the complexity of feedback design. Given this, the requirements of shaping system performance constraints, eliminating initial dependence, and reducing data transfer costs together give rise to an interesting and challenging problem. Constructing the time-receding horizon (TRH) and stitching it with the quadratic Lyapunov function (QLF) is the key to constrained tracking. Specifying TRHs as a set of smooth bounds with fixed-time convergence and forcing the system to stabilize within the constrained region before the prescribed settling time provide a sufficient condition for practical finite-time stability (PFS). For relaxing the initial dependence, a tuning function is designed to match the performance constraints under arbitrary system initial conditions. A dual-channel event-triggered mechanism (ETM) is developed to automatically adjust the controller and estimator data flow updates with less transmission burden. By combining a specific inequality with backstepping, uncertainties are overcome without the "complexity explosion" in recursion steps. Finally, simulations demonstrate the effectiveness of the proposed method.

Saturation-Tolerant Prescribed Control for Nonlinear Systems With Unknown Control Directions and External Disturbances.

In this article, saturation-tolerant prescribed control (SPC) is investigated for a class of multiinput-multioutput (MIMO) nonlinear systems. The key challenge lies in how to guarantee both input and performance constraints simultaneously for nonlinear systems especially under external disturbance and unknown control directions. We propose concise finite-time tunnel prescribed performance (FTPP) for better tracking performance, which features tight allowable set and user-specified settling time. To comprehensively tackle the conflict between the above two constraints, an auxiliary system is designed to explore their interconnections instead of neglecting their contradictions. By introducing its generated signals into FTPP, the obtained saturation-tolerant prescribed performance (SPP) has the ability to degrade or recover the performance boundaries in the light of different saturation conditions. Consequently, the developed SPC, together with nonlinear disturbance observer (NDO), can effectively improve the robustness and reduce the conservatism against external disturbances, input, and performance constraints. Finally, comparative simulations are presented to showcase these theoretical findings.

Adaptive Safe Reinforcement Learning With Full-State Constraints and Constrained Adaptation for Autonomous Vehicles.

High-performance learning-based control for the typical safety-critical autonomous vehicles invariably requires that the full-state variables are constrained within the safety region even during the learning process. To solve this technically critical and challenging problem, this work proposes an adaptive safe reinforcement learning (RL) algorithm that invokes innovative safety-related RL methods with the consideration of constraining the full-state variables within the safety region with adaptation. These are developed toward assuring the attainment of the specified requirements on the full-state variables with two notable aspects. First, thus, an appropriately optimized backstepping technique and the asymmetric barrier Lyapunov function (BLF) methodology are used to establish the safe learning framework to ensure system full-state constraints requirements. More specifically, each subsystem's control and partial derivative of the value function are decomposed with asymmetric BLF-related items and an independent learning part. Then, the independent learning part is updated to solve the Hamilton-Jacobi-Bellman equation through an adaptive learning implementation to attain the desired performance in system control. Second, with further Lyapunov-based analysis, it is demonstrated that safety performance is effectively doubly assured via a methodology of a constrained adaptation algorithm during optimization (which incorporates the projection operator and can deal with the conflict between safety and optimization). Therefore, this algorithm optimizes system control and ensures that the full set of state variables involved is always constrained within the safety region during the whole learning process. Comparison simulations and ablation studies are carried out on motion control problems for autonomous vehicles, which have verified superior performance with smaller variance and better convergence performance under uncertain circumstances. The effectiveness of the safe performance of overall system control with the proposed method accordingly has been verified.

Sliding-Mode Control for Perturbed MIMO Systems With Time-Synchronized Convergence.

This article introduces a novel approach called terminal sliding-mode control for achieving time-synchronized convergence in multi-input-multi-output (MIMO) systems under disturbances. To enhance controller design, the systems are categorized into two groups: 1) input-dimension-dominant and 2) state-dimension-dominant, based on signal dimensions and their potential for achieving thorough time-synchronized convergence. We explore sufficient Lyapunov conditions using terminal sliding-mode designs and develop adaptive controllers for the input-dimension-dominant case. To handle perturbations, we design a multivariable disturbance observer with a super-twisting structure, which is integrated into the controller. By utilizing the sliding-mode technique and the disturbance observer, the proposed controller ensures simultaneous convergence of all output dimensions. In the state-dimension-dominant case, where a full-rank system matrix is absent, only specific output elements converge to equilibrium simultaneously. We conduct comparative simulations on a practical system to highlight the effectiveness of our proposed method for the input-dimension-dominant case. Statistical results reveal the benefits of shorter output trajectories and reduced energy consumption. For the state-dimension-dominant case, we present numerical examples to validate the semi-time-synchronized property.

