The myopic best-response adjustment is a strategy updating rule frequently employed in evolutionary congestion games. Adopting this rule, however, may need precise strategy information of players. In this paper, a novel strategy updating rule is proposed by combining the classical best-response adjustment and a designed time-varying inertia. In the proposed rule, we first consider a prediction mechanism that utilizes the frequency of selected resources rather than players’ individual strategies, derived from any given limited-length historical information on past stages, to predict the congestion vector for the next stage. Furthermore, we consider an inertia-based best-response dynamics for the players with time-varying inertia based on the predicted congestion vector so that the players can either jump to the corresponding likely best-response strategy or keep the previous strategy in the next stage. The proposed rule guarantees almost sure convergence to the set of Nash equilibria where the convergence can be examined with the value of inertia. A server allocation problem of network functions virtualization (NFV) is used as the numerical example to verify the validity of the results.

This paper focuses on the design of controller for trajectory tracking and obstacle avoidance in robot formations operating within confined spaces. Specifically, to achieve obstacle avoidance, we approximate obstacles and robots as convex polyhedra and utilize the Lagrangian dual function to design an enhanced discrete-time control barrier function (DCBF). Moreover, a discrete-time control Lyapunov function (DCLF) algorithm is employed to maintain the formation while tracking a predefined trajectory. This enables the natural unification of DCBFs with DCLFs within a quadratic program (QP) framework. The proposed method eliminates the nested optimization problem in the QP framework, and the solving time is significantly reduced. Theoretical analysis demonstrates the feasibility of the proposed method. Finally, simulations and hardware experiments are conducted to validate its effectiveness.

In practical UAV with slung load systems, the suspension point often deviates from the UAV’s center of gravity due to structural limitations or operation requirement. This offset configuration disrupts the vehicle’s original structural symmetry and creates nonlinear coupling dynamics which leads to a complicated motion control problem. Conventionally, UAV systems are usually modeled and controlled from the perspective with its reference frame originating at the UAV’s center of gravity. Unlike existing approaches, this work introduces a new modeling and control framework by shifting the reference point from the UAV’s center of gravity to the connection point. The proposed dynamic model reveals that the load’s motion is directly driven by the acceleration of the UAV’s suspension point. A model-based control system is developed, comprising a decoupler, a mixer, and immersion-and-invariance adaptive control laws. An acceleration control law of the suspension point is proposed to actively control the velocity and swing angle of the slung load. Simultaneously, an inner-loop control torque and a mixer are developed based on the off-center frame to solve the coupling between the UAV and slung load attitude dynamics. The immersion-and-invariance adaptive laws are designed to estimate and compensate for external disturbances. Furthermore, a flexible gain function is introduced to improve the flexibility in shaping the control law, thereby achieving the potential for enhanced robustness and faster convergence of both the system states and parameter estimates compared with conventional control laws with linear gains. Finally, real-world flight experiments involving velocity tracking and disturbance rejection are conducted to demonstrate the advantages of the proposed control strategy.

This paper investigates a novel post-disaster power recovery scenario in which shelter batteries are subject to dynamic charging time constraints determined by their state-of-charge ($SoC$) dynamics. In this context, electric vehicles (EVs) serve as both transportation resources and mobile energy storage units. The objective is to determine an EV delivery plan that satisfies the power demand of shelter batteries within their dynamic charging time constraints while minimizing the total fleet travel costs. In addition, road-grade effects on EV energy consumption during travel are incorporated. In the first phase, a reduced graph is extracted from the original disaster-affected network to simplify the network topology. In the second phase, the problem is formulated as a mixed-integer linear programming (MILP) model. To obtain a high-quality delivery plan, an adaptive large neighborhood search (ALNS) algorithm is developed with problem-specific destroy and repair operators. The performance of the proposed ALNS is evaluated through comprehensive numerical experiments on benchmark instances of varying sizes constructed from a public road network. The results demonstrate that the proposed ALNS outperforms state-of-the-art methods. Finally, sensitivity analyses are conducted to quantify the impacts of uncertainties, time constraints, road grades, and reduced-graph construction.

