Simulation Examples Research Articles

Observer-Based Prescribed Performance Adaptive Neural Network Tracking Control for Fractional-Order Nonlinear Multiple-Input Multiple-Output Systems Under Asymmetric Full-State Constraints

In this work, the practical prescribed performance tracking issue for a class of fractional-order nonlinear multiple-input multiple-output (MIMO) systems with asymmetric full-state constraints and unmeasurable system states is investigated. A neural network (NN) nonlinear state observer is developed to estimate the unmeasurable states. Furthermore, the barrier Lyapunov functions with the settling time regulator are employed to deal with the asymmetric full-state constraint from the fractional-order MIMO system. On this ground, the prescribed performance adaptive tracking control approach is designed, assuring that all system states do not exceed the prescribed boundaries, and the tracking errors converge to the predetermined compact sets within a predefined time. Finally, two simulation examples are presented to show the effectiveness and practicability of the proposed control scheme.

Performance enhancement of a magnetoactive elastomer actuator through a coupled magnetoelastic topology optimization scheme

Abstract We have found that considering local magnetic fields, large deformations, and magnetoelastic coupling simultaneously significantly affect the resulting shape in magnetoelastic topology optimization in a uniaxial actuator case. In contrast to the work presented here, other works incorporate magnetoelastic formulations that include simplifying assumptions on the local field, and subsequent effects on the magnetization response of the material, or the absence of large deformation mechanics, or both. These assumptions were shown to produce solutions that differ substantively from cases where local fields and large deformations are addressed concurrently. Magnetoelastic topology optimization schemes are needed to optimize magnetoactive elastomer (MAE) devices. MAE devices are magnetic particle-filled polymer matrices designed for specific actuations and controlled remotely by an external magnetic field. They garner considerable research interest as an emerging technology for actuators in soft robots or in applications requiring untethered actuation. The material properties of MAEs are dependent on the volume fraction of particles in the elastomer matrix, where a high-volume fraction increases relative permeability (for soft magnetic particles) but also increases elastic modulus. For optimal actuation, a tradeoff between low stiffness and high magnetic response must be made by adjusting volume fraction and controlling material placement. Using a topology optimization scheme that considers both the magnetic and mechanical properties of the material, the shape and material composition of the device can be tuned to best achieve the desired actuation displacement. In this work, a density-based magnetoelastic multimaterial topology optimization scheme for soft magnetic material is developed in COMSOL Multiphysics. The optimization scheme uses multiphysics coupling that considers local magnetic fields and large deformations at each iteration through a Maxwell stress tensor formulation. A simulated example is then considered to demonstrate the effectiveness and necessity of a coupled optimization. The effect of considering large deformations during optimization is also investigated. It was found that a coupled topology optimization scheme with large deformations produced shapes with modes of actuation not captured by schemes with simplifying assumptions, leading to better performance at lower material cost. Considering large deformations in the coupled scheme offered significantly better performance, with an increase of 81.3% in a side-by-side performance simulation when compared to uncoupled cases.

Identification of fractional-order transfer functionsand nonzero initial conditions using exponentiallymodulated signals

Abstract A new methodology that uses exponentially modulated signals with arbitrary excitation waveforms for the identification of fractional order transfer functions is proposed. In contrast to previous approaches where initial conditions were not considered and the system was required to be at rest for the identification procedure, the current contribution extends the formulation to the case where the system has non-zero initial conditions, dispensing with the need to place it at a resting state. This generalization is important in feedback instrumentation and metrology applications where the measurement or control process may not be disrupted to perform identification. Moreover, the procedure has a broader scope of applications because it structurally contemplates the case when the model presents derivatives in the input. Full identification of the system parameters as well as the fractional exponents associated with the model dynamics are achieved through a grid search procedure with resolution adjustable by the user. Two simulation examples are presented to illustrate the effectiveness of the proposed approach. The first example is concerned with the effect of measurement noise at the observed system output, whereas the second involves the identification of the impedance of a three-dimensional RC network model. These types of RC networks
have dynamics capturing complex phenomena with emergent responses and are ideal for emulating the complex dynamics encountered across physical sciences and in particular interdisciplinary subject areas such as biomedical engineering.

