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In this paper, we consider the problem of discrete-time, diffusion-based distributed parameter estimation with the agents connected via directed graphs with switching topologies and a self loop at each node. We show that, by incorporating a recently introduced dynamic regressor extension and mixing procedure to a classical gradient-descent algorithm, improved convergence properties can be achieved. In particular, it is shown that with this modification sufficient conditions for global convergence of all the estimators is that one of the sensors receives enough information to generate a consistent estimate and that this sensor is well-connected. The main feature of this result is that the excitation condition imposed on this distinguished sensor is strictly weaker than the classical persistent excitation requirement. The connectivity assumption is also very mild, requiring only that the union of the edges of all connectivity graphs over any time interval with an arbitrary but fixed length contains a spanning tree rooted at the information-rich node. In the case of non-switching topologies, this assumption is satisfied by strongly connected graphs, and not only by them

Clustering taxi data is commonly used to understand spatial patterns of urban mobility. In this paper, we propose a new clustering model called Origin-Destination-means (OD-means). OD-means is a hierarchical adaptive k-means algorithm based on origin-destination pairs. In the first layer of the hierarchy, the clusters are separated automatically based on the variation of the within-cluster distance of each cluster until convergence. The second layer of the hierarchy corresponds to the sub clustering process of small clusters based on the distance between the origin and destination of each cluster. The algorithm is tested on a large data set of taxi GPS data from Santiago, Chile, and compared to other clustering algorithms. In contrast to them, our proposed model is capable of detecting general and local travel patterns in the city due to its hierarchical structure.

This paper is the first to investigate both communication and control in traffic channel (TCH) and control channel (CCH) respectively when considering leader-follower formation keeping in UAV communication networks. In this paper, we analyze the relationship between the mutual interference and information exchange cost, and then formulate the joint channel and link selection problem as a two-way consensus game between CCH and TCH. To characterize the two-way choice of link selection, we creatively propose the generalized two-way consensus equilibrium (GTCE) to capture the stable state. Then, we prove that the formulated game has at least one pure-strategy GTCE which can maximize the UAV communication network utility. A distributed better reply based joint channel and link selection (BRJCLS) algorithm as well as two-dimensional minimum spanning tree (MST) based initialization (TMSTI) algorithm are proposed to achieve the GTCE. Simulation results are presented to show the convergence and effectiveness of the formulated two-way consensus game and proposed algorithms.

In this paper, event-triggered attitude consensus is considered for multi-agent systems with guaranteed fixed-time convergence. Due to the non-Euclidean property of the attitude configuration space, the attitude consensus is more challenging to achieve under the sampled-data setting. An event-triggered attitude protocol and event-triggered condition are proposed based on the axis-angle attitude representation. The fixed-time attitude consensus is reached if the initial attitudes lie in local regions on the attitude configuration space and Zeno-behavior can be avoided. The theoretical results reveal that the settling time is related to the inter-event interval and the algebraic connectivity of the topology graph. We further consider the consensus protocol under a jointly connected graph. The settling time estimation is also obtained in this case, which is dependent on the switching instants. Numerical simulations are conducted to verify the validity of the theoretical results finally.

How to evaluate Pareto front approximations generated by multi/many-objective optimizers is a critical issue in the field of multiobjective optimization. Currently, there exist two types of comprehensive quality indicators (i.e., volume-based and distance-based indicators). Distance-based indicators, such as Inverted Generational Distance (IGD), are usually computed by summing up the distance of each reference point to its nearest solution. Their high computational efficiency leads to their prevalence in many-objective optimization. However, in the existing distance-based indicators, the distributions of the solution sets are usually neglected, leading to their lacks of ability to well distinguish between different solution sets. This phenomenon may become even more severe in high-dimensional space. To address such an issue, a kernel-based indicator (KBI) is proposed as a comprehensive indicator. Different from other distance-based indicators, a kernel-based maximum mean discrepancy is adopted in KBI for directly measuring the difference that can characterize the convergence, spread and uniformity of two sets, i.e., the solution set and reference set, by embedding them in Reproducing Kernel Hilbert Space (RKHS). As a result, KBI not only reflects the distance between the solution set and the reference set, but also can reflect the distribution of the solution set itself. In addition, to maintain the desirable weak Pareto compliance property of KBI, a nondominated set reconstruction approach is also proposed to shift the original solution set. The detailed theoretical and experimental analysis of KBI is provided in this paper. The properties of KBI have also been analyzed by the optimal μ-distribution.

Aiming to addressing the nonrepetitive uncertainties of multiagent systems, this work proposes a discrete-time-distributed adaptive iterative learning control (DDAILC) scheme for an output consensus problem, where two fundamental requirements in the traditional distributed iterative learning control (ILC) methods, i.e., the identical initial states and the repetitive desired trajectories, are removed. Furthermore, the algorithm design and analysis are directly aimed at discrete-time nonlinear multiagent systems, rather than continuous-time ones, to meet the needs of practical implementations. The iteration-varying trajectory of the virtual leader is included in the learning control protocol for a compensation. The adaptive parameter-updating law works along the iteration dimension by using a general consensus error that contains the output data of adjacent agents. To ensure the estimation of the control gain to be nonzero, a semisaturator is utilized in the parameter-updating law. The convergence of the output consensus is shown rigorously. Both numerical and practical examples are used to test the theoretical results. Moreover, the DDAILC efficiently improves performance of the building heating, ventilation, and air conditioning (HVAC) system by utilizing both the distributed topology and the repetitive dynamic characteristic.

