The issue of mean-square delayed consensus of nonlinear multi-agent systems (NMASs) suffered from denial-of-service (DoS) attacks under Markov switching topologies and dynamic event-triggered control (DETC) is studied. Firstly, the networks among agents are inevitably attacked because the communication networks are open access. DoS attacks that are destructive, stealthy and easy to realize are considered. Secondly, DoS attacks can result in random changes of the communication topologies, which are assumed to be uncertain nonhomogeneous Markov switching (UNMS) topologies with partially unknown transition rates (TRs). Then, in order to reduce unnecessary signal transmission, the adaptive control law and DETC are adopted. The sufficient conditions of mean-square delayed consensus of NMASs in term of random analysis method and distributed control theory are explored. Finally, the exactness of the results and the effectiveness of the methods are validated by the example given.

When a parameterized dynamical system is not (structurally locally) identifiable, it is crucial to reparameterize the system to ensure that the new parameters can be uniquely determined, at least locally. In the article1, Theorem 2 claims the existence of an identifiable reparameterization for a parameterized analytic function under specified conditions. We first give a counter-example to show that its conditions are indeed incomplete. Next, to address its incompleteness, we will propose a modified version of the theorem.

This paper studies the practical fixed-time consensus (Fd-TC) for uncertain multi-agent systems (MASs) under strongly connected directed graphs utilizing adaptive event/self-triggered controllers, respectively. Considering the uncertainty in the agent’s nonlinear dynamic parameters, the adaptive event-triggered controller is designed by incorporating an event-triggered strategy with an adaptive control scheme, which achieves the practical Fd-TC for uncertain nonlinear MASs and the settling time remains constant irrespective of the initial conditions. Moreover, the designed controller is independent of any global information, including communication topology and network size. It also employs the hyperbolic tangent function to tackle Zeno and nondifferential issue. Following this, the adaptive event-triggered controller serves as the foundation for introducing a self-triggered controller, which eliminates the necessity for continuous monitoring and guarantees practical Fd-TC. Furthermore, the results are extended to a more generalized case with uncertain disturbances, highlighting the adaptability of the designed controller. Finally, two numerical simulation examples are conducted to validate the efficacy of the developed controllers.

This paper addresses the identification of Nonlinear (NL) systems using a linearization approach, introducing a combined estimator to tackle this challenge. We assume that the unknown NL system operates around a stable, slowly varying operating point. The system trajectory is then perturbed slightly via small, typically fast, input perturbations. We demonstrate that the NL system’s response to these small perturbations can be approximated by a Linear Parameter-Varying (LPV) system model. Furthermore, we show that this LPV model represents the linearized version of the unknown NL system around the operating point. A new parametrization for the LPV model coefficients is introduced, establishing a structural relationship between the LPV coefficients. This structural relationship reduces the number of parameters to be estimated and ensures that the LPV model always corresponds to the linearized form of the NL system. Additionally, we demonstrate that this LPV model structure allows for the unique reconstruction of the NL system model through symbolic integration, resulting in a closed-form nonlinear Ordinary Differential Equation (ODE). This integration introduces a second structural relationship, linking the LPV model to the NL model. By leveraging these two structural relationships, we reformulate the problem of NL system identification via linearization as a combined estimation problem, leading to a unified LPV-NL estimation framework. This approach utilizes all available data, including perturbation data (linear response) and the varying operating point (NL response). We propose a combined estimator that jointly estimates the NL-LPV model, capturing the intrinsic structure of NL system identification through linearization. Finally, we present a numerical example to illustrate the performance of the proposed method.

Owing to the bilinear nature of robust performance conditions, it remains a challenge to effectively design a controller for parametric systems. To overcome this difficulty, we establish a metaheuristic-based design framework in this paper. This framework includes a simple initialization method, detailed search flows, and the associated objective functions for each step. In addition, this method can individually and easily shape the gain characteristics of closed-loop transfer functions, thus lowering the hurdle of control design for complex and uncertain systems. The whole design procedure is validated and illustrated through its application to a drivetrain bench. Numerous trials show that on average a success rate of 70% is achieved in the search for the controller.