Load transportation using unmanned aerial vehicles (UAVs) presents both intriguing possibilities and significant challenges in research and practical applications. This study aims to present a comprehensive literature review of recent progress in the development of multirotor UAVs transporting cable-suspended loads. A secondary objective is to assist researchers and engineers in the design and development of flight control systems for UAV-slung-load applications. To this end, the survey begins by providing a historical overview of flight control hardware platforms and load swing measurement systems used in UAV-slung-load systems. Subsequently, representative modeling approaches for UAV-slung-load systems are introduced. The survey then reviews a range of existing flight control strategies, highlighting their key characteristics and advantages. Finally, general challenges and potential future research directions for UAV-slung-load systems are discussed.

In safety-critical engineering applications, such as robust prediction against adversarial noise, it is necessary to quantify neural networks' uncertainty. Interval neural networks (INNs) are effective models for uncertainty quantification, giving an interval of predictions instead of a single value for a corresponding input. This article formulates the problem of training an INN as a chance-constrained optimization problem. The optimal solution of the formulated chance-constrained optimization naturally forms an INN that gives the tightest interval of predictions with a required confidence level. Since the chance-constrained optimization problem is intractable, a sample-based continuous approximate method is used to obtain approximate solutions to the chance-constrained optimization problem. We prove the uniform convergence of the approximation, showing that it gives the optimal INN consistently with the original ones. Additionally, we investigate the reliability of the approximation with finite samples, giving the probability bound for violation with finite samples. Through a numerical example and an application case study of anomaly detection in wind power data, we evaluate the effectiveness of the proposed INN against existing approaches, including Bayesian neural networks, highlighting its capability to significantly improve the performance of applying INNs for regression and unsupervised anomaly detection.

This article proposes a novel adaptive neurodynamic algorithm (ANA) to seek generalized Nash equilibrium (GNE) of the noncooperative constrained game with different monotone conditions. In the ANA, the adaptive penalty term, which acts as trajectory-dependent penalty parameters, evolves based on the degree of constraints violation until the trajectory enters the action set of noncooperative game. It is shown that the trajectory of the ANA enters the action set in finite time benefited from the adaptive penalty term. Moreover, it is proven that the trajectory exponentially (or polynomially) converges to the unique GNE when the pseudo-gradient of cost function in noncooperative game satisfies strong (or "generalized" strong) monotonicity. To the best of our knowledge, this is the first time to study the polynomial convergence of GNE seeking algorithm. Furthermore, when the pseudo-gradient mentioned above satisfies monotonicity in general, based on Tikhonov regularization method, a new ANA for finding its $\varepsilon $ -generalized Nash equilibrium ( $\varepsilon $ -GNE) is proposed, and the related exponential convergence of the algorithm is established. Finally, the river basin pollution game and 5G base station location game are given as examples to showcase the algorithm's effectiveness.

In this paper, the problem of emergency power supply for electric vehicles in road traffic networks after earthquake disasters is studied. In emergency response, it is important for evs to reach the target shelter as quickly as possible while considering the path dynamic changes caused by secondary disasters. Therefore, in order to solve this problem, this paper classifies it as the Dynamic Electric Vehicle Routing Problem with Time Windows (DEVRPTW), formulates it as a mixed integer linear programming model, and uses the Deep Reinforcement Learning (DRL) algorithm to solve it. The DRL algorithm adopted a dual-end merging Attention (DEMA) mechanism based on Multi-head Attention (MHA) modification. The DEMA model is subsequently trained using a reinforcement algorithm with rollout baselines. Finally, the MHA model, DEMA model and different training algorithms were compared and analyzed by using randomly generated geographic information data. The simulation results show that DEMA performs well when combined with an enhancement algorithm with rollout baseline.

Model predictive control (MPC) is subject to lim-itations, including stringent model accuracy requirements and time-consuming modeling, which impede its broader application. To address these challenges, this article proposes a predic-tive controller based on Data-Driven Recursive Subspace Least Squares (DD-RSPC), obviating the need for any prior model identification. Unlike traditional MPC, DD-RSPC dynamically updates the prediction model using real-time measured input-output data, thereby significantly enhancing system adaptability. Furthermore, this article introduces a rigorous stability theorem that employs the Lyapunov function to ensure the stability of the DD-RSPC approach. Finally, the effectiveness of DD-RSPC is validated through aeroengine simulation experiments, effectively substantiating its adaptability, stability, and practical applicability.

