Adaptive Output Feedback Control Design Research Articles

Differential neural network based adaptive average output feedback control design for dosage determination on cancer based immunotherapy treatment

Immunotherapy involves natural and synthetic substances to stimulate the body’s immune response. This treatment approach is practical not only for addressing immune deficiencies but also for combating malignancies. This paper describes a non-parametric approximated adaptive control process for managing cancer dynamics under immunotherapy treatment, utilizing a combination of a differential neural network (DNN) observer and nonlinear control techniques such as sliding mode and local optimal strategies. By employing the state estimation and control methods, close tracking between the estimated states provided by the neural network and the cancer model dynamics is possible. Internal model reconstruction and an observer provided by a variable structure model are essential for controlling unknown plants. Furthermore, the control design has successfully reduced tumor cells despite uncertainties and external perturbations affecting cancer dynamics. This robustness enhances the reliability of the proposed design. A virtual real-time scheme was developed to demonstrate this controller’s feasibility in real clinical scenarios. In this scheme, a simulated patient generates variables of immunotherapy dynamics as electrical signals, which are then analyzed by a real-time project.

Global adaptive event-triggered output feedback control for p-normal feedforward nonlinear systems with uncertain output function

This paper concentrates on the global adaptive event-triggered output feedback controller design for a class of p-normal feedforward nonlinear systems with uncertain output function. Due to the existence of unknown growth rate and uncertain output function, the considered uncertain nonlinear systems contain more uncertainties. To overcome this difficulty, a dynamic gain technique is introduced and an elaborate adaptive law of the gain is designed. Then, a completely time-varying event-based strategy is presented, which can effectively eliminate the effects caused by event-triggered errors. Since the presented event-triggered controller is not differentiable, a new analysis manner is developed to prove that the system is Zeno-free. Meanwhile, the closed-loop system(CLS) states are asymptotically convergent with the proposed strategy. Finally, a simulation example is given to illustrate the effectiveness of the proposed control scheme.

Inverse optimally adaptive neural output‐feedback control of stochastic nonlinear systems

SummaryIn this article, for a class of stochastic nonlinear systems with non‐strict feedback, a neural adaptive inverse optimal output feedback control design scheme is presented. First, according to the existing inverse optimal criterion, an auxiliary system is established. On this basis, a novel observer is built to evaluate the unpredictable states. Second, in the control process, neural networks (NNs) are applied to estimate the unknown functions. Based on NNs and the backstepping technology, an adaptive neural inverse optimal output feedback controller is established. It is indicated that the proposed scheme could ensure the semi‐globally uniformly ultimately bounded of the closed‐loop system and also achieve the objective of inverse optimality. Eventually, an example is applied to testify the feasibility of this scheme.

Output feedback and adaptive output feedback control design for input-affine nonlinear systems based on Riemannian metrics

This paper deals with output feedback and adaptive output feedback control design for input-affine nonlinear systems on the basis of Riemannian metrics. Firstly, an observer for the considered class of nonlinear systems is proposed. Then, it is shown that by combining the introduced observer with state-feedback controllers in a suitable way, output tracking can be achieved. Moreover, the obtained results are extended to be used for adaptive output feedback stabilization of uncertain nonlinear systems. The introduced adaptive controller is more general than the existing adaptive output feedback ones because it does not assume any constraint on the structure of the system and the parametric uncertainty. Simulation results are presented to verify the effectiveness of the proposed control methods.

Neural networks-based adaptive output-feedback control design for nonlinear systems with dead zone output and uncertain disturbances

This work focuses on the issue of neural networks-based adaptive output-feedback control for nonlinear systems with dead zone output and immeasurable states. Radial basis function neural networks (RBFNN) are utilised to approximate the unknown functions and an input-driven filter is used to estimate the immeasurable states. Nussbaum function is employed to address the issue of uncertain virtual control coefficient, which is brought by the dead zone in the output mechanism, and the presented control scheme requires only one adaptive law, making the structure of the controllers very realistic. Based on the approximation capabilities of NNs and the backstepping method, an adaptive controller is designed. Based on the Lyapunov stability theory, all signals in closed-loop systems are semi-globally uniformly ultimately bounded (SGUUB), and the tracking error converges to a small area near the origin. The effectiveness of the proposed adaptive control method is proved with the help of two examples.

Global adaptive event-triggered output feedback controller design for high-order nonlinear systems

This article focuses on the adaptive event-triggered output feedback stabilization problem for a class of high-order systems with uncertain output function. Firstly, an adaptive event-triggered mechanism with a dynamic gain is designed for the nominal system. Then the gain is employed into the observer and event-triggered controller to dominate the nonlinearities. Thirdly, it is proved that all system states converge to zero and the Zeno-behavior is excluded. Finally, a numerical example reveals the effectiveness of the proposed event-triggered control strategy.

Output feedback neural adaptive control design for nonlinear time-delay systems

This paper addresses the adaptive output feedback control design problem for a class of nonlinear systems with unknown state time delays by combining the dynamic gain and neural network. A novel reduced-order dynamic gain observer is introduced to estimate the unmeasured system states. Radial basis function neural networks (RBF NNs) are used to approximate unknown functions. An adaptive NN output feedback controller is designed based on the backstepping technique. By arranging the proper Lyapunov–Krasovskii functional, we prove that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. Finally, a physical example and a numerical example are given to prove the effectiveness of the proposed control scheme.

