This article addresses a collective heterogeneous multiagent pursuit-evasion (MPE) game problem where pursuers cooperatively capture escaping evaders. The analytical challenge of the present design lies in solving the associated coupled Hamilton-Jacobi-Isaacs (HJI) equations induced by the additional interacting roles in the MPE game while ensuring the achievement of the Nash equilibrium. To tackle this issue, a gaming framework is accordingly proposed to solve the coupled HJI equations. Sufficient conditions are derived to guarantee both the capturability and Nash equilibrium of the proposed collective MPE gaming scheme. Finally, numerical simulations are conducted to verify the effectiveness of the present MPE gaming strategy.

In this article, a heterogeneous multiagent pursuit-evasion (MPE) differential gaming problem is addressed. Therein, the pursuers aim to cooperatively capture the sole escaping evader. The theoretical analytical challenge lies in designing the Nash-minmax gaming strategy for such MPE games under directed graphs with external disturbances. Intra-alliance cooperation (pursuers and their peer neighbors) is regarded as a nonzero-sum game, while interalliance confrontation (multiple pursuers versus one evader) is treated as another zero-sum game. To tackle this issue, a hierarchical gaming framework is established to solve the associated coupled Hamilton-Jacobi-Isaacs (HJI) equation and accordingly derive the associated distributed gaming protocol. Finally, both numerical simulations and lake-based multi-autonomous surface vehicle (ASV) gaming experiments are conducted to verify the effectiveness of the present MPE gaming strategy.

Industrial circuit systems are subject to significant information uncertainties, such as noise, incomplete state measurements, unknown loads, and lumped disturbances, which can jeopardize stability and safety. This article addresses these challenges in stochastic port-Hamiltonian systems (SPHSs) through novel control strategies. A reduced-order observer, formulated via linear matrix inequalities, is developed to observe system states when complete measurements are unavailable. We prove that the observation error converges to zero both in almost sure and mean square senses. For SPHS with lumped disturbances, a disturbance observer enables feedforward compensation by observing unknown disturbances. In addition, tunable estimators are proposed to identify constant but unknown loads, with performance optimized through function selection. Furthermore, based on stochastic versions of LaSalle’s invariance principle and Barbalat’s lemma, we prove that a SPHS which is passive under constant control is also stabilizable via proportional–integral control. The efficacy of the proposed methods is demonstrated through circuit simulation examples, confirming their applicability in mitigating information uncertainties in industrial environments.

A hybrid Koopman deep learning algorithm is developed to predict the final synchronization of networked nonlinear dynamics with different topologies merely using neighboring state information. This algorithm introduces a nonlinear encoder as an observable function that maps the nonlinear state into a high-dimensional Hilbert space. By this means, a networked linear model is established to predict the future state of multiple transformed linear systems in the lifted space. Meanwhile, a nonlinear decoder is constructed, as the inverse of the lifting function, to retrieve the original nonlinear states. The virtue of the present algorithm lies in distilling and merging the linear features of multiple different topologies solely from the individual and/or neighboring state series. Therefore, the final synchronization states are calculated within the encoded linear space and subsequently decoded to recover the synchronization of the original nonlinear systems. Compared to most existing relevant algorithms that could only predict consensus values for linear networks, the present method could predict the final synchronization state of networked nonlinear dynamics with varying backbones. Sufficient conditions are derived to guarantee the prediction capability of the distributed final synchronization prediction (DFSP). Extensive numerical simulations verify its effectiveness.

Although significant progress has been made in coordinating multi-unmanned surface vehicle (multi-USV or USVs) systems over the past decades, consistent monitoring (or tracking) of such systems remains challenging as they do not share data with monitoring systems, further exacerbated by observed data inaccuracies and sparsity. To tackle the complicated issue, we hereby introduce the multi-unmanned aerial vehicle (multi-UAV or UAVs) system to monitor the multi-USV system. Therein, by introducing a sparse-Bayesian-learning-based (SBL-based) algorithm, the multi-UAV system can identify the potential coordinated dynamics of multi-USV system via only noisy and limited data. Then, by employing the Kalman filter (KF), the proposed approach can predict and update real-time data and optimize trajectory estimation for USVs, and enhance coordination control in the multi-UAV system to achieve coordinated monitoring. Finally, comparative simulations against the traditional control method, conducted under varying noise levels and data availability ratios, demonstrate the effectiveness and superiority of the proposed method.

Reconstructing dynamical networks based on time series of nodal states is of significant interest in many fields of science and engineering. Despite recent progress in network reconstruction, most research focuses on static structures, rather than on dynamic ones with unknown switching instants and number of subnetworks. Therefore, this paper develops a method for reconstructing switching networks, where a new sparse Bayesian learning algorithm is proposed to estimate switching instants. The proposed method is theoretically proved to be convergent. Experimental results are elaborated to demonstrate the effectiveness and superiority of the proposed method.