This paper considers the distributed structured optimization problem of collaboratively minimizing the global objective function composed of the sum of local cost functions. Each local objective function involves a Lipschitz-differentiable convex function, a nonsmooth convex function, and a linear composite nonsmooth convex function. For such problems, we derive the synchronous distributed primal-dual splitting (S-DPDS) algorithm with uncoordinated stepsizes. Meanwhile, we develop the asynchronous version of the algorithm in light of the randomized block-coordinate method (A-DPDS). Further, the convergence results show the relaxed range and concise form of the acceptable parameters, which indicates that the algorithms are conducive to the selection of parameters in practical applications. Finally, we demonstrate the efficiency of S-DPDS and A-DPDS algorithms by numerical experiments.

Pattern mining (PM) refers to the process of discovering patterns of interest to users from data. However, most studies have considered only one pattern, such as frequent pattern or high-utility pattern. With the continuous requirement of businesses in various industries, the single-objective PM methods are difficult to meet the increasingly diverse needs of users. In this paper, a multi-objective problem model for high quality pattern mining (HQPM) is proposed, where the objectives are support, occupancy, and utility. In order to solve the proposed three-objective problem efficiently, an improved multi-objective evolutionary algorithm for HQPM (MOEA-PM) is proposed. Two kinds of population initialization strategies are designed, which is used to ensure the population is effectively distributed in the feasible solution space. By taking the properties of the model into consideration, an auxiliary tool is proposed to accelerate the convergence of the algorithm. Experimental results on real-world datasets show that the proposed three-objective problem model with the MOEA-PM algorithm can discover patterns that are both frequently occurring and has a high utility in the transaction datasets, while at the same time being relatively complete. Compared with the state-of-the-art MOEA-based HQPM algorithms, MOEA-PM has better performance in terms of efficiency, quality, and convergence speed.

In this paper, a noisy output-based direct learning tracking control is proposed for stochastic linear systems with nonuniform trial lengths. The iteration-varying trial length is modeled using a Markov chain for demonstration of the iteration-dependence. The effect of the noisy output is asymptotically eliminated using a prior given decreasing gain sequence in the learning algorithm. Two alternative adaptive gains are presented for improving the tracking performance and the convergence speed. Both the mean square and almost sure convergence are provided. Numerical simulations on a 4 DoF robot arm are presented to illustrate the effectiveness of the proposed scheme.

In this article, an efficient method for finite-time secure dynamic state estimation in cyber-physical systems (CPSs) subjected to unknown inputs and cyber-attacks is proposed. The proposed approach is based on a set of local finite-time state estimators operating over the subsets of sensory nodes, which are designed to estimate the states of the CPS subjected to unknown inputs. When cyber-attacks compromise some sensory nodes, the estimation results of the local finite-time estimators which use the measurements of the attacked sensors can be corrupted. An efficient detection algorithm is thus proposed to identify the valid local estimators that are completely devoid of the attacked sensory nodes. The information of the valid local estimators is then used to achieve secure state estimation and localization of the launched cyber-attack. The necessary and sufficient conditions for the feasibility and finite-time convergence of the proposed estimation mechanism are analytically derived and proven. The effectiveness of the proposed method is demonstrated by testing it on a dc electric motor. Likewise, some online software-in-the-loop tests are conducted to demonstrate the real-time feasibility of the proposed algorithm.

Evolutionary multitasking, which solves multiple optimization tasks simultaneously, has gained increasing research attention in recent years. By utilizing the useful information from related tasks while solving the tasks concurrently, improved performance has been shown in various problems. Despite the success enjoyed by the existing evolutionary multitasking algorithms, still there is a lack of theoretical studies guaranteeing faster convergence compared to the conventional single task case. To analyze the effects of transferred information from related tasks, in this article, we first put forward a novel multitask gradient descent (MTGD) algorithm, which enhances the standard gradient descent updates with a multitask interaction term. The convergence of the resulting MTGD is derived. Furthermore, we present the first proof of faster convergence of MTGD relative to its single task counterpart. Utilizing MTGD, we formulate a gradient-free evolutionary multitasking algorithm called multitask evolution strategies (MTESs). Importantly, the single task evolution strategies (ESs) we utilize are shown to asymptotically approximate gradient descent and, hence, the faster convergence results derived for MTGD extend to the case of MTES as well. Numerical experiments comparing MTES with single task ES on synthetic benchmarks and practical optimization examples serve to substantiate our theoretical claim.

In this article, we present a novel matrix-factorization-based model, labeled here as Vicinal vertex allocated matrix factorization (VVAMo), for uncovering clusters in network data. Different from the past related efforts of network clustering, which consider the edge structure, vertex features, or both in their design, the proposed model includes the additional detail on vertex inclinations with respect to topology and features into the learning. In particular, by taking the latent preferences between vicinal vertices into consideration, VVAMo is then able to uncover network clusters composed of proximal vertices that share analogous inclinations, and correspondingly high structural and feature correlations. To ensure such clusters are effectively uncovered, we propose a unified likelihood function for VVAMo and derive an alternating algorithm for optimizing the proposed function. Subsequently, we provide the theoretical analysis of VVAMo, including the convergence proof and computational complexity analysis. To investigate the effectiveness of the proposed model, a comprehensive empirical study of VVAMo is conducted using extensive commonly used realistic network datasets. The results obtained show that VVAMo attained superior performances over existing classical and state-of-the-art approaches. AI Singapore National Research Foundation (NRF) This work was supported in part by the National Research Foundation, Singapore under its AI Singapore Programme (AISG) under Award AISG-RP-2018-004; and in part by the Data Science Artificial Intelligence Research Centre, Nanyang Technological University, and the Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU) that is funded by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant.

Solving optimization problems in multi-agent systems typically involves information exchange between agents using wireless networks with evolving topologies. The obtained solutions should be robust to possibly unbounded information delays and errors that affect the age-of-information of individual agents. In this note, we present a simple distributed gradient-based optimization framework and an associated algorithm. Convergence to a minimum of a given objective is shown under mild conditions on the network topology and objective. A key feature of our approach is that we merely assume that the messages sent reach the intended receiver, possibly delayed, with some positive probability. To the best of our knowledge, ours is the first analysis under such weak general network conditions. We also discuss in detail the verifiability of the assumptions involved. This note also makes a technical contribution in terms of the allowed class of objective functions. Specifically, we present an analysis wherein the objective function is such that its sample-gradient is merely locally Lipschitz continuous.

