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.

For absolute value terms related to yaw velocity generated by cross-flow effect and the unmodeled factors, a nonlinear parameter-varying (NPV) model is established with absolute value disturbances to regulate the heading of unmanned surface vehicle (USV) by state feedback (SF) control with a NPV disturbance observer (NPVDO). Firstly, the model of NPVDO is designed with gains to estimate the non-differentiable absolute disturbance. Secondly, a Lyapunov function is constructed with full states and varying parameter to solve the gains with NPVDO stability conditions by Euler homogeneity. Thirdly, since the NPVDO stability conditions contain the coupling term between the observer gain and the Lyapunov matrix, it is decoupled into NPVDO-sum of squares (SOS) conditions by the projection theorem and matrix transformation to solve NPVDO gains. Finally, a robust SF with NPVDO is designed for heading regulation with NPV model. Simulations and experiments indicate that NPVDO has a superior performance on parameter variation suppression and nonlinear non-differentiable disturbance estimation by comparing with other methods to reduce heading errors.

This paper studies the problem of obstacle avoidance and trajectory tracking for a class of uncertain nonlinear systems with unmeasured states and actuator faults. The main difficulty is that actuator faults may cause significant transient tracking errors, which might lead to collisions. To overcome this difficulty, an adaptive observer is developed to estimate system states and compensate for actuator faults. Additionally, the integral-multiplicative Barrier Lyapunov function (BLF) is integrated into the backstepping procedure to overcome the dynamics mismatching problem of the existing SUM-type BLF. The proposed adaptive scheme can avoid collisions in a multi-obstacle environment even if the actuator faults occur, and all the signals are uniformly ultimately bounded. Simulation results demonstrate the effectiveness of this approach.

In this paper, we investigate affine formation control problem for a class of multi-agent systems (MASs) where the formation information is only accessible to a small group of agents. We propose a class of generic formation maneuver (GFM) functions and unified analysis method to study different convergence properties including finite-time, fixed-time and predefined-time strategy. We first propose a consensus-based formation control of leaders and then a stress-matrix-based formation control for followers. Different GFM functions are specified for finite-time, fixed-time and predefined-time formation control of leader and follower groups, respectively. Finally, a number of simulation examples are provided to prove the effectiveness of the proposed algorithm.

This paper mainly solves the problem of synchronous control of the master-slave manipulator in an underwater teleoperation system with hardware performance limitations, model uncertainties, and unknown external disturbances. Firstly, in order to meet the limitations of hardware conditions such as the motor speed in the hardware platform, and a prescribed performance function(PPF) is proposed to constrain the speed error, effectively improving the transient performance of the system. Secondly, a prescribed-performance-based adaptive nonsingular integral sliding mode controller(ANISMC) is proposed. Compared with the existing integral sliding mode controllers, the controller in this paper has a faster convergence speed. Then, the Lyapunov method is used to prove that the transformation error and the sliding surface can converge to zero within a fixed time, solving the problem that the integral sign is difficult to cancel when the integral sliding mode controller is used to prove fixed-time stability with the Lyapunov method, and there is no need to know the upper limit of uncertainties and disturbances in advance. Finally, the simulation comparison experiment shows the superiority of the proposed PPF-based ANISMC

This paper investigates the formation composition and keeping problem for multi-quadrotor systems under time-varying communication delays. First, a distributed model predictive control (MPC) approach is employed to transform the formation composition and keeping of multi-quadrotor systems into an online rolling optimization issue, accompanied by the event-triggered mechanism to reduce solution frequency and communication load. Second, to address the asynchrony and time-varying communication delays introduced by event-triggered distributed MPC, a set of constraints is designed to restrict deviations between the current predictive state and previously broadcasted states. Consequently, based on the previously predicted states of neighbors, the cooperation of the multi-quadrotor systems is achieved under asynchronous communication and time-varying delays. This approach guarantees robust asymptotic stability and satisfactory formation performance for multi-quadrotor systems under various delay scenarios. Finally, the numerical and software-in-the-loop (SIL) simulations validate the effectiveness of the proposed algorithm of multi-quadrotors under communication delays.