Periodical time-delay scenario is often encountered in industrial manufacturing processes. However, the presence of time delays and periodical coefficients brings challenges to controller design and system analysis, which thereby hinders the performance improvement of such systems. In this work, the dynamics of milling systems are transformed into a time-invariant finite-dimensional uncertain model described by Fourier series and Padé approximation. An adaptive active control law is accordingly designed to stabilize such complex dynamics. With the assistance of LaSalle–Yoshizawa theorem, conditions are derived to guarantee sufficiently large stability regions of the corresponding closed-loop system. A numerical case study is conducted on a standard two degrees of freedom milling perturbation system to substantiate the superiority of the proposed adaptive active control technique in terms of enlarged stable operational regions.

In this article, an optimal surrounding control algorithm is proposed for multiple unmanned surface vessels (USVs), in which actor-critic reinforcement learning (RL) is utilized to optimize the merging process. Specifically, the multiple-USV optimal surrounding control problem is first transformed into the Hamilton-Jacobi-Bellman (HJB) equation, which is difficult to solve due to its nonlinearity. An adaptive actor-critic RL control paradigm is then proposed to obtain the optimal surround strategy, wherein the Bellman residual error is utilized to construct the network update laws. Particularly, a virtual controller representing intermediate transitions and an actual controller operating on a dynamics model are employed as surrounding control solutions for second-order USVs; thus, optimal surrounding control of the USVs is guaranteed. In addition, the stability of the proposed controller is analyzed by means of Lyapunov theory functions. Finally, numerical simulation results demonstrate that the proposed actor-critic RL-based surrounding controller can achieve the surrounding objective while optimizing the evolution process and obtains 9.76% and 20.85% reduction in trajectory length and energy consumption compared with the existing controller.

Identifying structures of complex networks based on time series of nodal data is of considerable interest and significance in many fields of science and engineering. This article presents a sparse Bayesian learning (SBL) method for identifying structures of community-bridge networks, where nodes are grouped to form communities connected via bridges. Using the structural information of such networks with unknown nodal dynamics and community formations, network structure identification is tackled similar to sparse signal reconstruction with mixed sparsity mode. The proposed method is theoretically proved to be convergent. Its superiority to mainstream baselines is demonstrated via extensive experiments without the need for manual adjustment of regularization parameters.

Coordinated landing control for multiple unmanned aerial vehicles (UAVs) on appropriate multiple unmanned surface vehicles (USVs) is an urgent yet challenging mission with the tremendous development of modern marine industry. To this end, we propose a coordinated multiple UAV-USV landing control algorithm via heterogeneous-feature matching. Specifically, the heterogeneous landing features of different UAVs and USVs are extracted to establish a dynamic UAV-USV cooperative landing ability mapping for the cross-domain UAV-USV fleets (CDUUFs). Then, by incorporating suitable allocation with UAV-USV landing convergence and collision avoidance among UAVs into constraints with the assistance of both control Lyapunov functions (CLFs) and control barrier functions (CBFs), the multiple UAV-USV landing control problem is formulated as a constraint-based optimization one. Therein, slack variables are introduced to fulfill the assignment and facilitate the searching of a balanced solution between control performance and landing safety. Finally, extensive simulations are conducted to substantiate the effectiveness of the present multiple UAV-USV landing control law.

Learning dynamical networks based on time series of nodal states is of significant interest in systems science, computer science, and control engineering. Despite recent progress in network identification, most research focuses on static structures rather than switching ones. Therefore, this article develops a method for identifying the structures of switching networks by exploring and leveraging both temporal and spatial structural information that characterizes the switching process. The proposed method employs a new sparse Bayesian learning algorithm based on coupled hyperblocks to estimate unknown switching instants. Experimental results on benchmark artificial and real networks are elaborated to demonstrate the effectiveness and superiority of the proposed method.

Exploring the mechanism of hysteresis dynamics may facilitate the analysis and controller design to alleviate detrimental effects. Conventional models, such as the Bouc-Wen and Preisach models consist of complicated nonlinear structures, limiting the applications of hysteresis systems for high-speed and high-precision positioning, detection, execution, and other operations. In this article, a Bayesian Koopman (B-Koopman) learning algorithm is therefore developed to characterize hysteresis dynamics. Essentially, the proposed scheme establishes a simplified linear representation with time delay for hysteresis dynamics, where the properties of the original nonlinear system are preserved. Furthermore, model parameters are optimized via sparse Bayesian learning together with an iterative strategy, which simplifies the identification procedure and reduces modeling errors. Extensive experimental results on piezoelectric positioning are elaborated to substantiate the effectiveness and superiority of the proposed B-Koopman algorithm for learning hysteresis dynamics.