Value iteration is a fixed point iteration technique utilized to obtain the optimal value function and policy in a discounted reward Markov Decision Process (MDP). Here, a contraction operator is constructed and applied repeatedly to arrive at the optimal solution. Value iteration is a first order method and therefore it may take a large number of iterations to converge to the optimal solution. Successive relaxation is a popular technique that can be applied to solve a fixed point equation. It has been shown in the literature that, under a special structure of the MDP, successive over-relaxation technique computes the optimal value function faster than standard value iteration. In this work, we propose a second order value iteration procedure that is obtained by applying the Newton-Raphson method to the successive relaxation value iteration scheme. We prove the global convergence of our algorithm to the optimal solution asymptotically and show the second order convergence. Through experiments, we demonstrate the effectiveness of our proposed approach., Comment: Accepted for publication at IEEE Transactions on Automatic Control

Symmetric high-dimensional and sparse (SHiDS) networks are frequently found in various industrial applications. A symmetric non-negative latent factor (SNLF) model can acquire essential features from them precisely, yet it suffers from slow convergence. To address this issue, this paper integrates a generalized momentum method into a symmetric, single latent factor-dependent, non-negative and multiplicative update (S2LF-NMU) algorithm, thereby achieving a momentum-incorporated, symmetric, single-latent-factor-dependent non-negative-multiplicative-update (MS2N) algorithm. Based on an MS2N algorithm, momentum-incorporated symmetric non-negative latent factor (MSNLF) models are proposed for an SHiDS network, which ensures fast convergence as well as high representative learning ability. Empirical studies on four SHiDS networks from industrial applications demonstrate that compared with state-of-the-art models, the proposed MSNLF models have significantly higher computational efficiency and representative learning ability.

This article proposes a new discrete collision handling method (DCH method) that can be used for resolving tanglements in clothing simulation, based on the existing continuous collision handling methods (CCH methods). The proposed method performs intersection analysis of the clothing mesh at every time step, and stores the result in the form of coloring the vertices, edges, and triangles. Referring to the coloring, the method resolves the tanglements in the out-to-in manner by applying the proposed operations, the triangle shrinkage and vertex pull, to the triangles and vertices around the intersection path. We take note of the CCH methods that use some small tolerance value to defend the round-off errors for the purpose of preventing false negatives. This work gives a second thought to that tolerance value, and proposes a new DCH method which uses the tolerance value for the resolution purpose. Under certain conditions, the method turns out to guarantee resolution of the tanglements in a finite number of time steps.

Rescue assignments strategy are crucial for multiple Autonomous Underwater Vehicle (multi-AUV) systems in three dimensional (3-D) complex underwater environments. Considering the requirements of rescue missions, multi-AUV systems need to be cost-effective, fast-rescuing, and less concerned about the relationship between rescue missions. The real-time rescue plays a vital role in the multi-AUV system with the characteristics mentioned above. In this paper, we propose an efficient Reward acting on Reinforcement Learning and Particle Swarm Optimization (R-RLPSO), to provide a strategy of real-time rescue assignment for the multi-AUV system in the 3-D underwater environment. This strategy consists of the following three parts. Firstly, we present a reward-based real-time rescue assignment algorithm. Secondly, we propose an Attraction Rescue Area containing a Rescue Area. For the waypoints in each Attraction Rescue Area, the reward is calculated by a linear reward function. Thirdly, to speed up the convergence of the R-RLPSO and mark the rescue states of Attraction Rescue Area and rescue area, we develop a Reward Coefficient based on the reward of all Attraction Rescue Areas and Rescue Areas. Finally, simulation results show that the system based on R-RLPSO is more cost-effective and time-saving than that of based on comparison algorithms ISOM and IACO.

A secure distributed estimation method against data integrity attacks is presented for general Internet-of-Things (IoT) systems. By exploiting the spatial sparsity of the attacks, a robust optimal estimation objective to protect the entire IoT system is established. Then, a false data processor is developed to suppress the bad effects of the attacks. The convergence of the presented method is analyzed. It shows that, under accessible conditions, the estimation error will converge to be uniformly bounded for all cases, i.e., all communication links and transmitted data are arbitrarily compromised. Furthermore, by utilizing the property of persistence, a quantitative trust model is established for IoT systems based on the past behaviors of the nodes. Then, a trust-based distributed estimation method is proposed to enhance security. Simulation results by considering an IoT system with 50 nodes and 145 edges are also provided to verify our theoretical results.

This article studies the resilient control problem for a class of unknown nonlinear systems with fading measurements under malicious denial-of-service (DoS) attacks. The system output is assumed to be transmitted through a fading channel, where the fading phenomenon is described by a Rice fading model. The strategy of the attacker is to periodically interfere with the networked channels to reduce the success rate of data transmissions. First, a dynamic linearization method along the iteration domain is introduced to convert the nonlinear system into an equivalent data-related model. Then, a model-free adaptive iterative learning control (MFAILC) scheme is presented, which is independent of model information. The convergence of the MFAILC scheme is deduced theoretically and the influence of DoS attacks and stochastic fading phenomenon on system stability are also analyzed. Finally, the effectiveness of the design is verified by a numerical simulation and a trajectory tracking example of wheeled mobile robots (WMRs).

