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.

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.

Unmanned Aerial Vehicles (UAVs) are expected to fully exploit their maneuvering capabilities to conduct aggressive maneuvers to complete tasks such as racing and aerobatics even in harsh environments. However, most of related works often focus on slow movements only suitable for regular tasks in well-structured environments. This motivates us to propose a highly robust control scheme designed specifically for UAV aggressive maneuvers, encoded by rapid and large changes in either position or attitude. The core of our approach is the data informed incremental dynamics and the quaternion facilitated control scheme. The former facilitates the model-free control that eliminates the requirement to model the complex dynamics at high velocities and accelerations accurately; While the latter avoids potential singularities during large angular changes. In particular, we first utilize one-step-backward data to construct incremental dynamics, a model-free representation of the UAV dynamics. Then, the constructed incremental dynamics serves as the basis for the development of both position-priority and attitude-priority control schemes, wherein the novel quaternion based aggressive maneuver tracking controllers are designed with complete theoretical analysis. The aggressive maneuvers such as roulette, barrel roll, multiple-flip and cobra maneuvers are chosen for tri/quad/hexa/octocopter platforms to evaluate the performance of our proposed position-priority and attitude-priority control schemes, during which the linear velocity up to 20 m/s, the angular changes up to 360 ◦.

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 proposes a three-step interval estimation method in the framework of optimization for discrete-time switched systems. The proposed method is realized based on observers design via reachability analysis and measurement update. In order to obtain interval estimation, a peak-to-peak index is used to improve the robustness caused by disturbances and noise. The calculation of zonotopes and boxes is used for reachability analysis. A new iterative algorithm is proposed to reduce computational complexity. For improving estimation accuracy, a method of 1-norm optimization is proposed, which can reduce the conservatism of computing intersections.

This paper discusses the design of an extremum seeking controller that relies on a monitoring function for a class of SISO uncertain nonlinear systems characterized by arbitrary and uncertain relative degree. Our demonstration illustrates the feasibility of achieving an arbitrarily small proximity to the desired optimal point through output feedback. The core concept involves integrating a monitoring function with a norm state observer for the unitary relative degree case and its expansion to arbitrary relative degrees by means of the employment of a time-scaling technique. Significantly, our proposed scheme attains the extremum of an unknown nonlinear mapping across the entire domain of initial conditions, ensuring global convergence and stability for the real-time optimization algorithm. Furthermore, we provide tuning rules to ensure convergence to the global maximum in the presence of local extrema. To validate the effectiveness of the proposed approach, we present a numerical example and apply it to a source-seeking problem involving a cart-track linear positioning servomechanism. Notably, the cart lacks the ability to sense its velocity or the source's position, but can detect the source of a light signal of unknown concentration field.

This paper introduces a novel approach for designing optimal control using data-driven Stochastic Game Theoretic Differential Dynamic Programming (SGT-DDP). The proposed method addresses unknown stochastic systems by approximating both drift and diffusion dynamics. The drift dynamics is estimated via Gaussian Process Regression (GPR) using input-output data. The diffusion dynamics is approximated from the noise data, which is extracted through subtracting the noisy output from the smoothed output. Subsequently, the binning method is combined with GPR to obtain the approximate model of the diffusion dynamics. These approximations are integrated into the SGT-DDP framework to compute optimal control policies. Simulations on benchmark nonlinear systems under unknown dynamics demonstrate the effectiveness of the method.

Recently, time-varying linear equality and inequality equations (TVLEIE) is becoming increasingly crucial for solving problems in various fields. Zeroing neural networks (ZNNs) can also be employed to address the TVLEIE. Generally, the design convergent parameters (DCPs) of ZNN schemes affect the convergent speed. Since the previous fixed-parameter ZNNs (FPZNNs) use fixed parameters, they are not suitable for real-world applications where parameters vary over time. Taking this into account, the varying-parameter ZNNs (VPZNNs) were introduced in this field. Although the VPZNNs surpass the FPZNNs, their DCPs typically continue to increase over time and can even become excessively large in the end. But extremely large parameters are unsuitable. Moreover, the increasing parameters can lead to wasted computing resources, even when the VPZNNs become convergent. According to these considerations, we put forward a novel varying-parameter ZNN (NVPZNN) scheme with prescribed-time (PT) convergence to address the TVLEIE. NVPZNN has the capability to adjust its DCPs to progressively converge to a constant once it achieves convergence within the prescribed time. Subsequently, the global and PT convergence of NVPZNNs and their upper bounds as well as stability are theoretically analyzed. In comparison to other ZNN schemes utilizing common activation functions (AFs), the NVPZNN schemes own faster convergent rate, shorter convergent time and superior stability. Numerical experiments are conducted to validate the effectiveness and advantages of the NVPZNN schemes. Moreover, the successful application of NVPZNN in UR5 Manipulator shows its reliability and industrial application value.

This work investigates three energy-shaping control approaches to address the trajectory-tracking problem for specific classes of underactuated mechanical systems. In particular, the notions of contractive systems and dynamic extensions are utilized to solve the trajectory-tracking problem while addressing implementation issues such as the lack of velocity sensors and the presence of constant disturbances. To this end, a first tracking controller is developed without the need for velocity measurements. A second controller is introduced, solving the trajectory-tracking problem while rejecting matched constant disturbances. Then, a third approach establishes conditions to combine both mentioned controllers. It is shown that the proposed design methods guarantee exponential convergence of closed-loop systems for specific classes of underactuated mechanical systems. The third method is illustrated with an example.