Data-based discovery of the underlying dynamics of nonlinear systems is of great importance to the prediction and control of engineering systems. This article presents a duplex neurodynamic learning (DNL) approach to the identification of discrete-time nonlinear systems subjected to both external disturbances and measurement noise. A neurodynamic learning method is proposed based on two-timescale recurrent neural networks (RNNs) for system identification. Truncated singular value decomposition is adopted to purify the data contaminated by external disturbances and measurement noises. Two RNNs are employed to cooperatively search for a global optimal solution, and the particle swarm optimization rule is used to reinitialize the RNNs upon the local convergence of the RNNs. The effectiveness and superiority of the proposed DNL method are demonstrated via simulations on benchmark chaotic and NARMAX systems.

This article presents a data-driven, Koopman operator-based modeling scheme for analyzing and predicting cross-coupling hysteresis effects of the piezoelectric tube scanners (PTSs) used in atomic force microscopes (AFMs). Such cross-coupling hysteresis effects between different PTS axes significantly reduce the positioning precision of AFMs. In contrast to most of the existing methods for PTS hysteresis, which involve complex nonlinear dynamics identification processes, the present study leverages the Koopman operator theory instead to treat the nonlinear hysteresis as a linear system. Therein, a Hankel extended dynamic mode decomposition (H-EDMD) algorithm is proposed to learn the finite-dimensional descriptions of the Koopman operator and the associated Koopman eigenspectrum. Moreover, the proposed H-EDMD even allows sparse sampling on the PTS systems, which is desirable in real industrial applications. Finally, extensive comparison experiments with a mainstream modified Prandtl-Ishlinskii model are conducted on an NTMDT Prima AFM to substantiate the effectiveness and superiority of the proposed H-EDMD method.

Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance. They have been widely used in biology, engineering, healthcare, etc. This article proposes BoostForest, which is an ensemble learning approach using BoostTree as base learners and can be used for both classification and regression. BoostTree constructs a tree model by gradient boosting. It increases the randomness (diversity) by drawing the cut-points randomly at node splitting. BoostForest further increases the randomness by bootstrapping the training data in constructing different BoostTrees. BoostForest generally outperformed four classical ensemble learning approaches (Random Forest, Extra-Trees, XGBoost and LightGBM) on 35 classification and regression datasets. Remarkably, BoostForest tunes its parameters by simply sampling them randomly from a parameter pool, which can be easily specified, and its ensemble learning framework can also be used to combine many other base learners.

Residential energy consumption continues to climb steadily, requiring intelligent energy management strategies to reduce power system pressures and residential electricity bills. However, it is challenging to design such strategies due to the random nature of electricity pricing, appliance demand, and user behavior. This article presents a novel reward shaping (RS)-based actor–critic deep reinforcement learning (ACDRL) algorithm to manage the residential energy consumption profile with limited information about the uncertain factors. Specifically, the interaction between the energy management center and various residential loads is modeled as a Markov decision process that provides a fundamental mathematical framework to represent the decision-making in situations where outcomes are partially random and partially influenced by the decision-maker control signals, in which the key elements containing the agent, environment, state, action, and reward are carefully designed, and the electricity price is considered as a stochastic variable. An RS-ACDRL algorithm is then developed, incorporating both the actor and critic network and an RS mechanism, to learn the optimal energy consumption schedules. Several case studies involving real-world data are conducted to evaluate the performance of the proposed algorithm. Numerical results demonstrate that the proposed algorithm outperforms state-of-the-art RL methods in terms of learning speed, solution optimality, and cost reduction.

In this article, we propose a distributed guiding-vector-field (DGVF) algorithm for a team of robots to form aspontaneous-orderingplatoon moving along a predefined desired path in the$n$-dimensional Euclidean space. Particularly, by adding a path parameter as an additional virtual coordinate to each robot, the DGVF algorithm can eliminate thesingular pointswhere the vector fields vanish, and govern robots to approach aclosedand evenself-intersectingdesired path. Then, the interactions among neighboring robots and a virtual target robot through their virtual coordinates enable the realization of the desired platoon; in particular, relative parametric displacements can be achieved with arbitrary ordering sequences. Rigorous analysis is provided to guarantee the global convergence of thespontaneous-orderingplatoon on the common desired path from any initial positions. Two-dimensional experiments using three HUSTER-0.3 unmanned surface vessels (USVs) are conducted to validate the practical effectiveness of the proposed DGVF algorithm, and 3-D numerical simulations are presented to demonstrate its effectiveness and robustness when tackling higher dimensional multirobot path-navigation missions and some robots breakdown.

