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Constrained integer stochastic optimization problems (CISOP) are optimization problems where the cost function is stochastic and the constraints are deterministic. Solving the CISOP using conventional optimization techniques is time-consuming when the design space is huge. Ordinal optimization offers an efficient technique suitable for solving the CISOP. In this paper, an approach that uses golden jackal optimization assisted by ordinal optimization (GJOO) is developed for resolving the CISOP in a relatively short time. The GJOO is composed of three phases: metamodel, global search, and ranking and selection. At first, the polynomial chaos expansion is used as a metamodel to rapidly assess a solution. Next, advanced golden jackal optimization is adopted to determine N excellent solutions from the entire design space. At last, the modified optimal computing budget allocation is adopted to determine a distinguished solution from the N excellent solutions. The GJOO is utilized to the shipping/receiving docks optimization problem of a container freight station in air cargo. The performances of the GJOO are compared with those of five meta-heuristic methods. Empirical results demonstrate the efficiency and robustness of the GJOO algorithm. [ABSTRACT FROM AUTHOR]

Based on game theory, this paper considers the consensus problem of second-order hybrid multiagent systems (HMASs) in a leader–follower framework, which includes continuous-time multiagents and discrete-time multiagents. Firstly, the competitive relationship between two groups is modeled as a multi-player game type. To achieve consensus, we design different cost functions according to the rules of the game and assume that each player has a global purpose of minimizing their own cost. Moreover, the players adjust their states at the next moment according to the Nash equilibrium solution. Then, using matrix theory and stability theory, we analyze the necessary and sufficient conditions for the consensus problem of the second-order HMASs with different leaders, and show that the control parameters (sampling interval and feedback gain) and the eigenvalues of the network topology have essential impact on the consensus of the system. Finally, the validity of the results is tested by simulation examples. [ABSTRACT FROM AUTHOR]

In this paper, we consider a distributed economic dispatch problem (EDP) in smart grids, where each generator only communicates with its neighbors and minimizes its own cost in the presence of network-wide equality constraints and local capacity limits. Distributed algorithms to solve this problem have become an attractive focus of engineering research due to their multiple advantages. Many existing methods are time-triggered, whereas few works have been dedicated to solving the problem using an event-triggered approach. This problem gets more challenging when further accelerated convergence is desired. To solve such a problem, we carefully design an event-triggered parameter distributed accelerated algorithm, named as ET-PDA. On the one hand, the choice of different parameters in ET-PDA will lead to different momentum (Nesterov or heavy-ball) methods, which facilitates the accelerated convergence of the algorithm. On the other hand, the event-triggered mechanism in ET-PDA makes the time gap between two continuous interaction moments of each generator larger than the iteration interval, contributing to improved communication efficiency. In particular, ET-PDA achieves linear convergence when the local cost function of each generator is smooth and strongly convex, moreover, it precludes Zeno-like behavior. The effectiveness of ET-PDA is also verified through a series of simulation experiments. [ABSTRACT FROM AUTHOR]

This paper proposes a novel importance-based persistent coverage for environments with hotspots. Multiple quadrotors are utilized to maintain coverage while acquiring high-quality information over prioritized regions by lowering the altitude. To achieve this, the approach utilizes distributed communication protocols and cost functions to address the tradeoff between wider coverage and better quality information. The cost functions determine three-dimensional waypoints based on coverage levels, sensor resolution, and Euclidean distance. Voronoi tessellations are used locally for efficient computation and collision avoidance. Trajectories of the quadrotors are generated based on quadrotor dynamics using error functions that take into account state information and desired waypoints. The proposed approach is validated through simulations, comparisons, and experiments for multi-agent systems under quadrotor dynamics. Further, metrics of performance analysis are formulated that show consistent coverage levels and differences to assess the novel importance-based approach. The simulations and experiments demonstrate the feasibility and effectiveness of the approach, with prioritized regions receiving higher sensor resolution while maintaining persistent coverage of the environment. • Novel importance-based persistent coverage for information acquisition over hotspots. • Quadrotor waypoints based on information resolution and decaying environmental costs. • Distributed communication protocols for scalability and low computational complexity. • Real-time importance-based persistent coverage for multi-quadrotor dynamical systems. • Proposed coverage with varying velocities is verified by simulation and experiments. [ABSTRACT FROM AUTHOR]

