Event-triggered group consensus for multi-agent systems subject to input saturation

This paper investigates group consensus for leaderless multi-agent systems with non-identical dynamics. The consensus protocol is put forward in the form of the distributed event-triggered control subject to saturation, which depends on information from neighboring agents at event-triggered instants. In order to exclude the Zeno behavior and save resources, the given event-triggered condition is detected only at discrete sampling times, where the sampling intervals can be variable. Based on the graph theory, Lyapunov–Krasovskii functional method and by adopting the free-weighting matrix technique, some sufficient group consensus criteria in terms of linear matrix inequalities are derived. Furthermore, optimization problems aiming at maximizing the event-triggered parameter and the consensus region are proposed. Finally, numerical simulations illustrate the effectiveness of the theoretical results.     

A simple structure robust attitude synchronization with input saturation

The attitude synchronization problem of multiple spacecraft is investigated in this paper. A simple cooperative control law, which can render spacecraft formation synchronized to a time-varying reference trajectory globally in the presence of model uncertainties, external disturbances and input saturation, is proposed. The globally asymptotic stability of the controller in the presence of model uncertainties and external disturbances is proven rigorously through a three-step proof technique. The controller can be used in orbit without modification due to its low computational complexity. Then, the proposed controller is extended to solve the consensus problem for multiple inertial agents with double-integrator dynamics. Finally, Numerical simulations are included to demonstrate the effectiveness of the developed controller.     

Robust quantized consensus of discrete multi-agent systems under input saturation

In this paper, we consider the quantized consensus problem of multiple discrete-time integrator agents which suffer from input saturation. As agents transmit state information through communication networks with limited bandwidth, the states of agents have to be quantized into a finite number of bits before transmission. To handle this quantized consensus problem, we introduce an internal time-varying saturation function into the controllers of all agents and ensure that the range of the state of each agent can be known in advance by its neighboring agents. Based on such shared state range information, we construct a quantized consensus protocol which implements a finite-bit quantization strategy to all states of agents and can guarantee the achievement of the asymptotic consensus under any given input saturation threshold. Such desired consensus can be guaranteed at as low bit rate as 1 bit per time step for each agent. Moreover, we can place an upper bound on the convergence rate of the consensus error of agents. We further improve that quantized consensus protocol to a robust version whose parameters are determined with only an upper bound on the number of agents and does not require any more global information of the inter-agent network. Simulations are done to confirm the effectiveness of our quantized consensus protocols.     

Active control for flexible mechanical systems with mixed deadzone-saturation input nonlinearities and output constraint

There exist mixed deadzone-saturation input nonlinearities and output constraint in the practical implementation environment for flexible mechanical systems, and they have crucial influences on the performance of flexible systems. In this paper, two class of flexible structures are investigated and analyzed by designing the active boundary vibration control with auxiliary systems. Based on the infinite dimensional dynamic model of flexible mechanical systems, the barrier logarithmic terms are brought into the Lyapunov function and boundary vibration control laws for maintaining the output signals within the constrained region. Besides, the auxiliary terms are designed in the control laws to compensate for mixed nonlinear inputs which integrate the deadzone and saturation characteristics. With the simulation results, the theoretical analysis for the flexible mechanical systems is verified to be correct and the designed control laws are effective.     

Bipartite output consensus for heterogeneous linear multi-agent systems with fully distributed protocol

This paper addresses the problem of bipartite output consensus of heterogeneous multi-agent systems over signed graphs. First, under the assumption that the sub-graph describing the communication topology among the agents is connected, a fully distributed protocol is provided to make the heterogeneous agents achieve bipartite output consensus. Then for the case that the topology graph has a directed spanning tree, a novel adaptive consensus protocol is designed, which also avoids using any global information. Each of these two protocols consists of a solution pair of the regulation equation and a homogeneous compensator. Numerical simulations show the effectiveness of the proposed approach.     

Semi-global leader-following coordination of multi-agent systems with input saturation and aperiodic intermittent communications

This paper addresses the semi-global leader-following coordination problem of general linear multi-agent systems, in which the control input of each agent is steered by aperiodically intermittent saturated actuator. Both the case with only one virtual leader and the case with multiple virtual leaders are discussed. By using multiple Lyapunov stability theory and applying algebraic Riccati equation-based low-gain feedback technique, sufficient conditions guaranteeing semi-global consensus tracking and semi-global containment tracking are provided. Numerical simulations finally verify the theoretical analysis.     