This article considers quadruped locomotion control in the presence of uncertainties. Two types of structured uncertainties are considered, namely, uncertain friction constraints and uncertain model dynamics. Then, a min-max optimization model is formulated based on robust optimization, and a robust min-max model predictive controller is proposed by recurrently solving the optimization model. We prove that the min-max optimization model is equivalent to a convex quadratic constrained quadratic program by exploiting the structure of uncertainties. Moreover, a two-stage optimization algorithm is proposed to solve the optimization problem efficiently, allowing for the deployment of the controller onto the real robot. The results show that the proposed optimization algorithm can improve solving frequency by$\sim$11× compared with Gurobi. The proposed controller is able to stabilize quadruped locomotion in challenging scenarios where the uncertainties are caused by significant disturbances and unknown environments.

In this article, the consensus of constrained linear heterogeneous multiagent systems under prediction and optimization is investigated. By optimizing the consensus problems constrained to state equations and general linear constraints, two types of distributed analytical model predictive controllers are proposed. Furthermore, stability conditions for the two types of controllers are derived, in which a relationship between the communication topology and dynamics of heterogeneous agents is clarified. Simulation examples of networked heterogeneous agents illustrate the convergence and validity of the proposed controllers.

Imbalanced vibration is an urgent yet challenging problem in active magnetic bearings (AMBs) rotor manufacturing due to the rotor mass imbalance effect. The virtue of active control in AMB systems lies in enabling substantial online mitigation of imbalanced vibrations. However, in practice, due to the lack of speed sensors in most of the existing AMB-rotor systems, efficient rotational speed feedback is still on the way. As a remedy, this article proposes a rotational speed sensor-free synchronous signal processor (SSP) with the double inputs:$x$- and$y$-axes direction displacement measurements of radial AMBs. The proposed SSP is capable of estimating the rotational speed and accordingly generating synchronous signals of the imbalanced vibrations by filtering noise in both directions. Such signals are afterward implemented as a feedforward compensator for eliminating the periodical imbalance effects. With the assistance of the Lyapunov theory, the conditions of the proposed SSP method together with the feedforward imbalance compensator are derived to guarantee the stability of the closed-loop AMB-rotor system. Extensive experimental results substantiate the effectiveness and superiority of the proposed SSP method in terms of imbalanced vibration suppression.

This work studies the scaled consensus problems of exponentially unstable networked systems with time-varying input delays. First, a distributed consensus algorithm is designed under the truncated predictor feedback technique. Then, a delay bound is evaluated by utilizing the Lyapunov function-based method. Moreover, the consensus can be guaranteed when the upper bound of the time-varying input delay is no more than the delay bound. Furthermore, the relation between the delay bound and the network synchronizability is established. In other words, better network synchronizability implies a bigger delay bound. Finally, in the simulation examples, the feasibility of the theoretical results is verified, and the actual delay bounds are evaluated.

This article proposes a distributed cooperative manipulation control scheme for multirobot systems to track reference trajectories with unknown payload dynamics, grasp positions, and external disturbances. An online learning module is established to estimate the payload dynamics. Then a wrench-synthetic trajectory tracking control protocol is thereby developed to manipulate an object under unknown external disturbances no matter where the grasping points are. Moreover, sufficient conditions are derived to guarantee the uniform boundedness of the tracking errors of the closed-loop cooperative manipulation system. Finally, numerical simulations are conducted to substantiate the effectiveness of the proposed cooperative manipulation control scheme.

This article develops an identification algorithm for nonlinear systems. Specifically, the nonlinear system identification problem is formulated as a sparse recovery problem of a homogeneous variant searching for the sparsest vector in the null subspace. An augmented Lagrangian function is utilized to relax the nonconvex optimization. Thereafter, an algorithm based on the alternating direction method and a regularization technique is proposed to solve the sparse recovery problem. The convergence of the proposed algorithm can be guaranteed through theoretical analysis. Moreover, by the proposed sparse identification method, redundant terms in nonlinear functional forms are removed and the computational efficiency is thus substantially enhanced. Numerical simulations are presented to verify the effectiveness and superiority of the present algorithm.

For scenarios to pass through narrow and irregular channels, we develop a pragmatic distributed flexible formation protocol for multiple unmanned surface vessel systems (Multi-USVs). Therein, for path tracking with two leaders USVs, we propose a model predictive trajectory tracking scheme. Specifically, we design a scanning-formation controller with a trajectory state estimator for the followers to follow the leaders in order to efficiently fulfill the passing-through mission. Asymptotic stability conditions are derived to guarantee the feasibility of the developed multi-USV controller. Finally, both numerical simulations and experiments are conducted to show the effectiveness of the proposed multi-USV control strategies.