Observer-Based Event-Triggered Fuzzy Control for Switched Multi-Agent Systems with State Constraints and Sensor Faults

In this paper, we consider the output feedback event-triggered tracking control problem for a class of switched nonlinear multi-agent systems (MASs) with sensor faults and state constraints. For unknown nonlinear functions in the system, fuzzy logic systems are used to approximate them. To address sensor faults, sensor error compensation coefficients are designed to compensate for the errors caused by sensor faults. Meanwhile, state observer is designed to estimate the unmeasurable states in the system, providing necessary information for control design. Based on directed switching networks containing a directed spanning tree, an event-triggered output feedback controller is designed to enable the MAS to track the leader agent. By constructing a suitable barrier Lyapunov function, the stability of the system is analyzed, and it is proved that the consensus tracking can be achieved without violating the state constraints, the uniform boundedness of all the closed-loop system signals is ensured and the Zeno behavior can be eliminated. Finally, the effectiveness of the proposed algorithm is verified through a simulation example.

Robust synchronization of Markovian jump complex dynamical networks under link attacks: Delay-dependent dynamic event-triggered control approach

This paper investigates the synchronization problem for Markovian jump complex dynamical networks (MJCDNs) subject to link attacks. An extended dynamic event-triggered (DET) control is proposed that takes into account limited network resources and time-varying delay constraints. The proposed method enhances the robustness and resource-saving ability of the DET mechanism, and avoids the Zeno phenomenon. It provides MJCDNs to have a buffering ability for external interference. Additionally, this paper introduces a robust model-based strategy to address scenarios where network attackers inject false data into the link channels between nodes. This approach reduces the complexity of processing MJCDNs subject to link attacks. By leveraging network topology and Lyapunov functions, the criteria are derived to achieve robust synchronization of MJCDNs under link attacks. Finally, simulation examples including Chua’s circuit are presented to verify the validity and superiority of the results.

Double adaptive event-triggered strategy for leader-following group consensus of heterogeneous multi-agent systems in cooperative–competitive networks

This article investigates adaptive event-triggered control for leader-following group consensus of hybrid heterogeneous multi-agent systems (HMASs) composed of single- and double-integrator agents under cooperative–competitive relationships. By using fully distributed control protocol and adaptive protocol, the leader-following group consensus can be achieved without global information. Additionally, a novel adaptive double dynamic event-triggered mechanism (DDETM) is proposed, in which two independent adaptive event-triggered conditions are devised to maintain events of communication and updates of controllers. Furthermore, adaptive parameters and internal dynamic variables are employed to reduce the number of triggers and exclude Zeno phenomenon. By utilizing Lyapunov stability theory, some sufficient conditions are acquired for the leader-following group consensus of the HMASs. Finally, simulation examples are given to prove the effectiveness of the proposed method.

Adaptive connectivity-preserving formation control with a dynamic event-triggering mechanism

Connectivity preservation for real-life multi-agent systems is key to designing an effective and reliable control algorithm to achieve desired objectives. But the coexistence of system nonlinearities and uncertainties makes it difficult for the algorithm design, and especially in the event-triggered setting, such difficulty becomes severer due to the demand for integration of multiple ingredients. This paper focuses on proposing an adaptive connectivity-preserving formation control strategy that incorporates a dynamic event-triggering mechanism, in the scenario of the unknown control directions and intrinsic nonlinearities coupling with parameter uncertainties. First, a cluster of potential functions serving as control barrier functions are given to maintain the prescribed connectivity of communication graph. Second, two types of dynamic gains (one type is based on a Nussbaum function) are introduced for agents to cope with the system nonlinearities, uncertainties and the adverse effect of the execution error. As such, an adaptive event-triggered formation protocol is constructed such that the desired formation with connectivity preservation is achieved while a positive minimum inter-execution interval is ensured for each agent to avoid Zeno behavior. Particularly, the threshold in the developed event-triggering mechanism is dynamically adjusted online, rather than static, which better improves resource efficiency. A simulation example is given to demonstrate the effectiveness and advantage of the proposed strategy.