Command filtered robust adaptive NN control for a class of uncertain strict-feedback nonlinear systems under input saturation

This paper develops a robust adaptive neural network (NN) tracking control scheme for a class of strict-feedback nonlinear systems with unknown nonlinearities and unknown external disturbances under input saturation. The radial basis function NNs with minimal learning parameter (MLP) are employed to online approximate the uncertain system dynamics. The adaptive laws are designed to online update the upper bound of the norm of ideal NN weight vectors, and the sum of the bounds of NN approximation errors and external disturbances, respectively. An auxiliary dynamic system is constructed to generate the augmented error signals which are used to modify the adaptive laws for preventing the destructive action due to the input saturation. Moreover, the command filtering backstepping control method is utilized to overcome the shortcoming of dynamic surface control method, the tracking-differentiator-based control method, etc. Our proposed scheme is qualified for simultaneously dealing with the input saturation effect, the heavy computational burden and the “explosion of complexity” problems. Theoretical analysis illuminates that our scheme ensures the boundedness of all signals in the closed-loop systems. Simulation results on two examples verify the effectiveness of our developed control scheme.     

Finite-time consensus of neutrally stable multi-agent systems in the presence of input saturation

The problem of finite-time consensus of linear multi-agent systems subject to input saturation is investigated and two control protocols are presented for leaderless and leader-following cases, respectively. The leaderless multi-agent systems with proposed non-smooth protocol can achieve consensus in finite time. The consensus protocol designed for leader-following case with directed topology can solve the finite-time consensus problem, where a priori constraint is adopted to deal with input saturation. Furthermore, the settling time is explicitly derived using finite-time Lyapunov theory. Finally, the effectiveness of the theoretical results is illustrated with several numerical simulations.     

Decentralized adaptive neural control for interconnected stochastic nonlinear delay-time systems with asymmetric saturation actuators and output constraints

This paper investigates the problem of decentralized adaptive backstepping control for a class of large-scale stochastic nonlinear time-delay systems with asymmetric saturation actuators and output constraints. Firstly, the Gaussian error function is employed to represent a continuous differentiable asymmetric saturation nonlinearity, and barrier Lyapunov functions are designed to ensure that the output parameters are restricted. Secondly, the appropriate Lyapunov–Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions, and the neural networks are employed to approximate the unknown nonlinearities. At last, based on Lyapunov stability theory, a decentralized adaptive neural control method is proposed, and the designed controller decreases the number of learning parameters. It is shown that the designed controller can ensure that all the closed-loop signals are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. Two examples are provided to show the effectiveness of the proposed method.     

Trajectory tracking control of a 6-DOF quadrotor UAV with input saturation via backstepping

In this paper, the trajectory tracking control problem of a six-degree of freedom (6-DOF) quadrotor unmanned aerial vehicle (UAV) with input saturation is studied. Applying a hierarchical control structure, a priori-bounded control thrust and the desired orientations are derived to stabilize the translational subsystem without singularities. By using a backstepping approach with a Nussbaum function, a priori-bounded control torque for the rotational subsystem is designed to track the desired orientations generated by the translational subsystem. With the proposed control scheme, the latent singularities in the attitude extraction process caused by saturation nonlinearities are avoided, and globally uniformly ultimately bounded (UUB) stability of the closed-loop system is achieved. The tracking error bound is further determined which can be made arbitrarily small by tuning certain control gains. Numerical simulation results are provided to show the effectiveness of the proposed control scheme.     

Active fault tolerant control scheme for aircraft with dissimilar redundant actuation system subject to hydraulic failure

In this paper, an active fault tolerant control (AFTC) scheme is proposed for more electric aircraft (MEA) equipped with dissimilar redundant actuation system (DRAS). The effect of various fault/failure of hydraulic actuator (HA) on the system performance is analyzed in this work. In nominal condition, the state feedback control law is designed for primary control surfaces. In the presence of fault/failure of certain HA, control allocation (CA) scheme together with integral sliding mode controller (ISMC) is retrofitted with existing control law and engaged the secondary (redundant) actuators into the loop. A modified recursive least square (RLS) algorithm is proposed to identify the parametric faults in HA and to measure the effectiveness level of the actuator. In an event of failure of all HA’s in the system, electro hydraulic actuators (EHA) are taken in loop to bring the system back to its nominal operation. In order to stabilize the closed-loop dynamics of HA and EHA, fractional order controllers are designed separately for each actuator. Simulations on the lateral directional model of aircraft demonstrated the effectiveness of the proposed scheme as compared to the existing methods in the literature.     