Regression in a sparse Bayesian learning (SBL) framework is usually formulated as a global optimization problem with a nonconvex objective function and solved in a majorization-minimization framework where the solution quality and consistency depend heavily on the initial values of the used algorithm. In view of the shortcomings, this article presents an SBL algorithm based on collaborative neurodynamic optimization (CNO) for searching global optimal solutions to the global optimization problem. The CNO system consists of a population of recurrent neural networks (RNNs) where each RNN is convergent to a local optimum to the global optimization problem. Reinitialized repetitively via particle swarm optimization with exchanged local optima information, the RNNs iteratively improve their searching performance until reaching global convergence. The proposed CNO-based SBL algorithm is almost surely convergent to a global optimal solution to the formulated global optimization problem. Two applications with experimental results on sparse signal reconstruction and partial differential equation identification are elaborated to substantiate the superiority and efficacy of the proposed method in terms of solution optimality and consistency.

As one of the most significant applications of nanopositioning technology, the tremendous development of atomic force microscopes (AFMs) has been witnessed these years. Essentially, the scanning motions of AFMs are generally driven by piezoelectric tube scanners (PTSs), whose cross-coupling effect hinders their high speed and high-precision positioning. Therefore, it becomes an urgent yet challenging mission to establish a niche model for the nonlinear cross-coupling dynamics of the PTS. As an emergent pure data-driven learning approach, the Koopman operator sheds some light on the PTS modeling methodology, which is thus adopted in this article to approximate the nonlinear cross-coupling dynamics of PTSs in an infinite-dimensional space. Moreover, an online high-order extended dynamic mode decomposition algorithm is proposed for the finite-dimensional approximation of the Koopman operator online. The merit of the present model lies in updating the identified cross coupling upon the arrival of new data in an incremental way. Finally, experiments are conducted to approximate theX–Yaxes cross coupling of PTSs of an NTMDT Prima AFM, which verifies the effectiveness and superiority of the proposed modeling algorithm. This article is expected to pave the way from the Koopman operator learning theory to real applications in dynamics modeling of abundant nanoscale measurement systems.

Sparse Bayesian learning (SBL) is a popular machine learning approach with a superior generalization capability due to the sparsity of its adopted model. However, it entails a matrix inversion at each iteration, hindering its practical applications with large-scale data sets. To overcome this bottleneck, we propose an efficient SBL algorithm with$\mathcal {O}(n^{2})$computational complexity per iteration based on a Gaussian-scale mixture prior model. By specifying two different hyperpriors, the proposed efficient SBL algorithm can meet two different requirements, such as high efficiency and high sparsity. A surrogate function is introduced herein to approximate the posterior density of model parameters and thereby to avoid matrix inversions. Using a data-dependent term, a joint cost function with separate penalty terms is reformulated in a joint space of model parameters and hyperparameters. The resulting nonconvex optimization problem is solved using a block coordinate descent method in a majorization–minimization framework. Finally, the results of extensive experiments for sparse signal recovery and sparse image reconstruction on benchmark problems are elaborated to substantiate the effectiveness and superiority of the proposed approach in terms of computational time and estimation error.

For portable devices with limited resources, it is often difficult to deploy deep networks due to the prohibitive computational overhead. Numerous approaches have been proposed to quantize weights and/or activations to speed up the inference. Loss-aware quantization has been proposed to directly formulate the impact of weight quantization on the model's final loss. However, we discover that, under certain circumstances, such a method may not converge and end up oscillating. To tackle this issue, we introduce a novel loss-aware quantization algorithm to efficiently compress deep networks with low bit-width model weights. We provide a more accurate estimation of gradients by leveraging the Taylor expansion to compensate for the quantization error, which leads to better convergence behavior. Our theoretical analysis indicates that the gradient mismatch issue can be fixed by the newly introduced quantization error compensation term. Experimental results for both linear models and convolutional networks verify the effectiveness of our proposed method.

Formation control finds broad applications in numerous fields, such as cooperative detection, surveillance, transportation, and disaster rescue. In this article, aiming at hunting a moving target, a two-stage surrounding control algorithm is proposed for linear multiagent systems subject to input saturations. An adaptive distributed observer is developed for each agent to reconstruct the target’s position. With the assistance of the algebraic graph theory and low gain feedback technique, distributed controllers are designed to drive the agents to encircle the moving target with a fixed radius and evenly distributed phase angles. Finally, both numerical simulations and experiments are conducted to verify the effectiveness of the proposed control algorithm.

