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 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.

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.

A document by Zhaoping Du. Click on the document to view its contents.

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.

In this paper, the prescribed-time fault-tolerant control for multiple input multiple output (MIMO) linear time-varying (LTV) systems is investigated. Both uncertain lexicographically fixed and non-lexicographically fixed LTV systems with precisely known models have been considered. By leveraging the special structure of controllable canonical form and employing the properties of the parametric Lyapunov equation, a global prescribed-time fault-tolerant controller is designed. By choosing a suitable Lyapunov-like function, it will be demonstrated that the state converges to zero within the designated time. In addition, the designed controller is bounded and maintains a linear, concise and smooth form. Ultimately, the effectiveness of the controller is verified through the simulation of the elliptical orbital rendezvous system.

This paper investigates a load frequency control issue for multi-area interconnected unknown power systems with random communication delays. First, a model-free adaptive control scheme is established by building an equivalent data relationship model between area control error and corresponding control input. Then, a dynamic event-triggered scheme is designed to improve resource utilization and reduce computational burden. Furthermore, random communication delays in the feedback and forward channels are considered. The results show that the proposed method is independent of any model information about the power system, only using the controlled system's control input and output data. Several simulation results validate the effectiveness of the proposed control scheme.

This article is focused on the design of an LMI-based observer for the class of disturbance-affected nonlinear systems. Two novel LMI conditions are derived by deploying a more general form of the matrix multiplier compared to the one used in the literature. The first method is based on the use of the H ∞ criterion, while the second one utilises an ISS notion. Both LMIs are developed by employing the reformulated Lipschitz property, a well-known LPV approach and the new variant of Young inequality. The key element of the proposed LMI conditions is the incorporation of the novel matrix multipliers which allow us to include some additional decision variables as compared to the methods proposed in the literature. These additional variables add extra degrees of freedom, thus enhancing the LMI feasibility. Furthermore, the effectiveness of the proposed methodologies is showcased through a numerical example.

This paper studies the tracking problem for a class of nonlinearly parameterized multi-agent systems with the powers being unknown time-varying functions. The essence of the multi-agent systems and the serious uncertainties distinguish the question in this paper from the existing works. By combing the algebraic graph theory, adding a power integrator method and adaptive technique, a new distributed adaptive tracking controller is presented with a dynamic estimating the unknown parameters, which guarantees that both the system state and the control input are globally bounded and meanwhile, the tracking error can be adjusted to arbitrarily small. Finally, we offer a simulation to interpret the validity of the control scheme design.

Steering a nonlinear system from one state to a desired one is a common task in control. While a nominal trajectory can be obtained rather systematically using a model, for example via numerical optimization, heuristics, or reinforcement learning, the design of a computationally fast and reliable feedback control law that guarantees robust asymptotic stability of the found trajectory can be much more involved. An approach that does not require high online computational power and is well-accepted in the industry is gain-scheduling. The results presented here pertain to the stability guarantees and the region of attraction of gain scheduled control laws, based on subsequent linearizations along the reference trajectory. The approach bounds the uncertainty arising from the linearization process, builds polytopic sets of linear time varying systems covering the nonlinear dynamics along the trajectory, and exploits sufficient conditions for robust stability to attempt the derivation of the desired gain-scheduled controller, via the solution of Linear Matrix Inequalities (LMIs). A result to estimate an ellipsoidal region of attraction is provided too. Moreover, arbitrary scheduling strategies between the control gains are considered in the analysis, and the method can be used also to check/assess the stability properties obtained with an existing gain-scheduled law. The approach is demonstrated experimentally on a small quadcopter as well as in simulation to design a scheduled controller for a chemical reactor model and to validate an existing control law for a gantry crane model.

Microgrids are a development trend and have attracted a lot of attention worldwide. The control system plays a crucial role in implementing these systems and, due to their complexity, artificial intelligence techniques represent some enabling technologies for their future development and success. In this paper, we propose a novel formulation of an economic model predictive control (economic MPC) applied to a microgrid designed for a faculty building with the inclusion of a predictive model to deal with the energy demand disturbance using an artificial neural network (ANN). First, we develop a framework to identify an ANN using historical data registered by a smart three-phase power quality analyzer to provide feedforward power demand predictions. Next, we present an economic MPC formulation that includes the prediction model for the disturbance within the optimization problem to be solved by the MPC strategy. We carried out simulations with different scenarios of energy consumption, available resources and simulation times to highlight the results obtained and analyze the performance of the energy management system. In all cases, we observed the correct operation of the proposed control scheme, complying at all times with the objectives and operational restrictions imposed on the system.

Real-world dynamical control systems are often subject to input saturation due to the physical constraints of actuators. This causes the response performance of the system to be reduced due to the effect of input saturation. To this end, we propose a finite-time performance enhanced bounded control (FTPE-BC) method based on sliding mode control for finite time tracking of linear systems with input saturation. In this method, an approach rate based on inverse tangent function is proposed to make the system converge in finite time. A bounded controller based on sliding mode control is proposed, in which two control parameters are introduced to reduce steady-state error, settling time and overshoot. Meanwhile, the finite-time stability of the system is proved in a domain of attraction. The parameters selection principle of the controller is given to improve the transient and steady-state performance of the closed-loop system. The simulation results verify the superiority of the proposed method in the transient and steady-state performance enhancement.

The problem of fixed-time formation tracking for multi-spacecraft systems without internal collisions is investigated in this paper. A novel adaptive immersion and invariance (I&I)-based control protocol is designed to solve this technical problem, with the goal of driving formation members to accurately realize and maintain the required configuration within the user-given time. The novelty here lies in two things. First and foremost, unlike the asymptotic convergence of the traditional I&I related works, the proposed protocol guarantees the fixed-time stability by integrating the prescribed performance control. Secondly, the event-triggered mechanism is adopted to alleviate the pressure of communication resources between formation members and reduce unnecessary information interaction. Lyapunov stability analysis shows that the proposed protocol can enable the defined implicit manifold to converge to the origin for most initial conditions. Also, benefiting from the prescribed performance techniques, the convergence time eliminates the dependence of designed controller parameters or initial system conditions, relying only on the actual mission requirements. In addition, we adopt a linear extended state observer to deal with the parameter uncertainties and external disturbances, and use the I&I adaption to estimate the observer errors to further improve the system performance. Moreover, a new exponential-type artificial potential function is designed to avoid close proximity between formation members and prevent internal collisions. Finally, numerical simulations are provided to verify the theoretical results.

In this article, we study an unified resilient asymmetric bipartite consensus (URABC) problem for nonlinear multi-agent systems with both cooperative and antagonistic interactions under denial-of-service (DoS) attacks. We first prove that the URABC problem is solved by stabilizing the neighborhood asymmetric bipartite consensus error. Then, we develop a distributed compact form dynamic linearization method to linearize the neighborhood asymmetric bipartite consensus error. By using an attack compensation mechanism to eliminate the adverse effects of DoS attacks and an extended discrete state observer to enhance the robustness against unknown dynamics, we finally propose a distributed resilient model-free adaptive control algorithm to solve the URABC problem. A numerical example validates the proposed results.

A document by Gargi Chakraborty. Click on the document to view its contents.