The article proposes a novel solution to the control problem of centrifugal gas compressors which are driven by three-phase induction motors (IMs) and three-phase permanent magnet synchronous motors (PMSMs), through a novel flatness-based control scheme which is implemented in successive loops. By re-arranging state variables and by splitting suitably the state vector of the IM-driven gas-compressor and of the PMSM-driven gas compressor into subsystems one arrives at writing the associated state-space models in the triangular (strict feedback) form. For the latter state-space description it is possible to solve the control and stabilization problem using chained control loops. The state-space model of the IM-driven gas-compressor and of the PMSM-driven gas-compressor is decomposed into cascading subsystems which satisfy differential flatness properties. For these subsystems virtual control inputs are computed, capable of inverting their dynamics and of eliminating the associated tracking error. The control inputs which are actually applied to the complete nonlinear form of the IM-actuated gas-compressor and of the PMSM-actuated gas compressor are computed from the last subsystem of the chained state-space description. These control inputs incorporate in a recursive manner all virtual control inputs which were computed from the individual subsystems included in the initial state-space equation. The control inputs that should be applied to the nonlinear system so as to assure that all state vector elements will converge to the desirable setpoints are obtained at each iteration of the control algorithm by tracing backwards the subsystems of the chained state-space model. The method is of proven global stability and ensures fast and accurate tracking of the reference setpoints by the state variables of the IM-driven gas compressor and of the PMSM-driven gas compressor.

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.

We review some known bounds for eigenvalues of matrices and use similar techniques to derive bounds for nonlinear eigen problems and the eigenvalues for LTI systems with delays as a special case. There are two classes of results. The first are based on Hermitian decompositions, the second on Gershgorin's theorem. The bounds are easily computable. We reflect on implications for stability theory, which may be contrasted with bounds that have been obtained via Riccati stability based on Lyapunov-Krasovskii theory.

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.

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.

In this paper, the stability and positive stabilization issues are addressed for discrete-time impulsive switched positive linear systems (DISPLSs) with a novel extended-sequence-pair-dependent (ESPD) hybrid signal. The combination of some common switching and impulsive signals can be seen as special cases of ESPD hybrid signal, such as the arbitrary (dwell time) switching signal and non-periodic (periodic) impulsive signals. Firstly, several necessary and sufficient stability conditions are given for DISPLSs with ESPD hybrid signal. Secondly, based on the non-conservative stability criteria, several necessary and sufficient positive stabilization conditions are presented. Thirdly, the results are extended to the time-delayed cases. Non-conservative stability and stabilization conditions for DISPLSs with multiple time delays are presented. All the results are given in terms of linear programming (LP). Finally, the advantages of the results over the literature are illustrated within four examples.

This paper introduces a novel nonlinear model predictive control (NMPC) framework that incorporates a lifting technique to enhance control performance for nonlinear systems. While the lifting technique has been widely employed in linear systems to capture intersample behaviour, their application to nonlinear systems remains unexplored. We address this gap by formulating an NMPC scheme that combines fast-sample fast-hold (FSFH) approximations and numerical methods to approximate system dynamics and cost functions. The proposed approach is validated through two case studies: the Van der Pol oscillator and the inverted pendulum on a cart. Simulation results demonstrate that the lifted NMPC outperforms conventional NMPC in terms of reduced settling time and improved control accuracy. These findings underscore the potential of the lifting-based NMPC for efficient control of nonlinear systems, offering a practical solution for real-time applications.

This paper investigates the prescribed-time output consensus (PTOC) of heterogeneous multi-agent systems (MASs) based on sampled data. Firstly, a novel dynamic compensator is designed for each agent and its state is used to interact with its neighbors. Then, a hybrid sampling strategy (HSS), including a dynamic event-triggered sampling (ETS) and a time-triggered sampling (TTS), is developed to determine when to sample and broadcast the compensator state of agents. The execution of the HSS is divided into two steps, a dynamic ETS is presented before the first prescribed-time (PT) where the dynamic threshold decays to zero as time approaches the first PT. Subsequently, a TTS with a constant sampling period (CSP) is introduced after the first PT. With the proposed HSS, all compensators achieve the state consensus at the first PT while excluding the Zeno behavior. Based on the proposed dynamic compensators and the HSS, a fully distributed controller with high scalability and flexibility is developed. Through the Lyapunov stability theory, it is proved that the heterogeneous MASs with the proposed controller achieve the output consensus at the second PT (after the first PT). Finally, a simulation example on 8-wheeled mobile robots is performed to verify the validity of the theoretical results.

This paper addresses the output consensus problem for heterogeneous multi-agent systems (HMAS) with Denial-of-Service (DoS) attacks under stochastic switching topologies. The stochastic switching communication network among agents is modeled as a time-varying directed graph governed by Bernoulli process. Taking into account the weight of the historical triggering data and corresponding triggering error, a novel dynamic memory event-triggered mechanism (DMETM) is proposed and it can reduce the number of event-triggered to realize significant communication savings. Consider the impact of DoS attacks, a distributed local full-state feedback controller is designed to ensure that the output state of the followers can track the leader. Rely on the Lyapunov theory, it is certificated that output consensus for HMAS can be guaranteed. Finally, the effectiveness and progressiveness of the proposed control scheme are verified by a simulation example.