Cooperative sweep coverage of multiagent systems (MASs) has found broad applications in various fields. This article proposes a scheme to address the sweep coverage problem of MASs within uncertain environments. In the proposed formulation, the coverage region is divided into multiple stripes, of which each has the workload completed by MASs in sequence. When the workload on the current stripe is completed, all the agents switch to the next together. The temporal dependence between the switching time computation and the sweep coverage operation is taken into account, and an online learning strategy is designed to handle environmental uncertainties and balance the workload among agents on the same stripe. Thereby, the distributed sweep coverage algorithm is developed to guarantee the complete sweep coverage, which consists of three operations, i.e., communication, workload partition, and sweeping. Theoretical analysis is afterward conducted to estimate the upper bound for the error between the actual and optimal coverage time. Finally, numerical simulations are carried out to substantiate the effectiveness and superiority of the proposed scheme.

Sparse representation has been widely applied to image classification, where the key issue is to extract a suitable discriminative dictionary. To this end, we propose a joint dictionary and classifier learning algorithm based on a parameterized Bayesian model. Therein, the Gaussian priors of a dictionary endow it with the capability of discrimination and representation. Moreover, we introduce a multivariate Gaussian prior for the sparse codes to achieve group sparsity, thereby substantially improving the classification performance. Furthermore, the sparse codes are estimated by a group-sparse Bayesian learning (GSBL) method, and the dictionary atoms are updated sequentially by maximizing a posterior. Moreover, to avoid manual parameter adjustment, the hyperparameters are optimized by an evidence maximization method. Accordingly, we develop a classification scheme via GSBL. Finally, extensive experiments are conducted on six benchmark datasets of face classification, object recognition, handwritten recognition, and scene categorization to substantiate the effectiveness and superiority of the proposed method.

This article presents a visual navigation and landing control paradigm for an unmanned aerial vehicle (UAV) to land on a moving autonomous surface vehicle (ASV). Therein, an adaptive learning navigation rule with a multilayer nested guidance is designed to pinpoint the position of the ASV and to guide and control the UAV to fulfill horizontal tracking and vertical descending in a narrow landing region of the ASV by means of merely relative position feedback. To ensure the feasibility of the proposed control law, asymptotical stability conditions are derived based on Lyapunov stability theory. Landing experimental results are reported for a UAV-ASV system consisting of an M-100 UAV and a self-developed three-meters-long HUSTER-30 ASV on a lake to substantiate the efficacy of the proposed landing control method.

This article investigates the observer-based non-negative scaled edge-consensus problems of a continues-time networked system with or without input saturation, in which the proposed algorithm only uses the neighboring edges’ outputs. First, the original networked model and preliminary algorithm are simplified by using the line graph theory and constructing a new variable. Then the specific mathematical formulations of feedback matrix and observer matrix are both derived by combining$H_{\infty }$control theory and low-gain feedback technique. Next, sufficient conditions which can guarantee the non-negative edge states and bounded control inputs are obtained. Moreover, the advantages of low-gain feedback technique for guaranteeing the non-negative edge states and designing the algorithm are developed. Finally, three cases are used to verify the validity of the theorems.

The complexity and randomness of the power system with distributed energy resources have led to the difficulties for fast optimal power flow (OPF) analysis. As a remedy, in this paper, we develop an interior point Powerball algorithm to accelerate the OPF solution process. To achieve better convergence characteristics, the proposed IPPB algorithm which is based on the Powerball optimization method, improves the search directions during iterative optimization by a nonlinear transformation. Also, a Newton—Raphson Powerball algorithm is derived for a faster power flow calculation, which is a basic yet critical part of the OPF problem. Numerical case studies are conducted on benchmark power systems with different scales to validate the proposed algorithms. Performances of the proposed algorithms to address improper initial points are studied by randomly picking the initial bus voltages. Numerical study results verify the feasibility and superiority of the proposed algorithms.

Effective supply chain management is a crucial competency for modern enterprises, but the issue has not been systemically addressed. To this end, we develop a distributed model predictive control (DMPC) approach with minimal information exchange and communication to handle supply chain operations and management. Therein, each decision maker relies on an agent with local information and they are collaborating to minimize a global cost function that measures the control performance of the entire network. The information flow topology is utilized to sequentially solve the DMPC optimization problem. The control sequence of downstream nodes is predicted with information transmitted to the upstream nodes. The stability of the proposed DMPC scheme is provably guaranteed. Finally, a numerical example is presented to verify the effectiveness of the proposed scheme.