Tunnel Prescribed Control of Nonlinear Systems With Unknown Control Directions.

This article solves the entry capture problem (ECP) such that for any initial tracking error, it can be regulated into the prescribed performance constraints within a user-given time. The challenge lies in how to remove the initial condition limitation and to handle the ECP for nonlinear systems under unknown control directions and asymmetric performance constraints. For better tracking performance, we propose a unified tunnel prescribed performance (TPP) providing strict and tight allowable set. With the aid of a scaling function, error self-tuning functions (ESFs) are then developed to make the control scheme suitable to any initial condition (including the initial constraint violation), where the initial values of ESFs always satisfy performance constraints. In lieu of the Nussbaum technique, an orientation function is introduced to deal with unknown control directions while such way is capable of reducing the control peaking problem. Using ESFs, together with TPP and an orientation function, the resulted tunnel prescribed control (TPC) leads to a solution for the underlying ECP, which also exhibits a low complexity level since no command filters or dynamic surface control is required. Finally, simulation results are provided to further demonstrate these theoretical findings.

Skin-like cryogel electronics from suppressed-freezing tuned polymer amorphization.

The sole situation of semi-crystalline structure induced single performance remarkably limits the green cryogels in the application of soft devices due to uncontrolled freezing field. Here, a facile strategy for achieving multifunctionality of cryogels is proposed using total amorphization of polymer. Through precisely lowering the freezing point of precursor solutions with an antifreezing salt, the suppressed growth of ice is achieved, creating an unusually weak and homogenous aggregation of polymer chains upon freezing, thereby realizing the tunable amorphization of polymer and the coexistence of free and hydrogen bonding hydroxyl groups. Such multi-scale microstructures trigger the integrated properties of tissue-like ultrasoftness (Young's modulus <10 kPa) yet stretchability, high transparency (~92%), self-adhesion, and instantaneous self-healing (<0.3 s) for cryogels, along with superior ionic-conductivity, antifreezing (-58 °C) and water-retention abilities, pushing the development of skin-like cryogel electronics. These concepts open an attractive branch for cryogels that adopt regulated crystallization behavior for on-demand functionalities.

Graph-Structure-Based Multigranular Belief Fusion for Human Activity Recognition.

The belief functions (BFs) introduced by Shafer in the mid of 1970s are widely applied in information fusion to model epistemic uncertainty and to reason about uncertainty. Their success in applications is however limited because of their high-computational complexity in the fusion process, especially when the number of focal elements is large. To reduce the complexity of reasoning with BFs, we can envisage as a first method to reduce the number of focal elements involved in the fusion process to convert the original basic belief assignments (BBAs) into simpler ones, or as a second method to use a simple rule of combination with potentially a loss of the specificity and pertinence of the fusion result, or to apply both methods jointly. In this article, we focus on the first method and propose a new BBA granulation method inspired by the community clustering of nodes in graph networks. This article studies a novel efficient multigranular belief fusion (MGBF) method. Specifically, focal elements are regarded as nodes in the graph structure, and the distance between nodes will be used to discover the local community relationship of focal elements. Afterward, the nodes belonging to the decision-making community are specially selected, and then the derived multigranular sources of evidence can be efficiently combined. To evaluate the effectiveness of the proposed graph-based MGBF, we further apply this new approach to combine the outputs of convolutional neural networks + attention (CNN + Attention) in the human activity recognition (HAR) problem. The experimental results obtained with real datasets prove the potential interest and feasibility of our proposed strategy with respect to classical BF fusion methods.

Dynamic Gain Reduced-Order Observer-Based Global Adaptive Neural-Network Tracking Control for Nonlinear Time-Delay Systems.