In this paper, the trajectory tracking control problem for underactuated autonomous vehicles moving in 2-D space is studied. Firstly, the trajectory tracking error dynamics with the equivalent point being the origin are given. Since both model and input uncertainties of vehicles are considered, a robust control problem is formulated by giving the extended trajectory tracking error dynamics. Through showing that a robust control law is equivalent to an optimal control law for a nominal system under some conditions, the robust control problem is transformed into an equivalent optimal control problem and a cost function composed of uncertainty bounds and discount factor is constructed. In order to solve the optimal control problem, a policy iteration-based ADP algorithm is proposed, in which two neural networks, called critic and actor networks, are applied respectively to describe the unknown value function and control law. The weights of the two networks are tuned through the gradient descent method at each iteration and the optimality and convergence properties are provided in the sequel. At last, simulation results are given to illustrate the validity of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• Rigid-flexible coupling dynamic model of a large antenna satellite is established. • Hybrid control of satellite attitude maneuver and structural vibration is studied. • The optimal distribution and constraints of cable actuators are both considered. This paper focuses on the study about hybrid control strategy of attitude maneuver and structural vibration for a large phased array antenna satellite. The satellite system is equipped with cable actuators in the antenna structure and control moment gyros on the central satellite. Firstly, the rigid-flexible coupling dynamic model of the entire satellite system is established based on the hybrid coordinate method. Then a control strategy is presented which could realize the ideal attitude maneuver of satellite while suppressing the structural vibration simultaneously. The proposed strategy is contained of two parts: the attitude controller is designed based on the computed torque method, and the vibration controller is achieved by using a piecewise cost function which could ensure the unidirectional and saturated constraints of cable actuators. Finally, numerical simulations of a large antenna satellite model demonstrate that the structural vibration during the attitude maneuver process can be suppressed effectively by applying the proposed control strategy. [ABSTRACT FROM AUTHOR]

As a blind equalization algorithm, the fractional lower-order statistics based multimodulus algorithm (FLOS-MMA) is suitable for equalizing multilevel quadrature amplitude modulation (QAM) signals in impulsive noise. However, the equalization performance of FLOS-MMA is severely degraded in strong impulsive noise because the first-order gradient of the cost function constrains the ability of FLOS to suppress large outliers in the equalizer output signal and is sensitive to large outliers in the equalizer input signal. To enhance the robustness of multimodulus blind equalization algorithm against impulsive noise, a FLOS based momentum fractional-order multimodulus algorithm (FLOS-MFOMMA) is proposed in this paper. The proposed algorithm mitigates the adverse effects of impulsive noise on the updating of equalizer coefficients by utilizing the fractional-order gradient and accelerates the convergence speed by incorporating a momentum term. Then, the steady-state excess mean square error and the computational complexity of FLOS-MFOMMA are analyzed theoretically. Finally, numerical simulations, for equalizing 16-QAM signals under impulsive noise environments, show that the proposed algorithm is superior to FLOS-MMA in terms of robustness and convergence speed. [ABSTRACT FROM AUTHOR]