Time-varying formation tracking of multi-agent systems with multiple leaders based on aperiodic time-triggered intermittent control

In this article, the distributed aperiodic time-triggered intermittent control protocol (DATICP) is presented for the time-varying formation tracking of multi-agent systems (MASs) with multiple leaders. Firstly, we present the mathematical description of DATICP. The characteristic of DATICP lies in that for each agent, its control signal is updated at aperiodic sampling instants on work time intervals and disappears on rest time intervals. Secondly, based on a mixed time-dependent Lyapunov functional (MTLF), the exponential stability theorem of the derived system is established. Thirdly, according to the theorem, sufficient conditions are given to achieve time-varying formation tracking for MASs with multiple leaders by DATICP. As a result, a corollary is obtained which compromises conservativeness and complexity. Finally, two simulation examples are offered for illustration.

Research on solving multi-preference intelligent scheduling problem based on improved genetic algorithm

Abstract. The paper will use the improved genetic algorithm that adds Gaussian perturbation based on the standard deviation of the population fitness and increases the variance probability operation to optimize the traditional genetic algorithm, and after iterating until the optimal scheduling strategy is found, we combine this algorithm with a mathematical model, and adopt a variety of variations to improve the efficiency of the algorithm. Among them, we take into account the customer flow, area of the store, employee work preference and other related factors to maximize its adaptability. We use real store employee data for simulation example experimental evidence, and compared with other algorithms, the results show that the study of the scheduling optimization ideas and algorithms are practical and feasible.

Distributed Neuroadaptive Formation Control for Aerial Base Station-Assisted Hovercraft Systems with Mixed Disturbances

Effectively addressing the formation control of ABS-assisted hovercraft systems with heterogeneities, unavailable leaders’ convex combination states, nonlinearities, and mixed disturbances poses significant challenges. This paper proposes a distributed neuroadaptive formation tracking strategy of ABS-assisted hovercraft systems for the first time, where aerial base stations (ABSs) are composed of unmanned aerial vehicles (UAVs) for data distribution and computation offloading. Firstly, UAVs are designed to track the virtual-leader while shaping a fixed formation, and the observer is devised for each follower hovercraft to estimate the convex combination states of UAVs. Then, output regulation equations are employed to transform heterogeneous systems into a compact form via the Kronecker product, while neural networks (NNs) are introduced to compensate for model nonlinearities. Furthermore, based on random differential equations (RDEs) combined with Lyapunov theory, the noise-to-state practical stability in probability (NSPS-P) property of the error dynamics under mixed disturbances can be obtained. Finally, simulation examples demonstrate that the outputs of follower hovercrafts rapidly achieve a time-varying formation and rotate around convex combination states of leader UAVs simultaneously.

New method for stability of probabilistic Boolean networks with inequality constraints

This paper studies the stability of probabilistic Boolean networks (PBNs) with state inequality constraints by establishing a new algebraic representation. On one hand, combining with the semi-tensor product (STP) of matrices and the law of total expectation, the algebraic representation of PBNs is established. On the other hand, based on the STP of matrices, an algorithm is proposed to determine the solution set of the state inequality constraints. Then, based on the algebraic representation and the inequality constrained solution set, a new form of transition probability matrix is constructed for PBNs. Finally, by the proposed transition probability matrix, new criteria are proposed for the finite-time stability with positive probability of PBNs. Several simulation examples are used to illustrate the effectiveness of the obtained results.

