Adaptive Output Feedback Control Research Articles

Event-Triggered-Based Adaptive Fuzzy Finite-Time Resilient Output Feedback Control for MIMO Stochastic Nonlinear System Subject to Deception Attacks

Event-Triggered-Based Adaptive Fuzzy Finite-Time Resilient Output Feedback Control for MIMO Stochastic Nonlinear System Subject to Deception Attacks

Flight Control Based on Adaptive Output Feedback for Quadrotors Using Quaternions

Quadrotors are expected to play an active role in out-of-sight areas such as equipment inspection and transportation of supplies to disaster areas. However, ensuring stable and highly accurate automatic flights has become a significant challenge. Trajectory tracking control methods that combine adaptive output feedback control based on almost strictly positive real characteristics and backstepping strategy have been proposed for a quadrotor control system with nonlinearities. These methods have simple structures and are robust. However, existing adaptive control methods have the problem of singularities, owing to the use of Euler angles in the modeling of quadrotors. In this paper, we propose an adaptive control method that enables a wider range of attitude changes using quaternions. The effectiveness of the proposed method is validated through numerical simulations.

Adaptive output feedback control of a bidirectional AC/DC totem‐pole PFC converter for wireless charging of electric vehicles

AbstractThis paper deals with the control problem of a single‐phase bidirectional alternating current/direct current (AC/DC) totem‐pole power factor correction (PFC) converter intended to be used in wireless power transfer (WPT) chargers for electric vehicles (EVs). The converter is operating as a bridgeless boost rectifier with the PFC functionality during grid to vehicle (G2V) mode and as a PFC inverter during vehicle to grid (V2G) mode. Firstly, the operating principle is presented. Secondly, an overall mathematical model governing all the operating modes of the converter is elaborated. Thirdly, due to the nonlinearity of the studied system, a nonlinear controller–based Lyapunov technique is designed to achieve the following control objectives: (i) unity power factor (UPF) during both power transfer modes G2V and V2G, (ii) regulation of the DC‐link voltage, and (iii) asymptotic stability of the closed‐loop system. Further, as the developed control law requires the measurement of all voltages and currents, an adaptive observer–based Kalman‐like technique is designed to estimate all the required signals and parameters. The performances of the proposed output feedback control technique are validated through several simulation scenarios showing that all control objectives are achieved. It should be noted that the use of the Kalman observer reduces the complexity of the systems as it requires only two measurements (grid voltage and current) making it an advantageous solution over existing solutions where three measurements are generally used. Furthermore, due to the low‐pass filtering behavior of the observer and its susceptibility to estimate the converter's parameters, the robustness of the controller and its insensitivity to the parameter uncertainties are greatly improved.

Adaptive Neural Control for Hysteretic Nonlinear Systems With Hysteresis Neural Direct Inverse Compensator and Its Application.

Aiming at high precision control for a class of hysteretic nonlinear systems, a new hysteresis direct inverse compensator-based adaptive output feedback control scheme is designed in this article. First, a novel long short-term memory neural network (LSTMNN)-based hysteresis inverse compensator is established to compensate the asymmetric hysteresis nonlinearity, where the LSTMNN is used as the prediction mechanism for model operator weights, rather than the overall mapping of hysteresis input and output. Second, by designing the modified high-gain K-Filter states observer and the error transformed function, the unmeasurable states are estimated with arbitrarily small estimation error and the prespecified tracking performance is achieved. Lastly, the biconical dielectric elastomer actuator (DEA) motion platform is constructed. Then, the effectiveness of the proposed LSTMNN-based hysteresis inverse compensator and control scheme are verified on the experimental platform. The experimental results illustrate the effectiveness and advantages of proposed control scheme.