A novel method for aircraft conflict resolution in air traffic management (ATM) using physics informed deep reinforcement learning (RL) is proposed. The motivation is to integrate prior physics understanding and model in the learning algorithm to facilitate the optimal policy searching and to present human-explainable results for display and decision-making. First, the information of intruders' quantity, speeds, heading angles, and positions are integrated into an image using the solution space diagram (SSD), which is used in the ATM for conflict detection and mitigation. The SSD serves as the prior physics knowledge from the ATM domain which is the input features for learning. A convolution neural network is used with the SSD images for the deep reinforcement learning. Next, an actor-critic network is constructed to learn conflict resolution policy. Several numerical examples are used to illustrate the proposed methodology. Both discrete and continuous RL are explored using the proposed concept of physics informed learning. A detailed comparison and discussion of the proposed algorithm and classical RL-based conflict resolution is given. The proposed approach is able to handle arbitrary number of intruders and also shows faster convergence behavior due to the encoded prior physics understanding. In addition, the learned optimal policy is also beneficial for proper display to support decision-making. Several major conclusions and future work are presented based on the current investigation.

Multitasking optimization can achieve better performance than traditional single-tasking optimization by leveraging knowledge transfer between tasks. However, the current multitasking optimization algorithms suffer from some deficiencies. Particularly, on high similar problems, the existing algorithms might fail to take full advantage of knowledge transfer to accelerate the convergence of the search, or easily get trapped in the local optima. Whereas, on low similar problems, they tend to suffer from negative transfer, resulting in performance degradation. To solve these issues, this article proposes an evolutionary multitasking optimization algorithm for multiobjective/many-objective optimization with two-stage adaptive knowledge transfer based on population distribution. The resultant algorithm named EMT-PD can improve the convergence performance of the target optimization tasks based on the knowledge extracted from the probability model that reflects the search trend of the whole population. At the first stage of knowledge transfer, an adaptive weight is used to adjust the search step size of each individual, which can reduce the impact of negative transfer. At the second stage of knowledge transfer, the search range of each individual is further adjusted dynamically, which can improve the population diversity and be beneficial for jumping out of the local optima. Experimental results on multitasking multiobjective optimization test suites show that EMT-PD is superior to other state-of-the-art evolutionary multitasking/single-tasking algorithms. To further investigate the effectiveness of EMT-PD on many-objective optimization problems, a multitasking many-objective optimization test suite is also designed in this article. The experimental results on the new test suite also demonstrate the competitiveness of EMT-PD.

This paper deals with the LUT-less sensorless control of synchronous reluctance (SyR) machines at zero and low speed, where LUT-less stands for avoiding the use of flux-map look-up tables (LUTs) or other pre-determined machine parameters. The new signal-injection based control scheme called the Locus of Incremental Saliency ratio Tracking (LIST) is presented, where a pulsating high-frequency voltage component is used for position tracking and a second rotating signal-injection is dedicated to on-line estimating the incremental saliency ratio. A trajectory of constant incremental saliency ratio is used for torque regulation, resulting in stable control at all operating conditions, including overload. This despite the effect of cross-saturation, which is known to introduce position error and harm the control stability progressively with the load. The proposed scheme is validated experimentally on a 1.1 kW SyR machine test-bench. Alternative LUT-less torque control laws are investigated, and their stability limits put in evidence using convergence analysis and experiments.

Multi-target tracking (MTT) is an important component of situation-awareness based on the Internet of Things (IoT). Existing algorithms mainly focus on tracking based on conventional measurements, e.g., bearings or ranges. However, measurement parameter estimations are considered in isolation, limiting the accuracy and resolution of MTT, and the related data association is an NP-hard multi-dimensional assignment problem. In this paper, we develop a new one-step MTT algorithm based on a novel dynamic Bayesian network (DBN), i.e, DBNMTT. The new MTT algorithm directly infers target states from the raw measurement data by fusing the array signal model, the signal propagation model, and the motion model. In this new DBNMTT framework, we treat target states and conventional measurements such as bearings and target energies as hidden random variables. The posterior joint probability optimization problem is translated into the problem of graphical model learning. In this way, we can improve the accuracy and resolution of MTT and convert the NP-hard data association problem to a hidden variable learning problem. For non-conjugate models in the DBNMTT, we develop a novel reparameterized approximation variational inference (ReAVI) approach to solve the learning problem. The ReAVI converts non-conjugate models to conjugate models with new parameters and reuses the mean-field algorithm. The performance of our proposed new MTT method, namely DBNMTT-ReAVI, is analyzed on extensive simulations in challenging scenarios. The simulation results show that the DBNMTT-ReAVI algorithm is superior to conventional measurement based MTT algorithms in several aspects including the success probability, the convergence, the resolution, and the accuracy.

Reinforcement learning is an efficient, widely used machine learning technique that performs well in problems with a reasonable number of states and actions. This is rarely the case regarding control-related problems, as for instance controlling traffic signals, where the state space can be very large. One way to deal with the curse of dimensionality is to use generalization techniques such as function approximation. In this paper, a linear function approximation is used by traffic signal agents in a network of signalized intersections. Specifically, a true online SARSA(λ) algorithm with Fourier basis functions (TOS(λ)-FB) is employed. This method has the advantage of having convergence guarantees and error bounds, a drawback of non-linear function approximation. In order to evaluate TOS(λ)-FB, we perform experiments in variations of an isolated intersection scenario and a scenario of the city of Cottbus, Germany, with 22 signalized intersections, implemented in MATSim. We compare our results not only to fixed-time controllers, but also to a state-of-the-art rule-based adaptive method, showing that TOS(λ)-FB shows a performance that is highly superior to the fixed-time, while also being at least as efficient as the rule-based approach. For more than half of the intersections, our approach leads to less congestion and delay, without the need for the knowledge that underlies the rule-based approach.

