In this paper, we discuss how to synthesize stabilizing Model Predictive Control (MPC) algorithms based on convexly parameterized Integral Quadratic Constraints (IQCs), with the aid of general multipliers. Specifically, we consider Lur’e systems subject to sector-bounded and slope-restricted nonlinearities. As the main novelty, we introduce point-wise IQCs with storage in order to accordingly generate the MPC terminal ingredients, thus enabling closed-loop stability, strict dissipativity with regard to the nonlinear feedback, and recursive feasibility of the optimization. Specifically, we consider formulations involving both static and dynamic multipliers, and provide corresponding algorithms for the synthesis procedures. The major benefit of the proposed approach resides in the flexibility of the IQC framework, which is capable to deal with many classes of uncertainties and nonlinearities. Moreover, for the considered class of nonlinearities, our method yields larger regions of attraction of the synthesized predictive controllers (with reduced conservatism) if compared to the standard approach to deal with sector constraints from the literature.

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.

The paper focuses on the problem of formal synthesis of controllers for control-affine nonlinear systems against complex properties. Our goal is to design a closed-form control policy that guarantees the satisfaction of complex properties that are expressed using ( ω)-regular languages and equivalently recognized by Non-deterministic Büchi Automata (NBA). We propose leveraging a funnel-based control approach to provide a closed-form solution to the problem. Our approach decomposes the specification represented by NBA into a sequence of reachability problems which we solve using a funnel-based control approach. Controllers associated with each reachability problem are then combined to design a hybrid control policy enforcing the desired ( ω)-regular property. We demonstrate the effectiveness of the proposed results on room temperature control and mobile robot motion control case studies.

In this paper, by considering the existence of unavailable state, external disturbance, and denial of service (DoS) attacks, an anti-disturbance trajectory tracking controller is proposed for the air-ground system composed of unmanned autonomous helicopter (UAH) and unmanned ground vehicle (UGV). Initially, by combining the advantages of conventional event-triggered scheme (ETS) and memory ETS, a switching-like event-triggered mechanism is put forward, which can cause less data transmissions without reducing control performance, and effectively restrain the DoS attacks. Secondly, by dividing the DoS attacks into active intervals and sleep ones, the concept of acknowledgement character technology (ACK) is presented to determine the suitable type of time intervals. Thirdly, the switching-like dynamic ETS is introduced for the sleep intervals of DoS attacks while the ETS with fixed triggered-threshold is exploited for the active intervals. Fourthly, based on the occurrence of the DoS attacks, a switching-like observer and a normal one are respectively proposed to estimate the UGV state, the UAH state, the reference input of the UGV, and the disturbance of the UAH, which are utilized to design the anti-disturbance tracking controller. Fifthly, an augmented closed-loop system consisting of observation errors and tracking error is established and its stability is analyzed by using Lyapunov stability theory. Then, a sufficient condition on co-designing the parameters of switching-like ETS, observers, and tracking controller is presented in terms of linear matrix inequalities (LMI). Finally, the validity and superiority of the proposed control scheme are verified by resorting to simulations and comparisons.

Health monitoring is critical for the maintenance and risk management of reinforced concrete (RC) structures. In this paper, a robust adaptive Kalman filter is proposed for an interstory drift estimation problem to show the health condition of RC structures in the case that the statistics or internal dynamics describing the signals and measurements are not known precisely. More precisely, we build an adaptive current Jerk model (ACJM) where the model parameters are updated in each time step to presuppose the statistics characterization of the RC dynamic, while the unknown measurement noise covariance is adapted based on a fixed-lag innovation with respect to measurements. Moreover, a robust adaptive Kalman filter is designed for the modeling mismatch in each time increment by solving a minimax game: one “hostile” player tries to select a worst model far from the proposed ACJM with an exponential decay tolerance, while an optimum filter is designed by minimizing the estimation error according to this worst model. Finally, some simulation and experimental results show the effectiveness of the proposed algorithm.

This paper is concerned with the stability analysis of the static output-feedback polynomial fuzzy-model-based (SOF PFMB) control systems through designing a novel membership grade integration (MGI) approach. The nonconvex problems of the SOF PFMB control systems are convexificated into the convex conditions by introducing block diagonal positive-definite Lyapunov matrix and nonsingular transformation matrix. We proposed a new approximated membership functions, i.e. Lagrange Membership Functions (LMFs) method, which can be introduced into the stabilization process to relieve the stability conservativeness results. The LMFs are general representations of piecewise-linear membership functions (PLMFs), which makes the number of stability conditions not limited by the number of sample points. In a fixed subdomain, arbitrary sample points can be employed by the LMFs method and achieve higher approximation capability by increasing more sample points, so that membership grades can be incorporated into the system analysis. Furthermore, a novel MGI approach including the information of premise variables and LMFs are proposed, which can make the stability conditions more relaxed. Finally, a simulation example is given to show the merits of the developed techniques.