Output Feedback Adaptive Optimal Control of Multiple Unmanned Marine Vehicles with Unknown External Disturbance

This paper presents an optimal output-feedback tracking control problem for multiple unmanned marine vehicles (UMVs) to track a desired trajectory. To guarantee the control objective in an optimal manner, adaptive dynamic programming (ADP) with optimal compensation terms is adopted. A neural velocity observer is designed based on a neural network (NN) to estimate the unmeasured system states and the unknown system dynamics. Furthermore, a disturbance observer (DO) is proposed to go against the effect of the unknown external disturbance of the sea environment. It is proved that the proposed controller can guarantee that all signals in the closed-loop system are bounded. Simulation results are given to demonstrate the effectiveness of the proposed control algorithm.

Disturbance observer-based nonsingular fixed-time fuzzy adaptive event-triggered output feedback control of uncertain nonlinear systems

This article examines the fuzzy adaptive event-triggered output feedback control issue of eliminating fixed-time singularity for a type of uncertain nonlinear systems with disturbance observer structures. By introducing novel bounded time-varying functions (TVFs), a set of more general intermediate-variable-based disturbance observers (IVBDOs) is constructed. Combining the corresponding auxiliary reduced-power function and the quartic Lyapunov method, a co-design scheme for a fuzzy approximation high-order controller and an improved event-triggered mechanism with a judgment indicator threshold is proposed. Especially, the developed framework not only removes the singularity during the fixed-time design process but also further reduces the communication resources. By means of theoretical analysis, the devised solution achieves fixed-time stable control (FTSC) of the system and the tracking error converges to the desired neighborhood around the origin. After verifying the absence of Zeno behavior, the efficacy of the presented methodology is supported by two simulation examples.

Sufficient conditions on input-to-stabilisation of time-varying semi-linear systems with disturbances in Hilbert spaces

In this paper, we investigate the problem of input-to-state stabilisation of time-varying semi-linear systems with disturbance in Hilbert spaces. An original adaptive input-to-state stabilisation strategy to derive this exceptional problem under the nonlinearities modelling is proposed using the Lyapunov method. Since all the states are not measurable, a Luenberger observer is designed to estimate unmeasurable states for input-to-state stabilisation. Thus, in the design procedure, first, a Luenberger observer is designed, and then the adaptive output feedback controller is constructed via the estimated states and adaptation law. Moreover, we give a sufficient condition, in terms of a perturbation term, to guarantee the input-to-state stability of the time-varying semi-linear systems under a stabilising nonlinear feedback law. An example of a controlled reaction-diffusion equation is given.

Neural Adaptive Intermittent Output Feedback Control for Autonomous Underwater Vehicles With Full-State Quantitative Designs.

In this article, a neural adaptive intermittent output feedback control is investigated for autonomous underwater vehicles (AUVs) with full-state quantitative designs (FSQDs). To achieve the prespecified tracking performance determined by quantitative indices (e.g., overshoot, convergence time, steady-state accuracy, and maximum deviation) at both kinematic and kinetic levels, FSQDs are designed by transforming constrained AUV model into an unconstrained model via one-sided hyperbolic cosecant boundaries and nonlinear mapping functions. An intermittent sampling-based neural estimator (ISNE) is devised to reconstruct the matched and mismatched lumped disturbances as well as immeasurable velocity states of transformed AUV model, where only system outputs after intermittent sampling are required. Using the estimations of ISNE and the system outputs after triggering, an intermittent output feedback control law incorporated with hybrid threshold event-triggered mechanism (HTETM) is designed to achieve ultimately uniformly bounded (UUB) results. Simulation results are provided and analyzed to validate the effectiveness of the studied control strategy with application to an omnidirectional intelligent navigator (ODIN).