Neural networks-based adaptive output feedback control for a class of uncertain nonlinear systems with input delay and disturbances

This paper focuses on the problem of adaptive output feedback control for a class of uncertain nonlinear systems with input delay and disturbances. Radial basis function neural networks (NNs) are employed to approximate the unknown functions and an NN observer is constructed to estimate the unmeasurable system states. Moreover, an auxiliary system is introduced to compensate for the effect of input delay. With the aid of the backstepping technique and Lyapunov stability theorem, an adaptive NN output feedback controller is designed which can guarantee the boundedness of all the signals in the closed-loop systems. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.     

A new singular system approach to output feedback sliding mode control for fractional order nonlinear systems

This paper studies the problem of output feedback sliding mode control (OFSMC) for fractional order nonlinear systems. A necessary and sufficient condition for the existence of a sliding surface is obtained by a new singular system approach and a linear matrix equality (LMI), which reduces the conservativeness of the system. Then an OFSMC law is designed based on a fractional order Lyapunov method, which ensures that the resulting fractional closed-loop system is asymptotically stable and the states of the fractional closed-loop system converge to the sliding surface in finite time. A fractional electrical circuit is discussed to illustrate the effectiveness of the proposed approach.     

Discrete time sliding mode controllers with relative degree one and two switching variables

In this paper, a new reaching law based sliding mode control strategy for discrete time systems is introduced. Contrary to most existing approaches, the new strategy uses a sliding variable with relative degree two. It is demonstrated that the new reaching law drives the sliding variable to a narrower quasi-sliding mode band than its relative degree one equivalent, while simultaneously ensuring the desired dynamic properties of the system. Furthermore, it is shown that the smaller quasi-sliding mode band width is reflected in reduced magnitude of all state variables in the sliding mode.     

A noise-filtering event generator for PIDPlus controllers

In this paper we propose a new event generator, which has strong noise-filtering capabilities, to be used in event-based control systems with a PIDPlus controller. An approximate frequency analysis is performed in order to characterize the event generator system and tuning guidelines are provided for its design parameter. Simulation and experimental results obtained with a laboratory setup demonstrate the effectiveness of the methodology in providing a satisfactory performance related to set-point and load disturbance step responses with a total variation that is significantly reduced with respect to the standard cases.     

Robust cooperative output regulation of heterogeneous uncertain linear multi-agent systems by intermittent communication

In this paper, we study the robust cooperative output regulation problem of heterogeneous linear multi-agent systems with system uncertainties and directed communication topology. A robust distributed event-triggered control scheme is proposed based on the internal model principle. To avoid continuous monitoring of measurement errors for the event-triggering condition, a novel self-triggered control scheme is further proposed. Moreover, by introducing a fixed timer in the triggering mechanisms, Zeno behavior can be excluded for each agent. An example is finally provided to demonstrate the effectiveness of the proposed self-triggered control scheme.     

Evaluation of fault diagnosability for networked control systems subject to missing measurements

The evaluation of fault diagnosability for networked control systems subject to missing measurements is addressed in this paper. In particular, the missing probability is assumed to be affected by norm-bounded uncertainties. By considering noise and missing measurements, the quantitative diagnosability problem is investigated via the principle of weight. To quantify the effect of uncertain missing probabilities, an interval criterion is presented and uncertainty ratio is defined. Furthermore, a novel method is proposed to alleviate the computation task of evaluating fault diagnosability. The effectiveness of the proposed method is verified by a numerical example.     

Adaptive finite-time control for stochastic nonlinear systems subject to unknown covariance noise

This paper is devoted to the adaptive finite-time control for a class of stochastic nonlinear systems driven by the noise of covariance. The traditional growth conditions assumed on the drift and diffusion terms are removed through a technical lemma, and the negative effect generated by unknown covariance noise is compensated by combining adaptive control technique with backstepping recursive design. Then, without imposing any growth assumptions, a smooth adaptive state-feedback controller is skillfully designed and analyzed with the help of the adding a power integrator method and stochastic backstepping technique. Distinctive from the global stability in probability or asymptotic stability in probability obtained in related work, the proposed design algorithm can guarantee the solution of the closed-loop system to be finite-time stable in probability. Finally, a stochastic simple pendulum system is skillfully constructed to demonstrate the effectiveness of the proposed control scheme.