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 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.

Model predictive control (MPC) has been widely applied in the field of autonomous driving; however, it still suffers from poor control accuracy in extreme conditions. To improve the smoothness and robustness of intelligent vehicle trajectory tracking, this study tries to investigate an improved MPC trajectory tracking control for intelligent vehicle, fuzzy adaptive dynamic model predictive control (FADMPC), with prediction time-domain dynamic optimization and objective function weight adaptive regulation. First, in the improved MPC framework, a vehicle dynamics model with three degrees of freedom is established as the theoretical vehicle model of MPC controller, and the recursive least square method (RLS) with forgetting factor is used to timely estimate tire cornering stiffness considering nonlinear error of the control-oriented vehicle model. Second, the relationship between the prediction time domain of the model and factors such as vehicle speed and road adhesion coefficient is analyzed through simulation. Then, a dynamic time-domain regulation scheme is determined by using the whale optimization algorithm (WOA). Third, a fuzzy controller is used to dynamically adjust the weight coefficient of the lateral displacement tracking error in the objective function. Last, a series of experiments is performed on a hardware-in-the-loop platform to validate the comparison effect of different trajectory tracking controllers on jointed and bisected adhesion condition roads. Results indicate that the proposed FADMPC can effectively improve trajectory tracking accuracy and vehicle stability compared with LQR and even ADMPC controller.

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 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.

Remaining Useful Life (RUL) has become widely recognized as a key metric for evaluating system reliability within the field of Prognostic and Health Management (PHM). It plays a crucial role in predicting a system’s health status and its expected lifespan. While significant efforts have been directed towards accurately estimating RUL, the process of decision-making regarding health management and potential adjustments to equipment lifespan still poses questions. This study introduces a state-space methodology designed to effectively manage RUL and, thus, the end-of-life of a degrading system, particularly focusing on controlled operation systems. Through an investigation of the relationship between the operational dynamics of the system and its aging process, the study specifically concentrates on regulating the degradation rate experienced during operation. A robust design method of an observer and a controller is presented to effectively govern the degradation trajectory, allowing for the achievement of an acceptable level of deterioration within a desired average lifetime. To demonstrate the performance and practical application of the proposed approach, it is applied to a variable-speed wind turbine with a flexible shaft subjected to torsion effects. The results highlight the benefits of using RUL control as a Health-Aware Control (HAC) strategy, aiming to find an optimal balance between reliability and production objectives. This study makes a valuable contribution to the field of reliability engineering by providing insight into managing the end-of-life of controlled processes suffering from aging. It is showed that the RUL control problem can be conveniently expressed as the problem of controlling a polytopic uncertain system. For this reason, a discrete-time robust LQR controller design is presented for solving the polytopic control design problem.

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.

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 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 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.

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 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.