In this paper, we propose a game control method for a machine-controlled spacecraft to cooperate with a human-controlled spacecraft in near-circular orbit for a rendezvous mission. Inspired by the cooperative game theory, the two-spacecraft rendezvous process is formulated as a cooperative differential game (DG) with a common cost function. A challenging problem is that the intent of the human-controlled spacecraft represented by the weighting matrix in the cost function is not available to the machine-controlled spacecraft. To complete the rendezvous mission optimally, an intent inferring based game control algorithm is developed for the machine-controlled spacecraft. Specifically, an adaptive approach is proposed for the machine-controlled spacecraft to identify the feedback gain matrix of the human-controlled spacecraft online based on the system state alone with the concurrent learning (CL) technique; furthermore, the unknown weighting matrix representing the intent of the human-controlled spacecraft in the cost function is retrieved by an inverse differential game (IDG) method. Accordingly, the control law of the machine-controlled spacecraft is updated in real-time. The control strategies of the spacecraft converge to near the Nash equilibrium. Finally, the simulation result is presented to illustrate the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

In practical decision-making scenarios, the challenges lie in obtaining the collective opinion that the majority of decision makers are willing to accept, thereby arriving at the final decision result. Among them, both the inherent uncertainty and reliability embedded in the information may lead to a deviation from the fact in the final decision result. Moreover, individual and social behaviors have the potential to influence the acquisition of effective decision result. Based on this, this paper constructs a novel social network-based consensus model under discrete Z -number environment, taking into account the individual and social behaviors of experts. First, novel trust propagation and trust aggregation operators with discrete Z -numbers are developed, which aim to form a complete social trust network. Then, unit consensus cost function and compromise boundary function based on social trust network are given to objectively determine the unit adjustment costs and compromise boundaries of each decision maker. In addition, a minimum cost consensus model considering the coexistence and interaction between individual and social behaviors is explored. Last, the feasibility and superiority of the model are proved through a case of the agricultural supply chain. Through simulation and comparative analysis, the results show the effectiveness of novel trust propagation and aggregation operators. And the necessity of considering individual and social behaviors for consensus reaching process is presented. [ABSTRACT FROM AUTHOR]

This paper investigates the fixed-time cluster optimization problem of first-order multi-agent systems (MASs) under directed detail-balanced networks, in which the optimization objective is a linear combination of local objective functions. Two piecewise distributed control protocols are proposed to solve the optimization problem with time-invariant and time-varying objective functions, respectively. Under the two proposed piecewise control protocols, the fixed-time cluster optimization problem can be solved on the basis of maintaining the cluster consensus unchanged. Furthermore, the initial states of all the agents need not be restricted to the minimum of their local cost functions. Finally, we provide two examples to illustrate the effectiveness of the proposed control protocols. [ABSTRACT FROM AUTHOR]

The system identification under non-negative constraints problem is a common and important one in the real-life problems. A non-negative least mean squares algorithm was proposed to address such problems. However, it suffers from slow and unbalanced convergence. Motivated by the introduction of non-Newtonian gradient in least mean squares algorithm can accelerate the convergence. In this paper, an efficient non-negative least mean squares algorithm based on q-gradient is developed. At first, the q-gradient of the mean square error cost function is derived from the definition of Jackson's derivative. Then, the modified algorithm is developed by replacing the conventional gradient with the q-gradient on a fixed-point iteration scheme using Karush-Kuhn-Tucker condition. Simulations for stationary and non-stationary system identification are conducted to illustrate the effectiveness of the developed algorithm for constrain the system parameters to be non-negative and accelerate the convergence speed. Besides, it can balance the convergence rate for widely distributed parameters, especially for the convergence of small parameters. For practical application, the accuracy of system identification when the order of the adaptive filter is larger than the order of the system is also verified by the simulation. • An efficient non-negative least mean squares algorithm based on q-gradient is developed. • The algorithm builds on a fixed-point scheme with KKT conditions using q-gradient of cost function. • The algorithm can improve the convergence speed and the balance of the convergence of parameters. [ABSTRACT FROM AUTHOR]