In large-scale process industries, one of the great challenges is to achieve optimum operation of systems with multi-time-scale property and partially unknown models. To this end, this paper proposes a novel multi-rate layered OOC method, which employs lifting technique to unify the relatively fast dual-rate of basic loop layer and relatively slow single-rate of operational layer. Besides, by integrating model-based predictive control of basic loop layer with data-based actor-critic reinforcement learning (RL) of operational layer, it overcomes the difficulty of building the operational process dynamic model. The convergence of the proposed method is proved, and dense medium separation (DMS) process is taken as an application case to illustrate the effectiveness of our proposed method via a self-developed simulation platform.

In this article, the distributed finite-time and fixed-time optimization problems are investigated by adopting the zero-gradient-sum (ZGS) framework in multiagent systems. Specifically, when the local convex functions are nonquadratic, a basic optimization protocol is proposed to obtain a finite-time convergence, such that the networked system can cooperatively seek the optimal solution of the global objective, the sum of local objective, in a limited time. By utilizing the property of quadratic functions, a reduced algorithm can remove the dependence of initial conditions in the estimation of the upper bound of settling time and achieve a fixed-time result. Besides, the problem with time-varying topologies is studied by introducing a modified algorithm with an artificial potential function to preserve the network connectivity. Finally, the validity of the protocols is demonstrated via some example simulations.

There is insufficient current understanding of how to apply fully decentralized control to networks of sparsely coupled nonlinear dynamical subsystems subject to noise to track a desired state. As exemplars, this class of problem is motivated by practical requirements of creating decentralized power grids robust to cascade failures, the digital transformation of Industry 4.0 managing IoT connectivity reliably, and controlling transport flow in smart cities by computing at the edge. We demonstrate that an approach utilizing probability theory to characterize and exploit the uncertainty in locally received information, and locally optimized messages passed between neighboring subsystems is sufficient to implicitly infer global knowledge. Thus, control of a global state could be realized through decentralized control signals applied only to local subsystems using only local information without any reference to a global current state. Given a global system that can be decomposed into a set of locally coupled subsystems, we develop a theoretical method of probabilistic message passing and probabilistic control signals all interacting only at the subsystem level, but which promotes a system-wide convergence to a desired state. Our theoretical results are corroborated using computational experiments on a network of a 10-node partially coupled system decomposed into four separated subsystems with control inputs applied and determined at the subsystem level. Comparing the results with a centralized control method utilizing information from all the nodes to achieve global state convergence validates our hypothesis that local decentralized probabilistic control can be affected by the mechanism of local probabilistic message passing without needing access to global centralized information. We also provide a set of numerical experiments increasing the network size showing that the decentralized algorithm is independent of the global system size.

Fuzzy neural network (FNN) is an effective learning system that combines neural network and fuzzy logic, which has achieved great success in nonlinear system modeling. However, when the input is practical-complex data with external disturbance, the existing FNN cannot extract effective input-features sufficiently, leading to unsatisfactory performances in learning speed and accuracy. It also fails to achieve a better generalization capability because of its fixed structure size (the number of rule neurons). In this work, an efficient self-organizing FNN (SOFNN) with incremental deep pre-training (IDPT), called IDPT-SOFNN, is developed to overcome these shortcomings. First, IDPT is designed to extract effective features and consider them as the input of SOFNN. Different from the existing pre-training, the self-growing structure of IDPT improves pre-training efficiency with a more compact structure. Second, SOFNN can dynamically add and delete neurons according to the current error and error-reduction rate. In this case, it can obtain better modeling performance with a more compact structure as well. Third, as a novel hybrid model with the cascade dual-self-organizing algorithm, IDPT-SOFNN combines the advantage of IDPT and SOFNN. Moreover, the convergence and stability are analyzed. Finally, simulation studies and comparisons demonstrate that the proposed IDPT-SOFNN has better performances than its peers in learning speed, accuracy and generalization capability.

Multi-agent systems (MASs) can autonomously learn to solve previously unknown tasks by means of each agent's individual intelligence as well as by collaborating and exploiting collective intelligence. This article considers a group of autonomous agents learning to track the same given reference trajectory in a possibly small number of trials. We propose a novel collective learning control method that combines iterative learning control (ILC) with a collective update strategy. We derive conditions for desirable convergence properties of such systems. We show that the proposed method allows the collective to combine the advantages of the agents' individual learning strategies and thereby overcomes trade-offs and limitations of single-agent ILC. This benefit is achieved by designing a heterogeneous collective, i.e., a different learning law is assigned to each agent. All theoretical results are confirmed in simulations and experiments with two-wheeled-inverted-pendulum robots (TWIPRs) that jointly learn to perform the desired maneuver.

Most recent studies on adaptive hydraulic tracking control focus on trajectory tracking performance while the parameter convergence property is often unsatisfying. This paper proposes a composite learning adaptive position tracking controller with improved parameter convergence for electro-hydraulic servo systems. In the composite learning, a prediction error is formulated to exploit input-output memory data, and parameter estimates are driven simultaneously by tracking and prediction errors. Practical exponential stability of the closed-loop system, which implies the convergence of both the tracking and parameter estimation error, is established by a more realizable interval-excitation condition than the stringent persistent-excitation condition. Therefore, superior trajectory tracking is obtained compared with the classical adaptive hydraulic control. Besides, the initial fluid control volumes of hydraulic systems are assumed to be unknown a priori, which enhances the generality of the proposed control approach. The above two properties are generally not achievable in prevalent approaches to adaptive hydraulic control. Moreover, noisy acceleration signals and the time derivatives of pressure signals are not needed in the proposed approach, which improves its robustness against measurement noise. Extensive experimental results verify its superiority over currently available ones.