This article addresses the Kullback-Leibler (KL) control problem in Boolean control networks. In the considered problem, an extended stage cost function depending on the control inputs is introduced; in contrast to a stage cost of the conventional KL control problems in the Markov decision process cannot take into consideration the control inputs. An associated Bellman equation and a matrix-based iteration algorithm are presented. The theoretical analysis shows that the proposed KL control results in an approximated form of conventional dynamic programming (DP). Furthermore, the convergence analysis is presented, with the weight parameter converging to zero and diverging to infinity. In practical application examples, a comparison of the conventional DP and proposed KL control is illustrated.

Convolutional neural networks (CNNs) have been developed quickly in many real-world fields. However, CNN’s performance depends heavily on its hyperparameters, while finding suitable hyperparameters for CNNs working in application fields is challenging for three reasons: (1) the problem of mixed-variable encoding for different types of hyperparameters in CNNs, (2) expensive computational costs in evaluating candidate hyperparameter configuration, and (3) the problem of ensuring convergence rates and model performance during hyperparameter search. To overcome these problems and challenges, a hybrid-model optimization algorithm is proposed in this paper to search suitable hyperparameter configurations automatically based on the Gaussian process and particle swarm optimization (GPPSO) algorithm. First, a new encoding method is designed to efficiently deal with the CNN hyperparameter mixed-variable problem. Second, a hybrid-surrogate-assisted model is proposed to reduce the high cost of evaluating candidate hyperparameter configurations. Third, a novel activation function is suggested to improve the model performance and ensure the convergence rate. Intensive experiments are performed on image-classification benchmark datasets to demonstrate the superior performance of GPPSO over state-of-the-art methods. Moreover, a case study on metal fracture diagnosis is carried out to evaluate the GPPSO algorithm performance in practical applications. Experimental results demonstrate the effectiveness and efficiency of GPPSO, achieving accuracy of 95.26% and 76.36% only through 0.04 and 1.70 GPU days on the CIFAR-10 and CIFAR-100 datasets, respectively.

This study addresses state estimation problems for probabilistic Boolean control networks (PBCNs). Compared with deterministic Boolean networks, PBCNs have the stochastic switching in logical update functions in the state equation. Consequently, statistical analysis is required to estimate unavailable states, which induces an optimization problem called maximum-likelihood estimation. This article mainly focuses on two scenarios: 1) state estimation from partially measured state and 2) state estimation from output data, meaning observer design. The resulting optimization problems are solved using efficient algorithms based on dynamic programming. Concurrently, Dijkstra-type algorithms, which solve equivalent shortest path problems, are also proposed using best-first search. Furthermore, both the proposed algorithms derive novel observer design methods for PBCNs. The proposed algorithms are evaluated with practical estimation problems aiming to the sensor reduction and applied to gene regulatory networks of apoptosis and Lac operon.

This paper is concerned with the motion control for a quadrotor with a cable-suspended load (QCSL). A fixed-time control strategy is presented to improve the transient response and robustness of the QCSL with external disturbance. The overall control scheme is designed with a cascade structure to better cope with the underactuated property of the QCSL and the indirect effect of the control force on the load’s velocity through the tensile force on the cable. The simulation results are given to demonstrate the performance of the proposed scheme. Furthermore, actual flight tests were performed on a new experimental QCSL to validate the effectiveness of the proposed control strategy.

In order to reduce the possibility of structural fatigue and increase the lifetime of conveying fluid pipe, transverse vibration must be effectively eliminated. In this work, using parallel nonlinear energy sinks (NESs), a passive vibration controller is proposed to dissipate the vibration energy of the conveying fluid pipe. A high-order model of the conveying fluid pipe-parallel NESs system, in the form of partial differential equation, is derived and then converted into a quadratic form model containing the gradient information of a convex function. Combining the energy disturbance technique and first order convexity characteristic, the exponential stability of the closed-loop system is proved, which addresses the effectiveness of the proposed parallel NESs. Then, numerical simulations are given to verify the theoretical results and to illustrate the advantages of parallel NESs comparing with single NES. Finally, the reliability of the proposed approach is preliminarily verified through experiment.