This paper deals with the guaranteed cost extended dissipative stabilization problem of switched IT-2 fuzzy systems (SFSs) via intermittent control. Firstly, we propose a new class of system performance coefficients, taking both the extended dissipative performance and quadratic cost function performance into account. Then, by using a switched-path-dependent fuzzy Lyapunov functions (SFLFs) approach, the membership-function-shape-dependent (MFSD) existence conditions of a set of intermittent controllers are established. Furthermore, we apply the designed intermittent control scheme to three practical control problems: 1) A controller/actuator-failure-triggered intermittent controller is designed, and the guaranteed cost extended dissipative performance of SFSs is ensured in the presence of controller/actuator failures; 2) A switching-triggered intermittent controller is designed, and the guaranteed cost extended dissipative performance of SFSs is ensured in the presence of transmission delay of switching signal, and the asynchronous phenomena is eliminated; 3) A hybrid-triggered intermittent controller is designed, and the guaranteed cost extended dissipative performance of SFSs is ensured while the control resources are saved. Finally, the effectiveness of the results are demonstrated through a robot arm model. [ABSTRACT FROM AUTHOR]

To improve the robust characteristic and filtering accuracy of the traditional adaptive filtering algorithms (AFAs) under censored regression (CR) model in impulsive interference environments, some robust CR-type AFAs (R-CR-AFAs) have been developed. However, these exiting R-CR-AFAs may suffer from high steady-state misalignment in parameter estimation scenario. Therefore, in this paper, we first construct a unified framework to develop a class of R-CR-AFAs by adopting various robust strategies with error nonlinearities as cost functions. Then the mean and mean-square stability ranges and steady-state convergence behavior of the R-CR-AFAs are analyzed via resorting to some frequently-utilized assumptions and lemmas. In particular, a novel censored regression generalized modified Blake-Zisserman adaptive filtering (CR-GMBZ-AF) algorithm is presented to achieve higher steady-state estimation accuracy contrasted to related R-CR-AFAs benefiting from the advantages of the newly proposed GMBZ criterion based on the unified framework. Numerical simulation experiments manifest that the developed CR-GMBZ-AF algorithm arrives at better learning performance in comparison with state-of-the-art algorithms and the theoretical steady-state model demonstrates good agreement with simulation results under parameter estimation and acoustic echo cancellation scenarios. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of distributed H 2 / H ∞ filtering through unreliable wireless sensor networks, where data packet drops occur during the signal transmission from neighbor nodes to local nodes due to limited communication resources. To optimize estimation performance in the H 2 and H ∞ senses, a matrix decomposition method is proposed to settle the couplings resulting from data packet drops. Compared with the existing H ∞ performance optimization methods, a less-conservative design against the non-zero initial condition is derived by introducing a modified cost function involving two H ∞ performance indexes. Moreover, in the distributed framework with data drops, the estimation performance of the local node is related to data arrival rates of relevant channels and, by proposing a notion of transmission capacity referring to data arrival rate, the H 2 performance margin of any one channel's transmission capacity is obtained. Especially, based on an equivalent form of a modified algebraic Riccati equation (MARE), a necessary and sufficient condition is further established for the existence of a local filter satisfying an expected H 2 performance. A simulation of a radar tracking system is given to demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive dynamic event-triggered (ADET) robust control method for unknown nonlinear systems using iterative neural dynamic programming (INDP). Firstly, the ADET robust control problem is transformed into an ADET optimal control problem by introducing an infinite domain integral function. Then, dynamic variables and adaptive thresholds are designed for discrete-time systems with auxiliary variables. The proposed ADET method reduces computation and transmission costs compared to existing static and dynamic event triggering methods. INDP is utilized to learn the optimal solution of the ADET Hamilton-Jacobi-Bellman equation within the heuristic dynamic programming (HDP) framework, providing system stability and algorithm convergence. The INDP algorithm employs model, action, and critic neural networks and includes a method to directly minimize the iterative cost function in the back-propagation process. The neural network implementation of the INDP algorithm is detailed. Simulation results demonstrate that the proposed ADET method based on INDP achieves faster convergence with fewer transmitted data and control updates. [ABSTRACT FROM AUTHOR]