Parameter uncertainty is the most frequently encountered model uncertainty. Although the research on the robust control of parametric systems has a long history, existing design tools are still either conservative or not numerically efficient, particularly for the performance problems. This paper treats polytopic systems which have good compatibility with physical systems. It is shown that less conservative robustness conditions can be derived from the well-known Lagrange method by treating the performance specification as an objective function in a dilated signal space and regarding the dynamics as a hyperplane in this space. A broad class of frequency domain specifications and regional pole-placement are analyzed in detail. Desirable multiplier structures are also revealed through numerical analysis. The results lay a solid foundation for an effective robust performance design of the polytopic systems.

The stochastic approximation based control law has been proved to be a powerful tool to achieve the robust distributed coordinated control for multi-agent systems (MASs) with uncertain disturbances in fixed or balanced time-varying network. However, its effectiveness proof in unbalanced time-varying networks is not well solved. The main contribution of this paper is solving this problem in two typical coordinated control problems based on a time-varying quadratic Lyapunov function. Firstly, the stochastic approximation for consensus problem of discrete-time single-integrator MASs with additive noises is studied. We build the weak consensus, mean square and almost sure consensus conclusion under the assumption that the time-varying network is uniformly strongly connected (USC) by adopting the stochastic approximation based consensus protocol. The convergence rate of the weak consensus is also quantified. Secondly, the stochastic approximation for formation control problem of MASs with relative-position information in the plane as an application of the built consensus conclusion is studied. We show that the stochastic approximation based formation control law can be used to achieve the desired formation for MASs if the network is USC. We finally give numerical simulations to verify the correctness of the conclusion.

This paper focuses on privacy-preserving distributed convex optimization across directed graphs within a prescribed time. To reduce the communication cost and achieve fast convergence, we propose a novel event-triggered and prescribed-time convergent distributed optimization algorithm built upon an extended Zero-Gradient-Sum method with free initialization. Specifically, we formulate event-triggering conditions for each agent, ensuring that inter-agent communication occurs solely upon meeting these conditions, thus significantly reducing communication costs. By the Lyapunov stability theory, the proposed algorithm is proven to achieve an accurate convergence to the optima within a prescribed time. Moreover, we establish the absence of Zeno behavior throughout any arbitrary period except the specified convergence time. When the environment exists eavesdropping attacks, we further provide a privacy-preserving prescribed-time event-triggered distributed algorithm based on state and objective decomposition. Finally, two comprehensive simulations demonstrate the performance of our proposed algorithm.

With an increasing focus on both performance and efficiency, UAV control systems have made great progress. In this work, we extend a previous LPV-MPC technique by including a more simplified control system for UAVs. The proposed control method removes the requirement for a velocity controller by concentrating on improving MPC performance using Particle Swarm Optimization (PSO) of the weight matrices. This system uses PSO to balance control effort with trajectory ac-curacy and simplifies the control architecture, hence improving UAV control efficiency. It is especially appropriate for situations where simplicity and energy economy are absolutely important.

This article investigates the stochastic stability of stochastic differential delay systems (SDDSs) with path information and their applications in consensus control of multi-agent systems (MASs) based on the path information feedback. Here, the integral path information and fragment-integral path information are considered respectively. The mean square (m.s.) and almost sure (a.s.) exponential stability criteria of the SDDSs with path integral information are established respectively according to the two types of path information. It is shown that the fragment-integral term may work positively for stochastic stability. Moreover, the obtained stochastic stability theorems are applied to design a distributed proportional integral/ fragment-integral control protocol and establish consensus conditions for stochastic MASs under proportional-integral (PI) -type controls. Finally, the effectiveness of the results is verified through two simulation examples.

This work is absorbed in the neighboring mode-dependent event-triggered control scheme for Markov jump systems with unknown interconnections. A dynamic event-triggered mechanism composed of neighboring modes parameters is provided to save communication resources and reduce the number of solution parameters. Distinguished from existing version, the global operating modes are required to be unknown for each local subsystem. Resorting to neighboring modes information, an event-triggered state feedback controller is established to assure system stability. The cyclic-small-gain criterion is used to tackle the unknown interconnections so that novel stability criteria are obtained for the closed-loop systems with H ∞ performance. Finally, an illustrative example is employed to confirm the proposed method in the aspect of validity.

This paper presents a controller based on Model-assisted Extended State Observer, aiming to address the challenge of suppressing parametric disturbances in non-minimum phase systems. By demonstrating the existence of equivalent input disturbance in the system with multiple input channels, it reveals the rationality of the proposed controller and the physical significance of the extended state. This paper derives the characteristic polynomial of the closed-loop system and analyzes the applicability of the proposed controller in two specific conditions. Through two representative case studies, it further substantiates the superiority of the proposed controller over the Full-order Extended State Observer-based control. The introduction of model-assisted techniques effectively resolves the issue of inherent input-parameter disturbances which is in the Full-order Extended State Observer-based control, providing non-minimum phase systems with an enhanced capability to suppress parametric disturbances.