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.

The paper addresses the problem of transforming observable, time-reversible and multi-input single-output discrete-time state equations into the extended observer form which comprises a linear observable component and a nonlinear injection term depending on the inputs, output, and a finite number of their past values. The intrinsic necessary and sufficient conditions for existence of the extended observer form are provided in terms of a certain vector field, defined by the system output and its past values. The algorithm is presented to find a parametrized state transformation that takes the state equations into the considered extended observer form. Two examples, one of them academic, illustrate the theory.

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 paper deals with the security control stabilization problem of uncertain Markov jump power systems with input dead zone under stochastic denial-of-service (DoS) attack. DoS attack is modeled as a discrete-time Markov process. Dual hidden Markov models are respectively used to detect the modes of the original power systems and the one under DoS attack. Based on the detected modes and neural networks (NNs), adaptive NN-based security asynchronous control strategies are proposed, where both state feedback and output feedback are studied simultaneously. With the developed control laws, all trajectories of the closed-loop systems are bounded stable in the stochastic setting. Simulation results demonstrate the correctness and usefulness of the proposed techniques.

In this paper, we present one Robust control Problem where \mathcal{P}=\{P(s,l,m)=U(s,l)/V(s,m):l\in L,m\in M \}  is a family of interval plants. Considering a multilinear function with two uncertain parameters l and m, we have shown the strictly positive real (SPR) constructing four Kharitonov Polynomials for that problem. For this case, the aim of the paper is twofold. First, we approach to show the robust stability of {P}(s,l,m). Second we show that   \displaystyle{\min_{l\in L,m\in M}}~~{Re U\left(j\omega,l ight)V^\ast\left(j\omega,m ight)>0} where V^\ast\left(j\omega,m ight) is conjugate of V(j\omega,m) and s=j\omega where omega is frequency assuming some domain. Then we have illustrated one example.

In this paper, we consider the matrix-weighted consensus problems with disturbances. To this end, we firstly propose a new disturbance observer design for systems with unknown matched or mismatched disturbances representable as a linear combination of time-varying basis functions. Under some assumptions on the boundedness and persistent excitation of the regression matrix, the disturbances can be precisely estimated at an exponential rate and thus, can be compensated by a suitable compensation input. Next, disturbance-observer based consensus algorithms are proposed for matrix-weighted networks of single- and double-integrators with matched or mismatched disturbances. We show that both matched and mismatched disturbances can be estimated and actively compensated, and the system globally asymptotically converges to a fixed point in the kernel of the matrix-weighted Laplacian. Depending on the network connectivity, the system can asymptotically achieve a consensus or a cluster configuration. The disturbance-observer based consensus design is further extended for higher-order integrators with disturbances. Finally, simulation results are provided to support the mathematical analysis.

Monte Carlo simulations have long been a widely used method in the industry for control system validation. They provide an accurate probability measure for sufficiently frequent phenomena, but are often time-consuming and may fail to detect very rare events. Conversely, deterministic techniques such as µ or IQC-based analysis allow fast calculation of worst-case stability margins and performance levels, but in the absence of a probabilistic framework, a control system may be invalidated on the basis of extremely rare events. Probabilistic µ-analysis has therefore been studied since the 1990s to bridge this analysis gap by focusing on rare but nonetheless possible situations that may threaten system integrity. The solution adopted in this paper implements a branch-and-bound algorithm to explore the whole uncertainty domain by dividing it into smaller and smaller subsets. At each step, sufficient conditions involving µ upper bound computations are used to check whether a given requirement – related to the delay margin in the present case – is satisfied or violated on the whole considered subset. Guaranteed bounds on the exact probability of delay margin satisfaction or violation are then obtained, based on the probability distributions of the uncertain parameters. The difficulty here arises from the exponential term classically used to represent a delay, which must be replaced by a rational expression to fit into the Linear Fractional Representation (LFR) framework imposed by µ-analysis. Two different approaches are proposed and compared in this paper. First, an equivalent representation using a rational function of degree 2 with the same gain and phase as the real delay, which results into an LFR with frequency-dependent uncertainty bounds. Then, a Padé approximation, whose order should be chosen carefully to handle the trade-off between conservatism and complexity. A constructive way to derive minimal LFR from Padé approximations of any order is also provided as an additional contribution. The whole method is first assessed on a simple satellite benchmark, and its applicability to realistic problems involving a larger number of states and uncertainties is then demonstrated.