Pneumatic muscle actuators (PMAs), a kind of soft/compliant actuators, have been attracted a great deal of attention in the studies of rehabilitation robots. However, the nonlinearities, uncertainties, hysteresis, and time-varying features of PMAs bring a lot of difficulties in their high-precision trajectory tracking tasks. In this paper, an echo state Gaussian process-based nonlinear model predictive control (ESGP-NMPC) is designed for the PMAs. The proposed strategy is comprised of an ESGP, which is suitable for modeling unknown nonlinear systems as well as measuring their uncertainties, and a gradient descent optimization algorithm for calculating the control signal sequences. Based on the Lyapunov theorem, characteristics of the closed-loop system are analyzed to guarantee the asymptotical stability. Both simulations and physical experiments are carried out to illustrate the validity of the proposed control strategy. Compared with other conventional methods, the ESGP-NMPC can achieve a better model fitting for the PMA and control performance for the high-precision tracking tasks. Note to Practitioners-High-precision control of pneumatic muscle actuators (PMAs) is a vital problem when PMAs are utilized as actuators of rehabilitation robots since the patient's safety and the performance of rehabilitation tasks are largely dependent on the accuracy of the actuators. Conventional model-based control approaches usually require relatively accurate identification of system parameters, which is difficult for the PMA, owing to its strong nonlinear and time-varying characteristics. This paper proposes a new model predictive control method based on an echo state Gaussian process that can describe the unknown dynamics of a PMA due to its universal approximation property. Through the optimization method, the controller can be efficiently realized and presents better performances than some comparatives. By applying this approach, it is possible to achieve not only high-precision control of PMAs but also a certain degree of robustness to the load.

The representation theorem for interval type-2 fuzzy sets (IT2 FSs), proposed by Mendel and John, states that an IT2 FS is a combination of all its embedded type-1 (T1) FSs, which can be nonconvex and/or subnormal. These nonconvex and/or subnormal embedded T1 FSs are included in developing many theoretical results for IT2 FSs, including uncertainty measures, the linguistic weighted averages (LWAs), the ordered LWAs (OLWAs), the linguistic weighted power means (LWPMs), etc. However, convex and normal T1 FSs are used in most fuzzy logic applications, particularly computing with words. In this paper, we propose a constrained representation theorem (CRT) for well-shaped IT2 FSs using only its convex and normal embedded T1 FSs, and show that IT2 FSs generated from three word encoding approaches and four computing with words engines (LWAs, OLWAs, LWPMs, and perceptual reasoning) are all well-shaped IT2 FSs. We also compute five constrained uncertainty measures (centroid, cardinality, fuzziness, variance, and skewness) for well-shaped IT2 FSs using the CRT. The CRT and the associated constrained uncertainty measures can be useful in computing with words, IT2 fuzzy logic system design using the principles of uncertainty, and measuring the similarity between two well-shaped IT2 FSs.

Given the input/output constraints and cross couplings of supply chain (SC) nodes, model predictive control (MPC) is efficient to seek the optimal solutions to the problems posed by interacting nodes to satisfy customer demands. In supply chain applications, due to the growing spatial distribution and interactions between the supply network elements, the information flow management becomes a challenging yet significant task. To reduce numerical complexity while maintaining implementability, a distributed MPC strategy is proposed. The scheme aims at finding the Nash equilibrium where the controller of each subsystem communicates with other ones in the presence of noncooperative interaction and strong coupled inputs due to the ordering decisions. Extensive numerical simulations verify that the strategy outperforms conventional policies in terms of substantially reduced SC operating cost.

The irregular and truncated probabilistic characteristics of wind power uncertainty lead to unknown influences on the power system operation. In this article, we propose a new probabilistic optimal power flow (POPF) framework, which can cope with such uncertainties, while taking into account the correlations among the wind generation power in multiple wind farms. A truncated multivariate Gaussian mixture model (Trun-MultiGMM) is designed to describe the irregular and multimodal wind power distributions with its typical truncation feature. Then an efficient Markov chain quasi-Monte-Carlo (MCQMC) sampler is developed to deliver wind power samples from the customized Trun-MultiGMM. Numerical simulations are conducted on the publicly available wind generation datasets and multiple benchmark power systems. The results have verified the effectiveness and efficiency of Trun-MultiGMM as well as the proposed POPF framework with MCQMC sampler.

Chatter is a typical kind of unstable dynamics often encountered in machining processes, which often results in overcut and rapid tool wear. Hence, chatter phenomenon worsens the surface quality and reduces productivity in milling systems as well. Recent years have witnessed a surging industrial demand of high quality and high efficiency machining. Specifically, for low radial immersion milling situation, large depth of cuts is inevitably needed so as to increase the machining efficiency. To fulfill such a task, this paper develops a robust active control method to mitigate the chatter dynamics of low radial immersion milling processes. The present approach increases the axial depth of cuts, and the improvement is inversely proportional to the radial immersion ratio. Finally, case studies are conducted to show the substantially enlarged stable region in the stability lobe diagram (SLD) spanned by spindle rotational speed and axial depth of cut. Thus, the method can be expected to improve the efficiency of milling processes.