Fuzzy clustering is one of the most popular clustering approaches and has attracted considerable attention in many fields. However, high computational cost has become a bottleneck which limits its applications in large-scale problems. Moreover, most fuzzy clustering algorithms are sensitive to noise. To address these issues, a novel fuzzy clustering algorithm, called Fast Fuzzy Clustering based on Anchor Graph (FFCAG), is proposed. The FFCAG algorithm integrates anchor-based similarity graph construction and membership matrix learning into a unified framework, such that the prior knowledge of anchors can be further utilized to improve clustering performance. Specifically, FFCAG first constructs an anchor-based similarity graph with a parameter-free neighbor assignment strategy. Then it designs a quadratic programming model to learn the membership matrix of anchors, which is very different from traditional fuzzy clustering algorithms. More importantly, a novel balanced regularization term is introduced into the objective function to produce more accurate clustering results. Finally, we adopt an alternating optimization algorithm with guaranteed convergence to solve the proposed method. Experimental results performed on synthetic and real-world datasets demonstrate the proposed FFCAG can significantly reduce the computational time with comparable even superior clustering performance, compared with state-of-the-art algorithms.

International audience; This paper deals with an observer-based identification framework to estimate both lateral dynamics states and tires' cornering parameters in the perspective of designing advanced rider assistance systems for powered two-wheeled vehicle. An adaptive observer is proposed to reconstruct the state variables regardless of the forward velocity variations and to estimate the real unknown tires' parameters. The stability and convergence analysis of the proposed observer is based on the Lyapunov theory, the persistency of excitation and the general Lipschitz condition. To enable this observer design, the linear parameter varying observer is transformed into Takagi-Sugeno exact form where the sufficient conditions are given in terms of linear matrix inequalities. Finally, an evaluation framework is proposed to provide a critical overview of the method's effectiveness. The proposed adaptive law is compared to direct estimation and dynamic inversion estimation methods. Co-simulation scenarios are performed by using both BikeSim© motorcycle simulator and real data-log obtained from an instrumented electrical scooter.

Renewable Energy Sources (RESs) integration strengthens the need for a power network that can robustly handle the systems uncertain scenarios. This paper proposes the first nonlinear novel dual-based bi-level approach for robust AC Transmission Expansion Planning (TEP) with uncertainties in RES generations and loads. It utilizes a convex relaxation and is solved by Benders Decomposition (BD), where the master determines the robust AC expansion plan. The novel dual slave for the second level of BD circumvents the issues in using conventional conic dual theory and aids in the worst-case realization of uncertainties using additional novel constraints. The proposed slave is solved using Interior Point Method (IPM), as it is not a mixed-integer problem. The proposed work includes additional linear constraints to speed up BDs convergence and direct the master towards optimality. The approach is tested on several transmission systems, including the Indian Northern Regional Power Grid (NRPG) system. The robustness of the obtained plans is verified by Monte-Carlo Simulation (MCS) of the actual nonlinear and nonconvex AC Optimal Power Flow (OPF). The effect of the budget of uncertainty on the AC expansion plan is also investigated. A comparison with two previous works reveals the efficacy of the proposed approach.

In this note, we provide an overarching analysis of primal-dual dynamics associated to linear equality-constrained optimization problems using contraction analysis. For the well-known standard version of the problem: we establish convergence under convexity and the contracting rate under strong convexity. Then, for a canonical distributed optimization problem, we use partial contractivity to establish global exponential convergence of its primal-dual dynamics. As an application, we propose a new distributed solver for the least-squares problem with the same convergence guarantees. Finally, for time-varying versions of both centralized and distributed primal-dual dynamics, we exploit their contractive nature to establish bounds on their tracking error. To support our analyses, we introduce novel results on contraction theory.

In this paper, the output feedback based direct model reference adaptive control of piecewise affine systems and its parameter convergence are investigated. Under the slow switching assumption, it is shown that all the closed-loop signals are bounded and the output tracking error is small in the mean square sense. Built upon this result, the estimation error of controller parameters is proved to converge to a residual set if the input signal is sufficiently rich. The relationship between the size of this residual set and the switching frequency is established. Moreover, the convergence of the estimated controller parameters to their nominal values can be achieved for a certain subsystem given that this subsystem is activated for infinitely long time. Simulation results validate the effectiveness of the proposed approach.

Vehicle speed is known as one of the most important parameters in the various goals for driving. Although the most critical goal of driving is safety, each driver can consider another secondary goal, such as reducing travel time, economical driving, green driving, passenger comfort, etc. In this paper, we propose a smartphone-based application that utilizes the cloud/fog computing service infrastructure in order to recommend speed according to primary safety goal and another secondary objective. Due to the strategic nature of the driving speed selection issue, we have modeled the problem as a game that the drivers are the players, and the speed of the vehicle is their strategy. We solve the proposed game via evolutionary dynamics with appropriate convergence time, and the resulting equilibrium profile is announced to the drivers as the recommended speed at specified time intervals. Finally, the experimental results obtained from the simulation of the proposed scheme confirm that the deviation from the proposed equilibrium velocity is not profitable for the offending player. Indeed, deviation from equilibrium conditions leads to the negative impacts on the formal evaluation parameters presented in this paper.

In the last decade, fuzzy community detection has received increasing attention, since it} can not only uncover community structure of a network, but also reflect the membership degrees of each node to multiple communities. Although some pioneers proposed a few algorithms for finding fuzzy communities, there is still room for further improvement in the quality of detected fuzzy communities. In this study, a metaheuristic-based modularity optimization algorithm, named Symbiotic Organisms Search Fuzzy Community Detection (SOSFCD) is proposed. On the one hand, an improved bio-inspired metaheuristic algorithm, Elite Symbiotic Organisms Search (Elite-SOS), is designed as optimization strategy to improve the global convergence of fuzzy modularity optimization. On the other hand, a Neighbor-based Membership Modification (NMM) operation is proposed to intensify exploitation ability and speed up convergence, by efficiently utilizing local information (i.e., node neighborhood) of network topology. Experimental results on both of synthetic and real-life networks with different scales and characteristics show that SOSFCD can find max-modularity fuzzy partitions and coverings, which outperforms many state-of-the-art algorithms in terms of accuracy and stability.