This paper presents a Youla-Kucera based interpolation between a set of Linear Parameter-Varying (LPV) controllers, each one being a gain-scheduled of Linear Time-Invariant (LTI) controllers designed separately for different operating points. The gain-scheduling is achieved based on Youla-Kucera (YK) parameterization. A generalized LPV-YK control structure is designed to interpolate between various LPV controllers. The closed-loop system is proved to guarantee the quadratic stability for any continuous/discontinuous interpolating signals in terms of a set of Linear Matrix Inequalities (LMIs). The proposed method can help multi-variable and multi-objective systems to achieve high performances at different operating conditions and different critical situations regardless of the interpolation rate. A numerical example is simulated to show the importance of the proposed method to achieve different objectives for lateral control of autonomous vehicles. In addition, the approach has been tested on a real Renault ZOE vehicle to validate its real performance, and compare it with a standard polytopic LPV controller.

This paper studies the practical fixed-time average consensus problem for general continuous linear multi-agent systems under switching topologies. Firstly, a distributed fixed-time consensus control protocol based on dynamic event-triggered mechanism is designed by using the local information exchange between individual agents. The protocol introduces auxiliary dynamic variables, and the triggering condition changes in real time based on the dynamic variable obtained online, which can significantly reduce the number of triggering events, effectively decrease the energy dissipation of the system and the update frequency of the controller. Then, under the designed control protocol, the sufficient conditions for the multi-agent system to solve the practical fixed-time average consensus problem are given, and it is proved that there is no Zeno behavior in the system. Finally, the simulation results verify the effectiveness of the conclusions.

Objective: Hybrid cardiovascular mock circuits (HMC), designed for dynamic testing of Ventricular Assist Devices (VAD), offer physiologic accuracy by sequestering model complexity in silico and ease of construction by reducing number of model elements in vitro. Despite superior response time and precision, pneumatic actuation is avoided in HMCs due to nonlinear dynamics and noise. We tested the hypothesis that a HMC consisting of a variable elastance-driven numerical circuit coupled to a pneumo-hydraulic physical circuit can be controlled without linearizing system dynamics. Methods: Reference left ventricular and aortic pressures generated in silico were tracked, respectively, in in vitro preload and afterload reservoirs by controlling non-linear pneumatic dynamics using the Lyapunov stability criterion. A centrifugal pump, the speed (i.e. flow) of which was adjusted using PID control, was interposed between the reservoirs and mimicked the VAD under evaluation. The flow of a recirculating gear pump was controlled by the backstepping method to equalize reservoir fluid volumes by rejecting pressure and flow disturbances. Sensor noise was reduced with discrete-time Kalman filtering. Results: Our results showed that normal, failing and assisted cardiovascular physiologies were numerically simulated and tracked at physical VAD terminals with high accuracy. Reservoir volumes remained stable at various combinations of heart rate, pressure, and VAD flow. Conclusion: The HMC described here offers a stable performance testing platform for VAD prototypes. Significance : This is the first proof that hybrid systems using pneumatic actuation at hydraulic interfaces can optimally be regulated with nonlinear controllers to achieve precise reference tracking and robust disturbance rejection.

We develop a delay-adaptive controller for a class of first-order hyperbolic partial integro-differential equations (PIDEs) with an unknown input delay. By employing a transport PDE to represent delayed actuator states, the system is transformed into a transport partial differential equation (PDE) with unknown propagation speed cascaded with a PIDE. A parameter update law is designed using a Lyapunov argument and the infinite-dimensional backstepping technique to establish global stability results. Furthermore, the well-posedness of the closed-loop system is analyzed. Finally, the effectiveness of the proposed method was validated through numerical simulations.

In this paper, an event-triggered nearly optimal tracking control method is investigated for a class of uncertain nonlinear systems by integrating adaptive dynamic programming (ADP) and integral sliding mode (ISM) control. By introducing a neural network (NN) adaptive term, the designed ISM-based discontinuous control law is employed to eliminate the influence of the uncertainties and obtain the tracking error system constructed from the sliding mode dynamics, as well as relax the known upper-bounded condition of uncertainties. In order to guarantee the stability of tracking error system and improve the control performance, under the ADP technique, a critic NN is applied to approximate the optimal value function for solving the event-triggered Hamilton-Jacobi-Bellman equation and the event-triggered nearly optimal feedback control is obtained. The feedback control law is updated and transmitted to plant only when events occur, thus both the communication and the computational resources can be saved. Furthermore, the stability of tracking error is proven thanks to Lyapunov’s direct method. Finally, we provide two simulation examples to validate the developed control scheme.

For the nonlinear parameter-varying (NPV) model of unmanned surface vehicle (USV) with the consideration of the velocities on yaw and surge as well as wave disturbances, a robust H ∞ control method is proposed based on extended homogeneous polynomial Lyapunov function (EHPLF) to regulate heading for the superior performance on the rapidity, accuracy and robustness. Firstly, a NPV model of heading error is established to design a general form of a state feedback controller with a robust H ∞ performance. Secondly, a Lyapunov matrix with full states and varying parameter is constructed to derive the robust H ∞ global exponential stability conditions by Euler’s homogeneity relation for the NPV system, known as the EHPLF stability conditions. Thirdly, since the EHPLF stability conditions consist of a set of nonlinear coupled inequalities that cannot be directly solved by sum of squares (SOS) toolboxes, they are decoupled with matrix transformations to obtain the EHPLF-SOS stability conditions, which is solved for the parameters of the state feedback controller. Finally, the simulation results indicate that EHPLF method exhibits a superior performance on dynamic, steady-state and robustness.