In this article, a globally adaptive neural-network tracking control strategy based on the dynamic gain observer is proposed for a class of uncertain output-feedback systems with unknown time-varying delays. A reduced-order observer with novel dynamic gain is proposed. An n th-order continuously differentiable switching function is constructed to achieve the continuous switching control of the system, thus further ensuring that all the closed-loop signals are globally uniformly ultimately bounded (GUUB). It is proved that by adjusting the designed parameters, the tracking error converges to a region which can be adjusted to be small enough. The effectiveness of the control scheme is demonstrated by two simulation examples.

The opening workspace control strategy of a novel manipulator-driven emission source microscopy system.

Emission source microscopy (ESM) technique can be utilized for localization of electromagnetic interference sources in the electronic systems, but its accuracy is limited by the typical planar scanning mode. In order to increase the accuracy, this paper presents a novel cylinder-aperture ESM measurement system driven by 6-DOF manipulator, and investigated the control strategy to generate the maximum-area aperture and optimized scanning trajectory. Based on the multiple constraints of the cylinder-aperture ESM measurement, we proposes analyzing the impact of the constraints by steps. This can obtain the analytical solution of the manipulator workspace and support solving the maximum aperture area. Besides, a modified RRT*(Rapidly-exploring Random Trees) algorithm is addressed to optimize the manipulator trajectory. The simulation and tests have proven that this algorithm could obviously reduce the joint mutation and cumulative tracking error. In the experimental section, the near-field scanning (NFS) tests, planar-aperture ESM measurement and proposed cylinder-aperture ESM measurement were conducted to measure one benchmark emission source. The results have demonstrated that the cylinder-aperture ESM measurement has the best convergences on the radiation pattern of the emission source.

Globally Adaptive Neural Network Output-Feedback Control for Uncertain Nonlinear Systems.

In this article, a globally neural-network-based adaptive control strategy with flat-zone modification is proposed for a class of uncertain output feedback systems with time-varying bounded disturbances. A high-order continuously differentiable switching function is introduced into the filter dynamics to achieve global compensation for uncertain functions, thus further to ensure that all the closed-loop signals are globally uniformity ultimately bounded (GUUB). It is proven that the output tracking error converges to the prespecified neighborhood of the origin. The effectiveness of the proposed control method is verified by two simulation examples.

Distributed Active Fault-Tolerant Cooperative Control for Multiagent Systems With Communication Delays and External Disturbances.

This article investigates the distributed active fault-tolerant cooperative control problem for leader-follower multiagent systems (MASs) in the presence of multiple faults, communication delays, and external disturbances. A new distributed consensus protocol is put forward to ensure the state consensus of MASs, which can be served as a nominal controller in fault-free cases with communication delays and external disturbances. A novel distributed time-delay intermediate observer, which can estimate system states and multiple faults simultaneously, is derived based on the time-delay closed-loop system equation. By integrating a fault compensation mechanism into the nominal controller, a distributed active fault-tolerant consensus controller is constructed for the follower agents to eliminate the adverse effects of multiple faults. Simulation examples are provided to demonstrate the effectiveness of the proposed method.

An Intelligent Collaborative System for Robot Dynamics.

In this article, we propose an intelligent collaborative system for robotic navigation and control (CNaC) governed by the Euler-Lagrange equation. First, a state reconstruction based on neural networks navigation (SR-NNN) law is designed to estimate the current position of the robot for intelligent CNaC. The SR-NNN makes full use of partial truth information and the mighty local fitting ability of neural networks. In the absence of landmark, SR-NNN still exhibits navigation performance with high precision. The maximum root-mean-squared error (RMSE) of DR is 0.096 and the maximum RMSE of SR-NNN is 0.053, which has been improved by 55%. In addition, the motion model obtained by SR-NNN online training can avoid the error introduced by the predetermined motion model and overcome the interference of the external environment. The intelligent CNaC still can achieve satisfactory control performance based on the estimated position given by the SR-NNN rather than the ground truth which is formed by postprocessing. The intelligent CNaC has been demonstrated by simulation tracking sample and real experiments, which verifies the effectiveness of the intelligent CNaC.

Mode-Mixed Effects Based Intralayer-Dependent Impulsive Synchronization for Multiple Mismatched Multilayer Neural Networks.