High-order regularization in depth map super-resolution (SR) contributes to producing smoother depth map. However, assigning appropriate weights within regularization term is also important for preserving more detail information. In this paper, a novel and more adaptive depth SR model is proposed by using non-local total generalized variation (NLTGV) with classified weights. A random forest based classifier is trained to classify the pixels of depth map into four categories on the basis of several local structure features, such as gradient magnitude and texture energy extracted from color image, and then the weights within NLTGV are assigned with four groups of parameters corresponding to the four kinds of pixels. Evaluation results demonstrate that the local features make pixels have a good separability, and the classified weights can obviously improve the accuracy of depth map SR.

This paper investigates a group of multiagents moving on a 2-D plane with a constant speed but maneuverable headings. It is called a minimal control multiagent model (MCMA) when each agent employs a static decentralized control law that relies on its neighbors' relative positions with respect to its local reference frame. In other words, the control law does not involve any complicated velocity measurement or estimation mechanism. Various minimal multiagent models have been investigated and extensively simulated in terms of their collaborative behaviors. This paper, for the first time, gives rigorous theoretical proofs for the functionalities of an MCMA model in both collision avoidance and velocity alignment.

This paper develops a model predictive flocking control scheme for the Cucker-Smale multi-agent model. A decentralized controller is designed based only on neighboring measurements. Connectivity conditions are established for guaranteeing the convergence to a rigid flock. Finally, numerical simulation demonstrates the effectiveness of the control scheme.

In order to solve the problem of depth map super-resolution, this paper proposes a novel approach to obtain super-resolution result from the raw depth map captured by a 3D camera under a convex optimization framework. In our method, non-local total generalized variation (NL-TGV) is utilized to measure the smoothness of depth map, and the data-fidelity term is expressed by the Huber norm. To preserve the sharpness of depth discontinuities, the smoothing weights are decided by the combination of bilateral weight and separating probability between pixels, and the histogram distance between pixels in the raw depth map is used to adjust the combination weights for suppressing the texture-transfer. We have derived a numerical solution scheme for the optimization problem using the first order primal dual algorithm. Quantitative and qualitative evaluations on the synthesis datasets and the real datasets demonstrate that our method is as good as the state-of-the-art approaches.

For a multiagent system in free space, the agents are required to generate sufficiently large cohesive force for network connectivity preservation and sufficiently large repulsive force for collision avoidance. This paper gives an energy function based approach for estimating the control force in a general setting. In particular, the force estimated for network connectivity preservation and collision avoidance is separated from the force for other collective behavior of the agents. Moreover, the estimation approach is applied in three typical collective control scenarios including swarming, flocking, and flocking without velocity measurement.

A fastest consensus problem of topology fixed networks has been formulated as an optimal linear iteration problem and efficiently solved in the literature. Considering a kind of predictive mechanism, we show that the consensus evolution can be further accelerated while physically maintaining the network topology. The underlying mechanism is that an effective prediction is able to induce a network with a virtually denser topology. With this topology, an even faster consensus is expected to occur. The result is motivated by the predictive mechanism widely existing in natural systems.

The connectivity of a group of agents in a flocking scenario is caused either by individual's local cohesion interaction mechanism or by external boundary constraints. The latter case is particularly interesting when an individual's cohesion ability is not reliable due to the limitation of communication range. The effect of external boundary constraints on the connectivity property of multiagents has been intensively investigated in natural observation and engineering simulation. A theoretical analysis is given in this paper which reveals that a group of agents in a bounded plane can be almost always jointly connected and hence form a complete flock.

Precision control of piezoelectric motor nanopositioning stages is widely used in a variety of nano-manufacturing equipments. But due to the hysteresis nonlinearity with input saturation, it is challenging to design an ultrafast output feedback controller with large region of closed-loop stability. To address this problem, we developed a dual-mode nonlinear model predictive control (NMPC) method, in which an optimal input profile found by solving an open-loop optimal control problem drives the nonlinear system state into the terminal invariant set; afterwards a linear output-feedback controller steers the state to the origin asymptotically. In contrast to the classical output-feedback controller, the settling time is effectively decreased and the closed-loop stable region is substantially increased by the present NMPC with almost no loss of the nanopositioning accuracy. Finally, the feasibility and superiority of the proposed switching control method are examined by extensive experiments on a Physik Instrumente P-563.3CL triple-axis nanopositioning stage.

A novel individual-based alignmen/repulsion algorithm is proposed in this paper for a flock of multiple agents. With this algorithm, each individual repels its sufficiently close neighbors and aligns to the average velocity of its neighbors with moderate distances. In both mathematical analysis and numerical simulation, we prove that the algorithm guarantees an uncrowded flocking behavior with asymptotical velocity synchronization when sufficiently intensive communication exists within the agents. Moreover, we provide the conditions for collision avoidance along the whole transient procedure. The proposed flocking model has its references in natural collective behaviors like escaping panic and traffic jam motions.