The paper devises a H ∞ -norm theory for the CSVIU (control and state variations increase uncertainty) class of stochastic systems. This system model appeals to stochastic control problems to express the state evolution of a possibly nonlinear dynamic system restraint to poor modeling. Contrary to other H ∞ stochastic formulations that mimic deterministic models dealing with finite energy disturbances, the focus is on the H ∞ control with infinity energy disturbance signals. Thus, the approach portrays the persistent perturbations due to the environment more naturally. In this regard, it requires a refined connection between a suitable notion of stability and the systems’ energy or power finiteness. It delves into the control solution employing the relations between H ∞ optimization and differential games, connecting the worst-case stability analysis of CSVIU systems with a perturbed Lyapunov type of equation. The norm characterization relies on the optimal cost induced by the Min-Max control strategy. The rise of a pure saddle point is linked to the solvability of a modified Riccati-type equation in a form known as a generalized game-type Riccati equation, which yields the solution of the CSVIU dynamic game. The emerging optimal disturbance compensator produces inaction regions in the sense that, for sufficiently minor deviations from the model, the optimal action is constant or null in the face of the uncertainty involved. A numerical example illustrates the synthesis.

Under the framework of the step-function method, the stability of a nonlinear fuzzy hybrid control system combining an impulsive controller and a continuous state feedback controller is investigated. Both the two controllers are assumed to be subject to both actuator saturation and time-varying delays, which has received little attention if any, in the existing studies. A new assumption is established enabling the use of generalized sector conditions to tackle the double saturation, and the conservatism of the stability results is remarkably reduced thanks to the improved step-function method. The stability theorem proposed in this paper removes restriction on the time delays of both controllers, which can be also applied to wider scopes of systems, including hybrid control systems with both stabilizing and instabilizing impulses, systems with varying impulsive gain, and systems with Zeno behavior. Numerical simulations of stabilization for different systems by delayed saturated hybrid control have been conducted, which demonstrate the validity of proposed theorems.

This article researches the trajectory tracking problem for unmanned marine vehicles (UMVs) with disturbances and under denial-of-services (DoS) attacks in the wireless channel. By applying the partial form dynamic linearization algorithm, an equivalent data-driven model of the UMVs with ocean disturbances is firstly established. And the disturbances are estimated by using extended state observer, which improves the immunity of the UMVs to disturbances in the environment, and the robustness of the UMVs systems is better. It is the first time that the DoS attacks are considered under the data model for UMVs, and a novel data-driven adaptive trajectory tracking control framework is constructed. When the proposed equivalent data model suffers from DoS attacks which follows the Bernoulli distribution, an attack predictive compensation mechanism is devised to relieve the influence of DoS attacks. Based on it, the data-driven adaptive trajectory tracking controller is designed such that the error of trajectory tracking is convergent under DoS attacks and external disturbances. Finally, the effectiveness of the proposed data-driven control scheme and the predictive compensation mechanism is validated through the simulations.

This note concerns the problem of k-hop connectivity in a network of mobile agents, which is achieved if any pair of agents can communicate with each other through a link of k-1 or fewer intermediate nodes. We propose linear constraints involving binary optimization variables to ensure k-hop connectivity. Such constraints are then integrated into a Mixed-Integer Linear Programming (MILP) trajectory planning model. Simulation results illustrate the application of the proposed method and the effect of varying k in the context of a mission involving the visitation of multiple targets.

This article explores recursive algorithms for parameter identification issues of Hammerstein output-error systems. The proposed approach includes the key term separation auxiliary model recursive gradient algorithm, which utilizes the gradient search and the key term separation. To enhance computational efficiency, the system is decomposed into two or three subsystems through the hierarchical identification principle. Based on this, a key term separation auxiliary model two-stage recursive gradient algorithm and a key term separation auxiliary model three-stage recursive gradient algorithm are presented. The simulation results verify the validity of the obtained algorithms.

The fixed time event-triggered control for high-order nonlinear uncertain systems with time-varying state constraints is investigated in this paper. First, the event-triggered control (ETC) mechanism is introduced to reduce data transmission in the communication channel. In consideration of the physical constraints and engineering requirements, time-varying barrier Lyapunov function (BLF) is deployed to make the system states confined in the given time-varying constraints. Then, the radial basis function neural networks (RBF NNs) is used to approximate the unknown nonlinear terms. Further, the fixed time stability strategy is deployed to make the system achieve semiglobal practical fixed time stability (SPFTS) and the convergence time is independent of the initial conditions. Finally, the proposed control scheme is verified by two simulation examples.