An output-feedback stabilisation scheme with exponential convergence for nonlinear systems with unknown polynomial-of-output growth rate

ABSTRACT This paper seeks adaptive output-feedback control which enables exponential stabilisation for nonlinear systems with unknown polynomial-of-output growth rate and dynamic uncertainties. The existence of uncertainties naturally requires the introduction of adaptive compensation mechanisms, while, convergence rate, as an important performance specification, is usually hard to attain in the context of adaptive control. For this, we are compelled to pursue a comprehensive and competent adaptive compensation mechanism which possesses dual capabilities of compensating uncertainties and affording convergence rate. Crucially, a subtle dynamic gain is designed with its dynamic updating law incorporating exponential-type time-varying information. Notably, the gain would not only deal with the unknown polynomial-of-output growth rate but particularly ensure exponential convergence with the convergence rate online adjusting in conformity to the dynamic uncertainties. Correspondingly, an adaptive output-feedback controller is constructed based on a dynamic-gain observer, which can render exponential convergence for system and observer states.

K-filter-based distributed adaptive bipartite tracking for nonlinear multi-agent systems with unknown control direction

This paper investigates K-filter-based distributed adaptive output-feedback bipartite tracking problem for multi-agent systems with unknown control direction. The agents are partitioned into two groups, where agents belonging to the same group are cooperative while agents belonging to the distinct group are competitive. Firstly, the unmeasurable state variables of the system under unknown control direction are estimated through K-filters. Then, a neighbourhood error is introduced to transform a structurally balanced network into a cooperative network. Based on K-filters, neighbourhood error, Nussbaum-type functions and dynamic surface control, a distributed adaptive output-feedback control protocol is designed to achieve the bipartite tracking. It is shown that all signals in the resulting closed-loop systems are semi-global bounded and the output of each agent can track the reference signal provided that the network is structurally balanced and contains a directed spanning tree. Finally, a numerical example is given to verify the effectiveness of the proposed method.

Output feedback control of stochastic nonlinear systems with external disturbances and inverse dynamics

SummaryThis work presents an approach to control a type of stochastic cascade nonlinear systems, which involve external disturbances and nonlinear terms. The external disturbances under the consideration are not measurable and regarded as extended states. The proposed method addresses the problem of unmeasured states and external disturbances by introducing an extended state observer (ESO). Based on the above work, a new stochastic nonlinear system including ESO is established. To ensure the stability and boundedness of the system, an adaptive output feedback controller is designed by using stochastic integrated input‐to‐state stability and backstepping design technique. This controller guarantees that all the states remain almost surely bounded and globally asymptotically stable in probability. Simulation experiments are conducted to validate the effectiveness of the proposed control scheme.

Output feedback adaptive inverse optimal security control for stochastic nonlinear cyber-physical systems under sensor and actuator attacks

Output feedback adaptive inverse optimal security control for stochastic nonlinear cyber-physical systems under sensor and actuator attacks

Output feedback adaptive neural network control for uncertain nonsmooth systems with application

The output feedback adaptive neural network control problem is addressed for uncertain nonsmooth systems with network communication constraints. First, a funnel function is provided to ensure that the tracking error restrains the pre-specified performance and improves the transient and steady-state performance simultaneously. Second, an event-triggered controller including the control input, a fixed threshold and the tracking error is designed to alleviate the communication burden. It is rigorously mathematically proved that there exists a positive lower bound to avoid the Zeno behavior. With the designed observer, the control algorithm assures that all the signals are semi-globally uniformly ultimately bounded. Finally, the validity of the proposed scheme is demonstrated through an application example.

Event‐triggered adaptive output feedback control of unmanned aerial vehicles under hybrid attacks

AbstractThis study addresses the adaptive dynamic output feedback (DOF) control for unmanned aerial vehicle systems subjected to hybrid attacks. These attacks are delineated by two Bernoulli variables, signifying denial‐of‐service (DoS) and deception attacks, respectively. In an effort to enhance energy efficiency and reduce communication costs in the presence of network delays, the study introduces an innovative probability event‐triggering mechanism (PETM). Subsequently, it crafts a DOF controller that operates based on the available measurement outputs. By harnessing Lyapunov stability theory, the study identifies sufficient conditions that guarantee the stability of the system's augmented dynamics. Finally, the efficacy and applicability of the proposed method are underscored through a case study on an unmanned aerial vehicle.