This article focuses on the intralayer-dependent impulsive synchronization of multiple mismatched multilayer neural networks (NNs) with mode-mixed effects. Initially, a novel multilayer NN model that removes the one-to-one interlayer coupling constraint and introduces nonidentical model parameters is first established to meet diverse modeling requirements in complex applications. To help the multilayer target NNs with mismatched connection coefficients and time delays achieve synchronization, the hybrid controller is designed using intralayer-dependent impulsive control and switched feedback control approaches. Furthermore, the mode-mixed effects caused by the intralayer coupling delays and switched intralayer topologies are incorporated into the novel model and analysis method to ensure that the subsystems operating within the current switching interval can effectively use the topology information of the previous switching intervals. Then, a novel analysis framework including super-Laplacian matrix, augmented matrix, and mode-mixed methods is developed to derive the synchronization results. Finally, the main results are verified via the numerical simulation with secure communication.

A Spatial-Motion-Segmentation Algorithm by Fusing EDPA and Motion Compensation.

Motion segmentation is one of the fundamental steps for detection, tracking, and recognition, and it can separate moving objects from the background. In this paper, we propose a spatial-motion-segmentation algorithm by fusing the events-dimensionality-preprocessing algorithm (EDPA) and the volume of warped events (VWE). The EDPA consists of depth estimation, linear interpolation, and coordinate normalization to obtain an extra dimension (Z) of events. The VWE is conducted by accumulating the warped events (i.e., motion compensation), and the iterative-clustering algorithm is introduced to maximize the contrast (i.e., variance) in the VWE. We established our datasets by utilizing the event-camera simulator (ESIM), which can simulate high-frame-rate videos that are decomposed into frames to generate a large amount of reliable events data. Exterior and interior scenes were segmented in the first part of the experiments. We present the sparrow search algorithm-based gradient ascent (SSA-Gradient Ascent). The SSA-Gradient Ascent, gradient ascent, and particle swarm optimization (PSO) were evaluated in the second part. In Motion Flow 1, the SSA-Gradient Ascent was 0.402% higher than the basic variance value, and 52.941% faster than the basic convergence rate. In Motion Flow 2, the SSA-Gradient Ascent still performed better than the others. The experimental results validate the feasibility of the proposed algorithm.

Barrier Lyapunov Function-Based Safe Reinforcement Learning for Autonomous Vehicles With Optimized Backstepping.

Guaranteed safety and performance under various circumstances remain technically critical and practically challenging for the wide deployment of autonomous vehicles. Safety-critical systems in general, require safe performance even during the reinforcement learning (RL) period. To address this issue, a Barrier Lyapunov Function-based safe RL (BLF-SRL) algorithm is proposed here for the formulated nonlinear system in strict-feedback form. This approach appropriately arranges and incorporates the BLF items into the optimized backstepping control method to constrain the state-variables in the designed safety region during learning. Wherein, thus, the optimal virtual/actual control in every backstepping subsystem is decomposed with BLF items and also with an adaptive uncertain item to be learned, which achieves safe exploration during the learning process. Then, the principle of Bellman optimality of continuous-time Hamilton-Jacobi-Bellman equation in every backstepping subsystem is satisfied with independently approximated actor and critic under the framework of actor-critic through the designed iterative updating. Eventually, the overall system control is optimized with the proposed BLF-SRL method. It is furthermore noteworthy that the variance of the attained control performance under uncertainty is also reduced with the proposed method. The effectiveness of the proposed method is verified with two motion control problems for autonomous vehicles through appropriate comparison simulations.

Distributed optimized dynamic event-triggered control for unknown heterogeneous nonlinear MASs with input-constrained.

