Adaptive Output Feedback Control Research Articles

The design method of simple adaptive control system with PFC for MIMO system via DE

This paper deals with a design method of adaptive output feedback control system with parallel feedforward compensator (PFC) for a multiple input multiple output system. An adaptive output feedback control system, which is based on almost strictly positive real (ASPR), requires PFC in most cases. The design method is using one-shot input/output data of the controlled system, and parameter optimization will be done by differential evolution (DE). By using DE, the designer can optimize the design parameters without trial and error, which can be a more practical method.

Fully distributed leader–follower consensus for multi-agent systems based on reduced-order adaptive feedback protocol

This paper addresses the leader–follower consensus problem of linear multi-agent systems by designing reduced-order adaptive output feedback protocol. Using relative output information among neighboring agents, node-based adaptive output feedback control protocol and edge-based adaptive output feedback control protocol are presented, respectively. For both cases, based on Lyapunov analysis and algebraic Riccati equation design, some sufficient conditions are given to achieve leader–follower consensus by applying the proposed adaptive output feedback protocols. Finally, some simulation examples are given to illustrate the validity of the theoretical results.

Finite-time adaptive neural network event-triggered output feedback control for PMSMs

This article investigates the finite-time adaptive neural network event-triggered output feedback control for the permanent magnet synchronous motor (PMSM) systems. The addressed PMSM systems include unknown nonlinear dynamics and immeasurable states. The neural networks are utilized to approximate the unknown nonlinear dynamics and an equivalent control design model is established, by which a neural network state observer is given to estimate the immeasurable states. By constructing an event-triggered mechanism and under the framework of adaptive backstepping control design technique and finite-time stability theory, a finite-time adaptive event-triggered output feedback control scheme is developed. It is proved that the proposed control scheme ensures the closed-loop system to be stable and the angular velocity, stator current and other state variables remain bounded in a finite time. Finally, the computer simulation is provided to confirm the effectiveness of the presented controllers.

Event-Triggered Robust Adaptive Dynamic Programming With Output Feedback for Large-Scale Systems

In this article, an event-triggered output-feedback adaptive optimal control approach is proposed for large-scale systems with parametric and dynamic uncertainties through robust adaptive dynamic programming and small-gain techniques. By using the input and output data, the unmeasurable states are reconstructed instead of designing a Luenberger observer. To save the communication resources and reduce the number of control updates, an event-based feedback control policy is learned based on policy iteration and value iteration, respectively. The closed-loop stability and the convergence of the proposed algorithms are analyzed by using Lyapunov stability theory and small-gain techniques. A practical example of multimachine power systems with governor controllers is given to demonstrate the effectiveness of the proposed methods.

Distributed Observer-Based Adaptive Fuzzy Consensus of Nonlinear Multiagent Systems Under DoS Attacks and Output Disturbance.

This article studies the adaptive output-feedback consensus control problem of nonlinear multiagent systems (MASs) against denial-of-service (DoS) attacks. The attacks on the edges instead of nodes are considered, where we allow different attack intensities but at least one edge is connected in each attacking interval. Affected by output disturbance, the sensor feedback signal of every agent is inaccurate, which will reduce the approximation accuracy of the observer. Then, we design a signal to revise the sensor feedback signal subject to disturbance. Meanwhile, a prescribed performance function is used to ensure the transient and steady-state performance of error. Leveraging the Lyapunov stability theory and the backstepping technique, a distributed output-feedback control scheme subject to asymmetric saturation nonlinearity is designed. For the asymmetric input saturation, an auxiliary signal is designed to simplify the designed progress of controller input. To deal with the inherent problem of "explosion of complexity" emerging with backstepping, dynamic surface control is utilized. It is proved that the consensus errors converge to small neighborhoods of the origin, and all signals within the closed-loop system are bounded. Finally, simulation results are offered to demonstrate the effectiveness of the proposed method.