Compensation Observer-Based Adaptive Output Feedback Control for Multi-Agent Systems

This study presents a modified compensating observer control strategy for nonlinear multi-agent systems affected by unknown hysteresis signal loops. Compared to conventional high-gain observers, this approach introduces a novel compensation signal, effectively reducing the tracking error of traditional observers. Then, by utilizing a backstepping method, an adaptive output feedback controller is designed, such that the tracking error converges to the small neighborhood around the origin. Simulation experiments with and without the compensation term demonstrate that this control strategy can effectively reduce error, but it increases input chattering to some extent.

Asymptotic adaptive output-feedback stabilisation of nonlinear systems with unknown growth rate and sensor uncertainty

For a class of nonlinear systems with sensor uncertainty, the problem of adaptive output feedback stabilisation is investigated in this paper. It is assumed that only the output is available for feedback control, which suffers from sensor uncertainty. At the same time, the nonlinear function in the system model has an unknown growth rate. To solve this problem, a set of state observers is designed to estimate the unknown system states based on the uncertain output, and an adaptive output-feedback control is designed based on this. By using the corollary of Barbalet's Lemma, it is shown that the equilibrium of the closed-loop system is asymptotically stable. Finally, simulation results verify the effectiveness of the control scheme

Prescribed performance adaptive fuzzy output feedback control for steer-by-wire vehicle system with intermittent actuator faults

Prescribed performance adaptive fuzzy output feedback control for steer-by-wire vehicle system with intermittent actuator faults

Adaptive output feedback stabilisation control for cascaded systems with output constraints and disturbance

The system under consideration is a cascaded system composed of integral input-to-state stable inverse dynamics and a non-holonomic system. An adaptive output feedback stabilisation control is proposed for the system with disturbance and output constraints. The dynamic uncertainty is eliminated using the technique that changes the supply rate. An extended state observer is proposed by defining the non-vanishing disturbance as a new state variable of the system. The asymmetric time-varying output constraint is solved by utilising the tan-type barrier Lyapunov function. The result shows that the adaptive output feedback controller guarantees the stability of the closed-loop system without violating the asymmetric time-varying output constraints under the backstepping technique. Simulation results are given to verify the effectiveness of the control method.

Adaptive output feedback control system design for nonlinear systems via neural networks

AbstractAdaptive output feedback control based on output feedback exponential passivity (OFEP) has a simple structure and strong robustness in regard to disturbances and system uncertainties. However, it is difficult for most nonlinear systems to satisfy the conditions of OFEP. Thus, the introduction of a suitable parallel feedforward compensator (PFC) to construct an OFEP‐augmented system with the controlled system has been used, but the control output cannot achieve perfect tracking because of the output of the introduced PFC. As a solution, introducing a feedforward (FF) input to build a 2 degree of freedom (2‐DOF) is a simple and effective way to solve this problem. In this paper, we propose the design schemes for suitable PFC and FF input of nonlinear systems via the use of neural networks (NN), respectively. Besides, to cope with possibly present input disturbances, we also provide a method to achieve disturbance compensation based on NN to reduce their interference. Finally, the effectiveness of all proposed design schemes is confirmed through numerical simulations.

Decentralized Output-Feedback Adaptive Event-Triggered Control for Interconnected Nonlinear Delay Systems with Actuator Failures

This paper investigates decentralized adaptive event-triggered fault-tolerant control for interconnected nonlinear delay systems with actuator failures. The actuator failures suffered include loss of effectiveness and bias faults. A control scheme based on the K-filter is proposed, which effectively compensates for the effects of unknown actuator failures. A hyperbolic tangent function and neural network are introduced to approximate the unknown interconnection function and nonlinear delay function. By introducing the dynamic surface control method, the “explosion of complexity” issue is addressed. Furthermore, our proposed controller can ensure that all states of the corresponding closed-loop system are semi-globally uniformly ultimately bounded and that the tracking error can converge to a small neighborhood of zero. Meanwhile, Zeno behavior can be effectively avoided. Finally, the validity of the proposed control scheme is verified using a simulation example.