The distributed optimized dynamic event-triggered controller is investigated for completely unknown heterogeneous nonlinear multi-agent systems (MASs) on a directed graph subject to input-constrained. First, the distributed observer is designed to estimate the information of the leader for each follower, and a network of the augmented system is constructed by employing the dynamics of the followers and the observers. An identifier with a compensator is designed to approximate the unknown augmented system (agent) with an arbitrarily small identifier error. Then, consider that the input-constrained optimal controller, along with Hamilton-Jacobi-Bellman (HJB) equation, is under pressure to execute in certain systems associated with bottlenecks such as communication and computing burdens. A critic-actor-based optimized dynamic event-triggered controller, which tunes the parameters of critic-actor neural networks (NNs) by the dynamic triggering mechanism, is leveraged to determine the rule of aperiodic sampling and maintain the desired synchronization service. In addition, the existence of a positive minimum inter-event time (MIET) between consecutive events is also proved. Finally, the applications in non-identical nonlinear MAS and 2-DOF robots illustrate the availability of the proposed theoretical results.

Distributed Formation Control of Multiple Euler-Lagrange Systems: A Multilayer Framework.

In this technical correspondence, a multilayer formation (MLF) control problem is considered and solved by a unified framework. The agents in each layer present a sort of hierarchical distinction: receive information from former layers, communicate inside the current layer, and send information to subsequent layers. With an arbitrary number of layers, we extend the previous result from undirected graphs to directed ones. The proposed controller achieves MLF without using the distributed estimators and the acceleration information. This removes the induced discontinuities and alleviates the system complexity. It is then proved that the closed-loop errors are semiglobally uniformly ultimately bounded. Simulations are presented to illustrate the effectiveness of this approach.

Inner-Estimating Domains of Attraction for Nonpolynomial Systems With Polynomial Differential Inclusions.

In this article, based on polynomial differential inclusions, we propose a heuristic iterative approach for estimating the domains of attraction for nonpolynomial systems. First, we use the fuzzy model to construct a polynomial differential inclusion for the nonpolynomial system, which can be equivalently written as a time-invariant uncertain polynomial system. Then, beginning with an initial inner estimation, we present an iterative approach to enlarge this initial inner estimation by calculating common Lyapunov-like functions. Furthermore, the domains of attraction are estimated by combining this iterative approach with heuristic construction of differential inclusions. In the end, our heuristic iterative approach is implemented with linear semidefinite programming and then tested on some nonpolynomial examples with comparisons to the existing methods in the literature.

Saturation-Tolerant Prescribed Control for a Class of MIMO Nonlinear Systems.

This article proposes a saturation-tolerant prescribed control (SPC) for a class of multiinput and multioutput (MIMO) nonlinear systems simultaneously considering user-specified performance, unmeasurable system states, and actuator faults. To simplify the control design and decrease the conservatism, tunnel prescribed performance (TPP) is proposed not only with concise form but also smaller overshoot performance. By introducing non-negative modified signals into TPP as saturation-tolerant prescribed performance (SPP), we propose SPC to guarantee tracking errors not to violate SPP constraints despite the existence of saturation and actuator faults. Namely, SPP possesses the ability of enlarging or recovering the performance boundaries flexibly when saturations occur or disappear with the help of these non-negative signals. A novel auxiliary system is then constructed for these signals, which bridges the associations between input saturation errors and performance constraints. Considering nonlinearities and uncertainties in systems, a fuzzy state observer is utilized to approximate the unmeasurable system states under saturations and unknown actuator faults. Dynamic surface control is employed to avoid tedious computations incurred by the backstepping procedures. Furthermore, the closed-loop state errors are guaranteed to a small neighborhood around the equilibrium in finite time and evolved within SPP constraints although input saturations and actuator faults occur. Finally, comparative simulations are presented to demonstrate the feasibility and effectiveness of the proposed control scheme.

Reduced-Order Filters-Based Adaptive Backstepping Control for Perturbed Nonlinear Systems.

In this article, a robust adaptive output-feedback control approach is presented for a class of nonlinear output-feedback systems with parameter uncertainties and time-varying bounded disturbances. A reduced-order filter driven by control input is proposed to reconstruct unmeasured states. The state estimation error is shown to be bounded by dynamic signals driven by system output. The bound estimation technique is employed to estimate the unknown disturbance bound. Based on the backstepping design with three sets of tuning functions, an adaptive output-feedback control scheme with the flat-zone modification is proposed. It is shown that all the signals in the resulting closed-loop adaptive control systems are bounded, and the output tracking error converges to a prespecified small neighborhood of the origin. Two simulation examples are provided to illustrate the effectiveness and validity of the proposed approach.