Many fundamental studies have indicated that the vibration of conveying fluid pipe is more severe and complex at high subcritical fluid velocity. The control of vibration in this case, however, still remains a challenge for the general vibration absorbers. In this work, the inerter enhanced nonlinear energy sink (NES) is used to solve the severe vibration problem. The partial differential equation form model of the conveying fluid pipe-inerter enhanced NES system is derived and converted to an ordinary differential equation with an easy solution. Global stability of the conveying fluid pipe-inerter enhanced NES system is proved under the Lyapunov stability theory framework and functional analysis technique, to explain the effectiveness of the inerter enhanced NES. The influence of parameters on the conveying fluid pipe is discussed through a sensitivity analysis. The parameters of the proposed controller are optimized based on the energy functional. Finally, numerical examples are provided to verify the control effectiveness and the theoretical results, and also to show the advantages of inerter enhanced NES by comparing with the general NES.

This article considers a Mayer-type optimal control problem of probabilistic Boolean control networks (PBCNs) with uncertainty on selection probabilities which obey Beta probabilistic distributions. The expectation with respect to both the selection probabilities and the transitions of state variables is set as a cost function, and it deduces an equivalent formulation as a multistage decision problem. Furthermore, the dynamic programming technique is applied to solve the problem and performs a novel optimization algorithm in the fashion of semitensor product. A numerical example of a biological model of apoptosis protein demonstrates the effectiveness and feasibility of the proposed framework and algorithms.

This article studies the optimal control of probabilistic Boolean control networks (PBCNs) with the infinite horizon average cost criterion. By resorting to the semitensor product (STP) of matrices, a nested optimality equation for the optimal control problem of PBCNs is proposed. The Laurent series expression technique and the Jordan decomposition method derive a novel policy iteration-type algorithm, where finite iteration steps can provide the optimal state feedback law, which is presented. Finally, the intervention problem of the probabilistic Ara operon in E. coil, as a biological application, is solved to demonstrate the effectiveness and feasibility of the proposed theoretical approach and algorithms.

This article considers the problem of event-triggered optimal control for discrete-time switched nonlinear systems with constrained control input. First, an event-triggered condition is given to make the closed-loop switched system asymptotically stable. Second, a novel method, event-triggered heuristic dynamic programming (ETHDP), is applied to derive the optimal control policy. Two neural networks (NNs) are utilized to approximate the value function and control law, respectively. When the event-triggered condition is violated, the weights of the two NNs are updated, which can decrease the networks calculation and transmission load notably. A proof of the convergence of the ETHDP is also carried out. Finally, the effectiveness of the proposed method is verified by an example.

This paper deals with the problem of a quadrotor experiencing a total failure of one actuator. First, a nonlinear mathematical model for the faulty quadrotor is derived with three control inputs and six outputs, which includes the translational and rotational dynamics. Because of the limited inputs, the controllability of the yaw state is sacrificed, and the control inputs are reallocated to the other three healthy rotors. Second, a nonlinear controller is designed based on the proposed model. This presented controller includes two subcontrollers: 1) a roll angle, pitch angle, and altitude subcontroller and 2) a horizontal position subcontroller. In the controller design, the Moore-Penrose pseudoinverse of the coefficient matrix is applied to overcome the singularity of the roll angle. Third, the stability of both of the designed subcontrollers is verified by the Lyapunov stability theorem, and the convergence of the designed subcontrollers is analyzed. Finally, simulation results are provided to verify the effectiveness of the proposed model and the designed controller.

This paper investigates an energy management problem of parallel hybrid electric vehicles (HEVs), which can be modeled as a finite horizon optimal control problem for the discrete dynamical systems. Taking the essential characteristics of plug-in HEVs into account, a logical-based optimization approach is applied to realize the equivalent energy cost minimization of the powertrain system. Then, based on semi-tensor product, an effective algorithm for obtaining an approximate optimal solution is proposed by using the logical network-based approach. Finally, simulation results are presented to illustrate and show the effectiveness of the proposed optimal control scheme and the corresponding algorithm.

This brief studies controllability for probabilistic Boolean control network (PBCN) with time-varying feedback control laws. The concept of feedback controllability with an arbitrary probability for PBCNs is formulated first, and a control problem to maximize the probability of time-varying feedback controllability is investigated afterward. By introducing semitensor product (STP) technique, an equivalent multistage decision problem is deduced, and then a novel optimization algorithm is proposed to obtain the maximum probability of controllability and the corresponding optimal feedback law simultaneously. The advantages of the time-varying optimal controller obtained by the proposed algorithm, compared to the time-invariant one, are illustrated by numerical simulations.