Modeling and Adaptive Boundary Robust Control of Active Heave Compensation Systems

Heave compensation systems are essential for operations’ safety, reliability, and efficiency in harsh offshore environments. This paper investigates the vibration suppression problem of a type of deep-sea robot with the length of time variation and harsh operating environments for active heave compensation systems, where hydraulic heave compensators implement actuators with input nonlinearity, model coupling, and unknown nonlinear disturbances. A robust adaptive output feedback control scheme based on the backstepping control method is designed to eliminate deep-ocean robot vibration, where the adaptive law handles the system parameter uncertainty. Meanwhile, a nonlinear disturbance observer (NDO) is introduced to overcome the effects of random disturbances and model coupling. In addition, the stability of the whole system is proved according to Lyapunov’s theory, and the scheme is shown to be feasible by theoretical analysis. Finally, a comparative simulation study was conducted to validate the effectiveness of the proposed controller.

Neural-network-based output feedback adaptive resilient control for stochastic nonlinear systems subject to sensor and actuator attacks

This paper addresses the output feedback adaptive resilient control problem for a class of stochastic nonlinear systems under sensor and actuator attacks. By applying a linear state transformation, the original system is transformed into a new one for which output feedback control becomes possible. The Nussbaum function is exploited to overcome the unknown control direction problem generated by uncertain control coefficients and adversary attacks. Based on the available compromised output signal and the states reconstructed by a new extended state observer, an output feedback adaptive resilient control scheme is proposed for such systems by combining the neural-networks-based approximation approach, backstepping technique and some special methods. The proposed controller can guarantee that all signals in the closed-loop system are semiglobally bounded in probability and the stabilisation error variables remain in an adjustable vicinity of origin. Finally, the effectiveness and applicability of the proposed control methodology is verified by two illustrative examples.

Adaptive Backstepping Axial Position Tracking Control of Autonomous Undersea Vehicles with Deferred Output Constraint

In this paper, an adaptive backstepping control scheme is proposed to solve the the surge motion tracking control problem of an autonomous undersea vehicle (AUV) with system constraint. First, an initial rectification reference signal is constructed for the subsequent implementation of deferred output constraint and making the control input smaller and smoother in the early stage of system operation. Second, a barrier Lyapunov function is adopted for developing an output-constrained state feedback adaptive controller. Then, on the basis of coordinate transformation and estimating the derivative of surge displacement by a linear and nonlinear combined differentiator, we develop an output feedback adaptive backstepping control scheme for AUVs whose velocity signals are unmeasurable. We also carried out a comparative numerical simulation with traditional adaptive control to verify the feasibility of the proposed control strategy.

Adaptive output feedback asymptotic tracking for uncertain nonlinear systems with dead-zone input

In this paper, the adaptive output feedback asymptotic tracking problem is investigated for nonlinear systems with dead-zone input and unknown nonlinearities, where only system output is available. First, an observer is constructed to estimate unavailable states. Then, by the observer and barrier Lyapunov function, an adaptive output feedback controller is designed, which compensates the influence of dead-zone input and unknown nonlinearities. Furthermore, based on the proposed controller and tuning function, the global boundedness of all signals in closed-loop system is achieved, and the tracking error can asymptotically converge to zero. Particularly, it should be noted that there is no need to calculate the derivatives of virtual control laws in controller design, thus reducing the computational complexity. Finally, two numerical examples are taken to verify the effectiveness of the designed controller.

Adaptive Output Feedback Control for Constrained Switched Systems with Input Quantization

This paper investigates adaptive output feedback control problem for switched uncertain nonlinear systems with input quantization, unmeasured system states and state constraints. Firstly, fuzzy logic systems are introduced to identify system uncertainties, then the fuzzy based observer is constructed to estimate unavailable states. Secondly, combing the backstepping technique and the barrier Lyapunov function method, an adaptive fuzzy output feedback control law is designed, which guarantees that all signals in the closed-loop system are bounded, the system output tracks the reference signal, and system states satisfy their corresponding constraint conditions. Finally, simulation results further show the good performances of the proposed control scheme.

Adaptive Fuzzy Output Feedback Tracking Control for Uncertain Nonstrict Feedback Systems With Variable Disturbances via Event-Triggered Mechanism

This article studies the adaptive fuzzy event-triggered output feedback control for nonlinear systems. The systems have the nonstrict feedback structure and variable disturbances simultaneously. A fuzzy state observer with an adaptive parameter is first proposed, and a less conservative condition to solve the observer gains is obtained. By constructing the command filter, the compensator, the fuzzy controller, the event-triggered mechanism, and the adaptive parameter, the adaptive fuzzy output feedback control scheme is built to guarantee that all the signals of the closed-loop systems are bounded, and the tracking errors are less than a prescribed accuracy in finite time. Simulation examples confirm the effectiveness of the proposed method.