In this work, a novel neural-adaptive nonlinear delay stochastic filter is designed to address the attitude estimation problem. This filter is represented using the special orthogonal group SO ( 3 ) , and employs low-cost sensors’ units. Specifically, the Brownian motion is introduced to characterize the noise that maps the system dynamics to stochastic differential equations (SDEs), ensuring that the attitude estimation problem can be analyzed in a stochastic sense. Neural networks (NNs) are employed to design an attitude filter that accounts for sensor measurement delay and noise by incorporating a Lyapunov function. This stochastic filter design ensures that the closed-loop system is almost semi-globally uniformly ultimately bounded (SGUUB) in the mean square sense. Finally, simulations to verify the efficiency of the proposed attitude filter.

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This paper focuses on the issue of adaptive event-triggered leader-follower consensus for multi-agents systems with output constraints and dead-zone inputs. By introducing an advanced nonlinear mapping technique to obtain the unconstrained auxiliary variables of constrained system states, a new systems model without output constraints is constructed. Unlike existing schemes, the proposed strategy can be used in both constrained and unconstrained situations without requiring changes to the control structure. Moreover, a state estimator is constructed to observe the unavailable states. To conserve communication resources, an event-triggered rule with a dynamic threshold is designed to decrease superfluous information transmissions from the controller to the actuator. It is proven that all signals in closed-loop systems are ultimately bounded and the system output does not violate the given constraint range. At last, a numerical simulation example is provided to confirm the correctness and efficiency of the proposed method.

The semi-global stabilization of a class of cascade systems (e.g., partially linear composite systems) is investigated via partial state feedback. The system comprises a nonlinear subsystem with a cross-term and a linear subsystem in the Byrnes-Isidori normal form. The cross-term that involves any two consecutive states of chains of integrators is incorporated into the nonlinear subsystem. Based on the established lemma for separate design, the semi-global stabilization problem for the entire composite system is reduced to stabilizing its linear subsystem subject to non-peaking constraints on the consecutive states. To address the later problem, a linear low-and-high gain feedback law is developed in the backstepping manner, which can be recognized as partial state feedback for the composite system as it uses only the states of the linear subsystem.

This paper tackles the challenge of designing a memory-dynamic event-triggered output feedback controller for cyber-physical systems (CPSs) under distributed denial of service (DDoS) attacks. First, we consider the impact of hybrid time delays on communication networks and focus on DDoS attacks that can continuously paralyze network. Second, with limited bandwidth communication resources, a memory-dynamic event-triggered scheme (MDETS) that efficiently utilizes the bandwidth resources as well as mitigates the network-induced phenomena is proposed. In addition, to ensure the safe operation of CPSs, an output feedback controller with memory is designed based on time delay states in the measurement channel. Then, selecting the proper multi-area Lyapunov-Krasovskii functionals (LKFs), criteria for sufficient exponentially mean-square H ∞ stability condition is formulated. Finally, an illustrative example is used to demonstrate the effectiveness and applicability of the designed strategies.

With the vigorous development of the electric vehicle (EV) industry, the demand for charging has surged. However, the relative lag in the construction of charging infrastructure has led to a series of problems for drivers, such as difficulty in finding available charging stations and long waiting times for charging. To address this, this paper proposes an EV charging guidance framework based on an Informer network and Multi-Agent Reinforcement Learning (MARL), aiming at achieving efficient EV charging guidance. Firstly, this paper regards charging stations as independent agents, integrating information from vehicles, charging stations, and traffic, transforming the multi-objective optimization problem of EV charging guidance into a multi-agent reinforcement learning task. Then, an Actor-Critic algorithm combined with the Informer is designed, utilizing the Informer in the Critic network to model the interactions between different charging stations, thereby reducing the complexity of policy learning and enhancing coordination among agents. Subsequently, after calculating the advantage function for the agents, the Actor network is updated to improve learning efficiency. The proposed algorithm was simulated and validated in two different EV charging scenarios. The simulation results show that compared with several state-of-the-art methods, our algorithm achieved the best results in multi-objective optimization, demonstrating its superiority and practicality of our proposed algorithm.