In this article, we investigate the asymptotical feedback set stabilization in distribution of probabilistic Boolean control networks (PBCNs). We prove that a PBCN is asymptotically feedback stabilizable to a given subset if and only if (iff) it constitutes asymptotically feedback stabilizable to the largest control-invariant subset (LCIS) contained in this subset. We proposed an algorithm to calculate the LCIS contained in any given subset with the necessary and sufficient condition for asymptotical set stabilizability in terms of obtaining the reachability matrix. In addition, we propose a method to design stabilizing feedback based on a state-space partition. Finally, the results were applied to solve asymptotical feedback output tracking and asymptotical feedback synchronization of PBCNs. Examples were detailed to demonstrate the feasibility of the proposed method and results.

The stability problem is studied for hybrid systems with delays in this paper. Based on Lyapunov–Razumikhin approach, a novel theorem is presented for such system with the characteristic of homogeneity so that the system is globally preasymptotically stable. In particular, under the homogeneous assumption, we are able to obtain some rather weak conditions compared with general nonhomogeneous hybrid systems in this paper. Finally, two illustrative numerical examples are presented to demonstrate the applicability and the effectiveness of our theorems.

This paper investigates the regulation problem for a class of nonholonomic systems that includes power form systems and an approximated system of the rolling sphere as special cases. The basic idea is first to introduce a virtual periodic moving trajectory that satisfies certain persistent excitation condition (PE) and has the zero value at some time instants. Based on LaSalle invariance principle, the associated tracking problem is then solved under a necessary condition for stabilization and particularly true for the power form systems and the rolling sphere. With the help of virtual trajectory, the achieved tracking result is applied to the regulation problem and used to guarantee practical stability. The proposed controllers have a simple and explicit form, and hence are easily implemented. Simultaneously, fast convergence is guaranteed, thanks to the K-exponential convergence. More interestingly, the used approach is adding sufficiently exciting signals to the systems by considering virtual tracking signals so that the attractivity of the origin can be guaranteed based on LaSalle invariance principle. Thus, it is possible to extend the proposed results to more general systems. To verify the effectiveness of the proposed scheme, interesting simulation results are presented.

In this paper, a variation of the congestion situation is considered and a new game, the congestion game with player-specific (CGPS) utility functions, is proposed. This paper is motivated by some application scenarios that rule out the possibility of employing the existing game model to study such congestion situations. The CGPS game is characterized by adding a player-specific term and a weighted parameter with respect to the utility function. By using the semitensor product of matrices, the algebraic representation of the CGPS game is given and the existence of the weighted potential function is proved. Finally, the results are applied to solve the service chain composition problem in network function virtualization (NFV) and to analyze the effect of player-specific function on service chain configuration in NFV. Note to Practitioners-This paper is motivated by resource allocation problems in congestion networks where strategic users behave selfishly and aim at optimizing their own individual utility in the absence of a central controller. Compared with the centralized algorithms of poor reliability and scalability, game-theoretic control provides a promising distributed approach for resource allocation. In the game-theoretic framework, the existence and seeking of the desired solution are important issues. In this paper, a novel model is established to extend the utility functions space guaranteeing the existence of the solution. The developed utility design is used to capture users' different sensitivities to the effects of the network system. Simultaneously, it is more meaningful from the view of engineering to design the utility functions so that the desirable behavior is reachable. We also give an explicit scheme to seek the desired solution. The proposed model is finally applied to the service chain composition problem in NFV, of which the aim is to find the best service chain of users that accommodates their individual requirements. The proposed model shows reliable and effective.

This brief investigates the infinite horizon optimal control problem for stochastic multivalued logical dynamical systems with discounted cost. Applying the equivalent descriptions of stochastic logical dynamics in term of Markov decision process, the discounted infinite horizon optimal control problem is presented in an algebraic form. Then, employing the method of semitensor product of matrices and the increasing-dimension technique, a succinct algebraic form of the policy iteration algorithm is derived to solve the optimal control problem. To show the effectiveness of the proposed policy iteration algorithm, an optimization problem of p53-Mdm2 gene network is investigated.