Many problems in industrial smart manufacturing, such as process operational optimization and decision-making, can be regarded as distributed non-convex optimization problems, whose goal is to utilize distributed nodes to cooperatively search for the minimal value of the global objective function. Considering data transmission mode, transmission condition and communication waste in industrial applications, it is meaningful to study the distributed non-convex optimization problem with an event-triggered strategy over time-varying directed networks. To solve such a problem, a distributed non-convex event-triggered algorithm is proposed in this paper. Under some assumptions on local objective functions, gradients and step-sizes, the convergence of the proposed event-triggered algorithm to the local minimum is proved. Moreover, it is obtained that the proposed distributed event-triggered algorithm has a convergence rate of O(1/ln(t)). Finally, two examples in industrial systems are provided to validate the effectiveness of the proposed algorithm.

Multiview subspace clustering has been demonstrated to achieve excellent performance in practice by exploiting multiview complementary information. One of the strategies used in most existing methods is to learn a shared self-expressiveness coefficient matrix for all the view data. Different from such a strategy, this article proposes a rank consistency induced multiview subspace clustering model to pursue a consistent low-rank structure among view-specific self-expressiveness coefficient matrices. To facilitate a practical model, we parameterize the low-rank structure on all self-expressiveness coefficient matrices through the tri-factorization along with orthogonal constraints. This specification ensures that self-expressiveness coefficient matrices of different views have the same rank to effectively promote structural consistency across multiviews. Such a model can learn a consistent subspace structure and fully exploit the complementary information from the view-specific self-expressiveness coefficient matrices, simultaneously. The proposed model is formulated as a nonconvex optimization problem. An efficient optimization algorithm with guaranteed convergence under mild conditions is proposed. Extensive experiments on several benchmark databases demonstrate the advantage of the proposed model over the state-of-the-art multiview clustering approaches.

We consider a robust state space filtering problem in the case that the transition probability density is unknown and possibly degenerate. The resulting robust filter has a Kalman-like structure and solves a minimax game: the nature selects the least favorable model in a prescribed ambiguity set which also contains non-Gaussian probability densities, while the other player designs the optimum filter for the least favorable model. It turns out that the resulting robust filter is characterized by a Riccati-like iteration evolving on the cone of the positive semidefinite matrices. Moreover, we study the convergence of such iteration in the case that the nominal model is with constant parameters on the basis of the contraction analysis in the same spirit of Bougerol. Finally, some numerical examples show that the proposed filter outperforms the standard Kalman filter.

Neighbor discovery (ND) is a key initial step of network configuration and prerequisite of vehicular ad hoc network (VANET). However, the convergence efficiency of ND is facing the requirements of multi-vehicle fast networking of VANET with frequent topology changes. This paper proposes gossip-based information dissemination and sensing information assisted ND with multi-packet reception (GSIM-ND) algorithm for VANET. GSIM-ND algorithm leverages efficient gossip-based information dissemination in the case of multi-packet reception (MPR). Besides, through multi-target detection function of multiple sensors installed in roadside unit (RSU), RSU can sense the distribution of vehicles and help vehicles to obtain the distribution of their neighbors. Thus, GSIM-ND algorithm leverages the dissemination of sensing information as well. The expected number of discovered neighbors within a given period is theoretically derived and used as the critical metric to evaluate the performance of GSIM-ND algorithm. The expected bounds of the number of time slots when a given number of neighbors needs to be discovered is derived as well. The simulation results verify the correctness of theoretical derivation. It is discovered that GSIM-ND algorithm proposed in this paper can always reach the short-term convergence quickly. Moreover, GSIM-ND algorithm is more efficient and stable compared with completely random algorithm (CRA), scan-based algorithm (SBA) and gossip-based algorithm. The convergence time of GSIM-ND algorithm is 40%-90% lower than that of these existing algorithms for both low density and high density networks. Thus, GSIM-ND can improve the efficiency of ND algorithm.

Flexible joint robot (FJR) manipulators can offer many attractive features over rigid manipulators, including light weight, safe operation, and high power efficiency. However, the tracking control of the FJR is challenging due to its inherent problems, such as underactuation, coupling, nonlinearities, uncertainties, and unknown external disturbances. In this article, a terminal sliding mode control (TSMC) is proposed for the FJR system to guarantee the finite-time convergence of the systems output, and to achieve the total robustness against the lumped disturbance and estimation error. By using two coordinate transformations, the FJR dynamics is turned into a canonical form. A cascaded finite-time sliding mode observer (CFTSMO) is constructed to estimate states and lumped disturbance in a finite time based on two measurable states, which not only attenuates the measurement noise but also reduces the peaking phenomenon. The closed-loop stability and the finite-time convergence are rigorously proved by using Lyapunov theorem. The upper bound of the finite convergence time is derived for the reaching and sliding phase. Comparative study is conducted experimentally in real time on the FJR manipulator to verify the effectiveness of the proposed control method.

In this article, a new learning-based time-varying impedance controller is proposed and tested to facilitate an autonomous physical human-robot interaction (pHRI). Novel adaptation laws are formulated for online adjustment of robot impedance based on human behavior. Two other sets of update rules are defined for intelligent coping with the robot's structured and unstructured uncertainties. These rules ensure stability via Lyapunov's theorem and provide uniform ultimate boundedness (UUB) of the closed-loop system's response, without a need for HRI force/torque measurement. Accordingly, the convergence of response signals, including errors in tracking, online impedance learning, robot parameter adaptation, and controller gain variation, is proven to operate in a bounded region (compact set) in the presence of robot and human uncertainties and bounded disturbances. The performance of the developed intelligent impedance-varying control strategy is investigated through comprehensive experimental studies in a repetitive following task with a moving target.