Event-Triggered Adaptive Output-Feedback Control of Switched Stochastic Nonlinear Systems With Actuator Failures: A Modified MDADT Method.

This article investigates the adaptive event-triggered output-feedback control problem for a class of switched stochastic nonlinear systems with actuator faults. In the existing works, the developed results on adaptive control for switched stochastic nonlinear systems are almost based on the average dwell-time method, and how to construct a desired adaptive controller in the frame of the mode-dependent average dwell time (MDADT) remains a control dilemma. By presenting a general adaptive control rule based on the MDADT, this article implements the adaptive output-feedback control for the switched stochastic system under interest. In the process of controller design, fuzzy-logic systems, a flexible approximator, are utilized to approximate the unknown nonlinear functions. The dynamic surface design approach is employed to avoid taking derivatives of the constructed virtual controls to decrease the difficulty of complex calculation greatly. Meanwhile, a switched observer is designed to estimate the unknown states. In the frame of backstepping design, an event-triggered-based adaptive output-feedback controller is constructed such that all signals existing in the closed-loop system are ultimately bounded under a class of switching signals with MDADT property. Finally, the simulation results show the validity of the proposed control strategy.

Energetic optimization of an autonomous mobile socially assistive robot for autism spectrum disorder.

The usage of socially assistive robots for autism therapies has increased in recent years. This novel therapeutic tool allows the specialist to keep track of the improvement in socially assistive tasks for autistic children, who hypothetically prefer object-based over human interactions. These kinds of tools also allow the collection of new information to early diagnose neurodevelopment disabilities. This work presents the integration of an output feedback adaptive controller for trajectory tracking and energetic autonomy of a mobile socially assistive robot for autism spectrum disorder under an event-driven control scheme. The proposed implementation integrates facial expression and emotion recognition algorithms to detect the emotions and identities of users (providing robustness to the algorithm since it automatically generates the missing input parameters, which allows it to complete the recognition) to detonate a set of adequate trajectories. The algorithmic implementation for the proposed socially assistive robot is presented and implemented in the Linux-based Robot Operating System. It is considered that the optimization of energetic consumption of the proposal is the main contribution of this work, as it will allow therapists to extend and adapt sessions with autistic children. The experiment that validates the energetic optimization of the proposed integration of an event-driven control scheme is presented.

Event-Triggered output feedback adaptive control for nonlinear switched interconnected systems with unknown control coefficients

This paper proposes an event-triggered adaptive control scheme for switched nonlinear interconnected systems with unknown control coefficients and unknown backlash hysteresis. Firstly, unknown interconnected terms and unknown nonlinearities of the system are approximated through fuzzy logic systems. By introducing the Nussbaum function and choosing an appropriate Lyapunov function, an event-triggered output feedback controller is developed. The proposed control scheme can guarantee that the whole states are semi-globally uniformly ultimately bounded, meanwhile, the elimination of Zeno behavior is ensured. Eventually, the effectiveness of the method is verified by simulation results.

Global output feedback control for large‐scale time‐delay systems with inherent nonlinearities and measurement uncertainty

AbstractThis paper investigates the problem of global state regulation by output feedback for a class of large‐scale nonlinear time‐delay systems with inherent nonlinearities and unknown measurement sensitivity. The uncertain nonlinearities are bounded by nonlinear growth of lower triangular unmeasurable (delayed) states multiplied by the unknown constant, polynomial‐of‐output and polynomial‐of‐input. Different from closely related works, the simultaneous existence of complicated nonlinearities and unknown measurement sensitivity makes the problem more involved and complex. Based on the dynamic gain scaling technique, we design a set of new adaptive output feedback controllers with a simpler structure by constructing two Lyapunov–Krasovskii functionals appropriately, which can globally regulate all states of the large‐scale systems. An illustrative example is proposed to demonstrate the validity of the presented control scheme.