Attack-Dependent Adaptive Event-Triggered Security Fuzzy Control for Nonlinear Networked Cascade Control Systems Under Deception Attacks

This article investigates the issue of H∞ security output feedback control for a nonlinear networked cascade control system with deception attacks. First, to further reduce the amount of communication data, reasonably schedule network resources, and alleviate the impact of multi-channel deception attacks, an attack-dependent adaptive event-triggered mechanism is introduced into the primary network channel, and its adaptive triggered threshold can be adjusted according to the random attack probability. Secondly, the output dynamic quantization of the secondary network channel is considered. Then, a novel security cascade output feedback controller design framework based on the Takagi–Sugeno (T-S) fuzzy networked cascade control system under deception attacks is established. In addition, by introducing the Lyapunov–Krasovskii stability theory, the design conditions of the controller are given. Finally, the effectiveness and superiority of the proposed design strategies are verified by two simulation examples of power plant boiler–turbine system and power plant boiler power generation control system.

Adaptive backstepping control for nonlinear CAVs platoon with input delays and disturbances

In recent years, the platoon control of connected autonomous vehicles (CAVs) has attracted a lot of attention in intelligent transport research. The third-order nonlinear vehicle dynamics model, due to its practicality, has been adopted in many research projects. In platoon control, controller input delays and disturbances are the significant issues that cannot be ignored. To this end, this paper investigates the cooperative control problem of third-order nonlinear CAVs platoon with controller input delays and unknown external disturbances. First, the neural network (NN) is used to approximate external disturbances with unknown bounds. Then, with the help of Pade approximation, a new variable is introduced to deal with the input delays. Subsequently, a corresponding compensation system is constructed based on the introduced variable. At last by using backstepping approach, a distributed adaptive control strategy based on vehicle-to-vehicle (V2V) communication is proposed in this paper. Additionally, Lyapunov functions and the uniformly ultimately bounded stability theorem are used to demonstrate the stability of the closed-loop systems, and the effectiveness of the control strategy is demonstrated by a simulation example.

Stealthy Attack Against Distributed State Estimation for Cyber‐Physical Systems

ABSTRACTThis article studies a stealthy attack strategy design for distributed state estimation from the perspective of an attacker in cyber‐physical systems, where both internal and external attack scenarios are considered simultaneously. To enhance robustness against outliers and reduce communication burden, a distributed fusion estimator is developed by fusing the innovation residuals of neighboring smart sensor nodes without any attack. Based on the designed distributed fusion estimator, the evolution of the distributed estimation error covariance is analyzed, and its lower and upper bounds are obtained. Moreover, a stealthy attack framework embedding adjustable parameter is designed to weaken the estimation performance, where the constraints of the adjustable parameter are provided based on the desired attack effect. Finally, a simulation example is provided to manifest the validity of the main results.

Signed-network-based consensus control for nonlinear multi-agent systems: a dynamic encryption–decryption approach

Abstract This paper is concerned with the bipartite secure control issue for a class of discrete-time nonlinear multi-agent systems under a dynamic encryption–decryption approach. First, the coupling relationships between agents are portrayed through a signed graph topology containing the edges of positive and negative connection weights. Next, the information transmissions between agents and their neighbors are executed via a shared communication network. The dynamic encryption–decryption approach is implemented to transform original signals into ciphertexts. Under the assumption that the signed graph is structurally balanced, a signed-network-based bipartite secure controller is designed to achieve the desired state control for the nonlinear multi-agent system and sufficient conditions are obtained for the existence of the desired signed-network-based state controller. Furthermore, a precise definition of the minimum channel capacity is proposed to understand the factors affecting the minimum channel capacity. Finally, the effectiveness of the proposed approach is illustrated by two simulation examples.