This paper deals with the stabilization of a linear hyperbolic system subject to a boundary disturbance. Our feedback design relies on the strategy called active disturbance rejection control (ADRC). The unknown disturbance is estimated by an extended state observer. We prove the existence of solutions of the closed-loop system and the global asymptotic stability of the closed-loop system. A numerical example is given to illustrate the efficiency of our strategy.

This paper is concerned with parameter estimate and adaptive control problems of deterministic autoregressive moving average (DARMA) systems on the basis of quantized data of system output signals which are generated by a kind of uniform quantizer. By designing system input signals, the extended least-squares (ELS) algorithm with uniform output observations is proved to yield bounded estimation errors under some mild assumptions. Moreover, the adaptive tracking controller under inaccuracy observations are also designed. To analyse the properties of tracking error, I use the expanded form of ELS and research the properties of quantization noise. In addition, I give the expression of tracking error and show how it depends on the size of quantization step when the quantization step satisfies some conditions. A numerical example is supplied to demonstrate the theoretical results.

This paper proposes a fault-tolerant control strategy for the air path system of a turbocharged diesel engine, considering the simultaneous presence of matched and mismatched external disturbances, additive and loss of effectiveness fault modes in the EGR and VGT subsystems. Firstly, a disturbance observer based on H ∞ theory is designed for estimating additive faults and external disturbances, and the upper bound of observation error is obtained. Based on the observation information, an integral sliding mode surface is designed, and the effectiveness of the sliding mode surface is analyzed. For the loss of effectiveness faults in the actuators, a novel adaptive update sliding mode controller is designed. The proposed control algorithm can get the fault information of the system and achieve fault-tolerant control of through controller reconstruction. Ultimately, the effectiveness of the proposed method is validated through simulations and comparative analysis.

This paper develops a neuro-adaptive observer for state and nonlinear function estimation in systems with partially modeled process dynamics. The developed adaptive observer is shown to provide exponentially stable estimation errors in which both states and neural parameters converge to their true values. When the neural approximator has an approximation error with respect to the true nonlinear function, the observer can be used to provide an H ∞ bound on the estimation error. The paper does not require assumptions on the process dynamics or output equation being linear functions of neural network weights and instead assumes a reasonable affine parameter dependence in the process dynamics. A convex problem is formulated and an equivalent polytopic observer design method is developed. Finally, a hybrid estimation system that switches between a neuro-adaptive observer for system identification and a regular nonlinear observer for state estimation is proposed. The switched operation enables parameter estimation updates whenever adequate measurements are available. The performance of the developed adaptive observer is shown through simulations for a Van der Pol oscillator and a single link robot. In the application, no manual tuning of adaptation gains is needed and estimates of both the states and the nonlinear functions converge successfully.

This article investigates an event-triggered adaptive estimated inverse control scheme for a class of uncertain nonlinear systems with hysteresis effects, parametric uncertainties and disturbances. An online estimated inverse hysteresis compensation mechanism is developed, where an adaptive technique is employed to obtain the value of unknown hysteresis parameters. Compared with the common approaches, its biggest advantage lies in that it is not necessary to obtain the hysteresis parameters by means of experiment, which relaxes time-consuming off-line identification work.Moreover, an adaptive radial basis functions neural network (RBFNN) is utilized to approximate the unknown disturbances, whose weight coefficients along with parametric uncertainties are all estimated by the adaptive technique. Besides, the communication cost can be largely saved by introducing the relative threshold event-triggered control (ETC). Through Lyapunov analysis, the proposed controller guarantees the boundedness of all the signals and the convergence of the error signals. The results of numerical simulation illustrate the effectiveness and superiority of the developed controller.