Transients Improvement in the Output-Feedback Adaptive Backstepping Control of Nonlinear Systems with Input Constraints

The paper addresses the problem of transient performance improvement in the output-feedback adaptive backstepping control of parametrically uncertain nonlinear system represented in the cascaded output-feedback form. The system state is inaccessible for measurement, the system input is constrained, and the control gain is assumed unknown (its sign is known). The problem is solved by applying specially modified K-filters with adjustable parameter, using the backstepping procedure taking into account the input constraints, and involving the Kreisselmeier-like adaptation algorithm with a modified swapping scheme. The algorithm enables us to improve the closed-loop transient performance without increasing initial “swings” in the control error transients. The latter property is achieved by including the high-order time derivatives of the adjustable parameters, calculated by the adaptation algorithm, in the control law. The properties of the closed-loop system are illustrated via simulation.

Dynamic Event-Triggered Adaptive Neural Output Feedback Control for MSVs Using Composite Learning

This paper investigates the control issue of marine surface vehicles (MSVs) subject to internal and external uncertainties without velocity information. Utilizing the specific advantages of adaptive neural network and disturbance observer, a classification reconstruction idea is developed. Based on this idea, a novel adaptive neural-based state observer with disturbance observer is proposed to recover the unmeasurable velocity. Under the vector-backstepping design framework, the classification reconstruction idea and adaptive neural-based state observer are used to resolve the control design issue for MSVs. To improve the control performance, the serial-parallel estimation model is introduced to obtain a prediction error, and then a composite learning law is designed by embedding the prediction error and estimate of lumped disturbance. To reduce the mechanical wear of actuator, a dynamic event triggering protocol is established between the control law and actuator. Finally, a new dynamic event-triggered composite learning adaptive neural output feedback control solution is developed. Employing the Lyapunov stability theory, it is strictly proved that all signals in the closed-loop control system of MSVs are bounded. Simulation and comparison results validate the effectiveness of control solution.

Adaptive Control of Hyperbolic PDEs Coupled With a Disturbed and Highly Uncertain ODE

In this article, we address adaptive output-feedback boundary control of coupled hyperbolic partial differential equations (PDEs) with spatially varying coefficients and on a time-varying domain, whose uncontrolled boundary is coupled with a disturbed ordinary differential equation (ODE), where multiple parameters in the state matrix and the amplitudes of the harmonic disturbance are unknown. The asymptotic convergence to zero of the ODE state and the boundedness of the PDE states are ensured. This article is motivated by lateral vibration suppression of a mining cable elevator, where the interaction dynamics between the cage and the flexible guide is approximated as a viscoelastic system, including spring and damping, with unknown stiffness and damping coefficients. The performance of the proposed controller is tested in the application of the mining cable elevator by numerical simulation.

Event-Triggered Adaptive Output Feedback Control for Stochastic Nonlinear Systems With Time-Varying Full-State Constraints

This brief investigates the issue of event-triggered adaptive output feedback control for stochastic nonlinear systems with time-varying full-state constraints. Firstly, the unmeasurable states are estimated by the fuzzy observer. Secondly, the quartic time-varying barrier Lyapunov functions are constructed to avoid the violation of time-varying constraints. Thirdly, the command filter technique and error compensation mechanism are incorporated into the controller design to get over the issue of “explosion of complexity” and compensate the filtered errors. Then, the event-triggered mechanism is introduced to improve the efficiency in resource utilization. It is shown that the tracking error can converge to the desired neighborhood of the origin and all the signals in the closed-loop systems are bounded. Finally, the validity of the control strategy is demonstrated by a physical example.

Damping assignment of boundary controlled port-Hamiltonian systems with unknown open-loop damping

A damping assignment control law for infinite-dimensional port-Hamiltonian systems in one-dimensional space with actuators and sensors located at the spatial boundaries is proposed with the novelty that the boundary damping is unknown. This allows us to fix a desired decay of energy for the cases in which the system is over-damped, poorly damped, and even with negative damping. We propose an observer composed of an infinite-dimensional model and a finite-dimensional one for the state and parameter estimation. The asymptotic convergence of the observer is shown using LaSalle's invariance principle assuming that the trajectories are pre-compact. Finally, an observer-based adaptive output feedback controller is proposed for the damping assignment in the closed loop. The passivity of the closed-loop system is guaranteed with respect to the initial Hamiltonian of the system under the assumption that the observer is initialized identically to the current state and close enough to the parameter value. The transmission line is used to exemplify this approach.