Robust Control for Multiplayer Nonzero‐Sum Differential Games With Switching Topology and Input Delay

ABSTRACTThis article investigates a multiplayer nonzero‐sum differential game (MNDG) with switching topology and input delay. The Hamilton–Jacobi–Isaacs (HJI) equation is used to derive the optimal control strategies. To solve the issue of switching topology, the control strategies are decoupled with the communication topology. Then, the stability of the system can be achieved regardless of changes in communication topology with the designed control strategies. This does not require a restriction on the dwell time, making the system stability conditions more relaxed. The Lyapunov–Krasovskii functional (LKF) is constructed to prove the stability of the system in the presence of time‐varying input delay. To cope with the real‐world situation, the input delay is considered to be disturbed by a time‐varying perturbation in this article. With these treatments, the system is more in line with real‐world applications. Simulation examples are given to validate the efficiency of the presented methods.

Conditional sufficient variable selection with prior information

AbstractDimension reduction and variable selection play crucial roles in high-dimensional data analysis. Numerous existing methods have been demonstrated to attain either or both of these goals. The Minimum Average Variance Estimation (MAVE) method and its variants are effective approaches to estimate directions on the Central Mean Subspace (CMS). The Sparse Minimum Average Variance Estimation (SMAVE) combines the concepts of sufficient dimension reduction and variable selection and has been demonstrated to exhaustively estimate CMS while simultaneously selecting informative variables using LASSO without assuming any specific model or distribution on the predictor variables. In many applications, however, researchers typically possess prior knowledge for a set of predictors that is associated with response. In the presence of a known set of variables, the conditional contribution of additional predictors provides a natural evaluation of the relative importance. Based on this concept, we propose the Conditional Sparse Minimum Average Variance Estimation (CSMAVE) method. By utilizing prior information and creating a meaningful conditioning set for SMAVE, we intend to select variables that will result in a more parsimonious model and a more accurate interpretation than SMAVE. We evaluate our strategy by analyzing simulation examples and comparing them to the SMAVE method. And a real-world dataset validates the applicability and efficiency of our method.

Self-triggered consensus resilient control for multi-agent systems against sensor deception attacks based on a single parameter learning method

This paper presents a self-triggered consensus resilient control method for nonlinear multi-agent systems (MASs) under sensor deception attacks. A single parameter learning method is integrated into backstepping technique to simplify design procedure. The neural networks are utilized to compensate for unknown dynamics of the MASs. Moreover, a self-triggered mechanism is presented for MASs to refrain from continuously monitoring triggering conditions and conserve communication resources. The designed controller can resist sensor deception attacks and guarantee that all signals of the MASs are uniformly bounded. An expository simulation example reveals the virtue of the presented method.

Enhanced Looped Lyapunov Functional for Sampled-Data Control for T-S Fuzzy Systems with Time Delay

This paper addresses the problem of sampled-data control in T-S fuzzy systems with time delays. Initially, a negative-definite criterion is developed for the matrix containing membership functions with the help of the idea of a switching mechanism. Building upon this criterion, a membership-function-based looped Lyapunov functional is introduced. Furthermore, a sampling-time-dependent looped Lyapunov functional is constructed, incorporating crucial information about the sampling instance. Notably, this functional matrix is not constrained to satisfy positive-definiteness, and it varies with each sampling point. By utilizing the enhanced looped Lyapunov functional, less conservative stability conditions and a new controller criterion for the T-S fuzzy system with time delays are derived. This demonstrates that our proposed stability conditions allow for a larger sampling interval compared to existing work. Finally, the proposed method’s effectiveness is demonstrated through the validation of two simulation examples.

Simultaneous fault-tolerant consensus control and disturbance suppression for multi-agent systems with polynomial form fault under switching topology

For continuous-time linear leader-follower multi-agent systems with polynomial faults, the problem of fault-tolerant state consensus under switching topologies is studied. firstly, the average dwell time method is introduced, and observers are designed for different topologies to observe faults and disturbances. Secondly, according to the observed estimated value, the corresponding fault-tolerant control consensus protocol is proposed considering the switching situation of the communication topology. Finally, both theoretical analysis and simulation examples verify that the proposed design scheme can compensate faults and disturbance rejection.