This study addresses a finite-time prescribed performance controller within the concise fuzzy-neural framework with application to a waverider aircraft. Firstly, new finite-time performance functions are developed to construct a constraint funnel which accomplishes that tracking errors converge to their steady-state values in a given time (i.e., finite time convergence), being expected to guarantee tracking errors with small overshoots. Then, the equivalent transformation approach is introduced to unify unknown dynamics such that the control complexity is reduced. Moreover, to further reduce computational costs, a single-learning-parameter-based regulation scheme is developed for fuzzy-neural approximation. Finally, the proposed method is applied to a waverider aircraft to test its effectiveness and superiority.

The attitude synchronization problem for multiple spacecraft with angular velocity and input constraints is investigated in this article, under the mild assumptions that the leader's states can reach each follower through a path and the communications between followers are bidirectional. An adaptive finite-time distributed observer is designed to estimate the leader's states for each follower spacecraft, which has the superiority in the fully distributed that the communication topology or some other global information is not required, and the finite-time convergence can be guaranteed. Furthermore, the novel auxiliary systems are first proposed to avoid the angular velocity and input constraints being violated, while the finite-time command filtered backstepping controller is designed to track the leader's attitude motion. The remarkable features of the developed algorithm are the global finite-time attitude consensus of multiple spacecraft, with the constraints satisfied. Moreover, the efficiency of the proposed method is illustrated by numerical simulations and real-time platform verification.

This study solves the attitude stabilization problem of 2-degree-of-freedom (2-DOF) helicopters by robustly assigning the variable-performance to the closed-loop system in the presence of the model-plant mismatches and load variations. The proposed approach re-formulates the original problem to a surface stabilization one which yields three contributions: a) the proposed attitude velocity observer forms a conventional Luenberger-type observer incorporating the parameter-independence and estimation gains specifically designed for causing the pole-zero cancellation, b) the pole-zero cancellation velocity control loop makes it possible to accomplish the surface stabilization task with the first-order closed-loop order, and c) the closed-loop analysis results demonstrate the attractive properties of the exponential performance recovery and convergence. The effectiveness of the proposed technique is verified using the experimental platform provided by the Quanser AERO.

In this article, we investigate the distributed Nash equilibrium (NE) seeking problem for the multi-cluster game under switching communication topologies. Specifically, the communication topology switches between a group of jointly connected digraphs. First, a new distributed NE seeking algorithm for the multi-cluster games is designed by the consensus protocol and gradient play rule under the switching communication topologies. Furthermore, in order to make the algorithm still applicable when the agent only knows part of the decision information, the leader-following consensus protocol is used to generate the estimates for all agents action in the cluster under the assumption that the switching topology between clusters is directed and strongly connected. A more general NE seeking algorithm for the multi-cluster games is designed. For these two algorithms, the results of local convergence and non-local convergence are given, respectively. Two examples verify the validity of the theoretical results.

To guarantee vehicle platoon driven pattern in heterogeneous nodes of mixed vehicle platoon (composed of connected and automated vehicles and human-driven vehicles, CAVs and HVs) on curved roads, this study develops a combined longitudinal and lateral controller, which comprises of selecting the key points (KPs) from the trajectory points of detected HVs, correcting the reference trajectory and controlling CAVs with the aid of the corrected KPs. To this end, a new concept, called KPs matrix, is proposed to manage the physical components of every KP by using image processing and vehicle-to-infrastructure (V2I) communication technology. Then, a trajectory correction scheme is presented to suppress the influence of nonstandard human-driven behavior by point set mapping approach in Real Variable Function theory. Furthermore, a novel controller is designed by incorporating the corrected KPs matrix and communication time delay. The stability and convergence of the proposed controller are rigorously analyzed based on the Lyapunov-Krasovskii stability theorem. In addition, extensive experiments are conducted to test the performance including three parts: the first part investigates the feasibility of the corrected KPs matrix by analyzing a video on high-way; the next part illustrates the control performance of the proposed controller on handling the cutting-corner issue (i.e. turning in advance), compared with the conventional controller. Meanwhile, the influence of time delay on the control performance is also analyzed in this study. The last implements driver-in-loop comparative experiments such that the performance of the proposed controller on eliminating the influence of nonstandard human-driven behavior is verified.

In this paper, an online optimization control (OPC) scheme is proposed for the angle control of MEMS scanning micro-mirror actuated via electromagnetic method. In order to avoid complicated process of building precise model of the MEMS micro-mirror, a control structure not relying on precise model is developed. In this structure, a simplified linear dynamic model is used to describe the basic dynamic behavior of the micro-mirror. Then, a model error compensation mechanism (MECM) is introduced to compensate for the model uncertainty caused by un-modelled dynamics and nonlinear factors, such as hysteresis. Moreover, a predictive gradient descent (PGD) based optimization mechanism is proposed to compensate the effect of time-delay and speed up the convergence of on-line optimization process. Then, the stability analysis of the proposed control system is implemented. Finally, the proposed control scheme is applied to the angle control of electromagnetically driven scanning micro-mirror (EDSMM) and the corresponding experimental results are presented.

Reinforcement learning has gradually demonstrated its decision-making ability in autonomous driving. Reinforcement learning is learning how to map states to actions by interacting with environment so as to maximize the long-term reward. Within limited interactions, the learner will get a suitable driving policy according to the designed reward function. However there will be a lot of unsafe behaviors during training in traditional reinforcement learning. This paper proposes a RL-based method combined with RL agent and Monte Carlo tree search algorithm to reduce unsafe behaviors. The proposed safe reinforcement learning framework mainly consists of two modules: risk state estimation module and safe policy search module. Once the future state will be risky calculated by the risk state estimation module using current state information and the action outputted by the RL agent, the MCTS based safe policy search module will activate to guarantee a safer exploration by adding an additional reward for risk actions. We test the approach in several random overtake scenarios, resulting in faster convergence and safer behaviors compared to traditional reinforcement learning.