Prescribed Performance Fuzzy Adaptive Output Feedback Control for Nonlinear MIMO Systems in a Finite Time

This article studies the fuzzy adaptive output feedback control design problem for nonstrict feedback multi-input–multi-output nonlinear systems with full-states prescribed performance in finite time. Fuzzy logic systems are introduced to solve the problem of unknown nonlinear dynamics. And on this basis, a fuzzy-based state observer is designed to observe the unmeasurable state. Further, by combining the adaptive back-stepping control algorithm and the nonlinear filters, a novel dynamic surface control (DSC) method is proposed, which not only solves the computational complexity explosion problem inherent in the back-stepping control algorithm, but also improves the control performance, in contrast to the traditional DSC methods with linear filters. Besides, to further improve the tracking performance under the structure of full-states prescribed performance, a new Lyapunov function is designed considering the transform error constraint. Based on the Lyapunov theory, the stability of the closed-loop system is analyzed to ensure that all signals of the closed-loop system are semiglobal practical finite-time stability. Finally, to elaborate on the feasibility and effectiveness of the proposed control method, a simulation example is provided.

Adaptive output-feedback control design for maximum power point tracking of uncertain photovoltaic systems

The problem of maximum power point tracking (MPPT) control of uncertain photovoltaic (PV) systems is addressed in this paper, on the basis of an adaptive Kalman-like observer. The system is composed of a photovoltaic generator (PVG) supplying power to a DC centrifugal pump driven by a DC–DC boost converter. The PVG is connected to the converter by a long PV cable. Indeed, many PV power plant situations require that the PV panels be installed at a great distance from the converter for several reasons including the security and the property of the site and its exposure to good daily solar irradiance. This obviously leads to the difficulty of PVG output current and voltage measurement using ordinary sensors. Such quantities measurement being in fact necessary for MPPT algorithms and controllers design. Furthermore, the specific parameters of the long PV cable used, namely its resistance and inductance, could have a significant effect on the MPPT control efficiency if instead of PVG delivered voltage and current measurements, only those accessible at the converter side of the cable are used in MPPT controllers design. Therefore, this work aims at overcoming the two aforementioned issues by proposing an adaptive output-feedback control-based MPPT for PV systems. An adaptive Kalman-like observer providing online estimates of inaccessible state variables as well as of PV cable unknown parameters is firstly designed. Then, a backstepping control law is synthesized to meet the MPPT objective. The convergence of both adaptive observer and closed-loop system control has been established using Lyapunov approach and the effectiveness of the developed adaptive MPPT controller in achieving an accurate and robust MPPT control towards uncertainties of the PV cable parameters, has been validated through numerical simulations.

Adaptive output feedback controller design for high-order stochastic nonlinear systems with uncertain output function

This paper concentrates on the adaptive output feedback controller design for a class of high-order stochastic nonlinear systems(SNSs) with uncertain output function. Firstly for the nominal system, a homogeneous observer and output feedback controller is put forward through adding a power integrator method. Secondly, a dynamic gain technique is adopted into the observer and controller to guarantee the convergence of original system states and boundedness of the dynamic gain in probability. Besides, the proposed controller design scheme can be also broadened to upper-triangular SNSs. Finally, two numerical examples are provided to indicate the effectiveness of the proposed method.

Adaptive fuzzy output feedback controller design for a HAGC system with input saturation and output error constraints

The comprehensive effect of external disturbance, measurement delay, unmeasurable states and input saturation makes the difficulties and challenges for a HAGC system. In this paper, an adaptive fuzzy output feedback control scheme is designed for a HAGC system under the simultaneous consideration of those factors. At the first place, by state transformation technique, the dynamic model of a HAGC system is simply expressed as a strict feedback form, where measurement delay is converted into input delay. Then, an auxiliary system is employed to compensate for the effect of input delay. Furthermore, an asymmetric barrier Lyapunov function (BLF) is constructed to ensure the output error constraint requirement of thickness error and the fuzzy observer is established to solve unmeasurable states, unknown nonlinear functions at the same time. With the aid of backstepping method, adaptive fuzzy controller is developed to assure that the closed-loop system is semi-globally boundedness and the output error of thickness error doesn’t violate its constraint. At the end, compared simulations are carried out to verify the efficiency of the proposed control scheme.

Adaptive Output Feedback Control Design with Adaptive Parallel Feedforward Compensator and Feedforward Input via Kernel Method for Nonlinear System

It is well recognized that an adaptive output feedback control based on almost strictly positive real‐ness (ASPR) or output feedback strictly passivity (OFSP) property of the system has strong robustness with respect to disturbances and uncertainty. However, most practical systems do not have such properties. In order to realize an augmented ASPR system for designing adaptive output feedback controller, a kind of adaptive parallel feedforward compensator (PFC) design schemes have been proposed for linear systems. In this paper, we propose an adaptive PFC design scheme by Kernel method for nonlinear systems and provide output feedback passivity based adaptive output feedback control for nonlinear systems. The effectiveness of the proposed method will be confirmed through numerical simulations. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Switched-observer-based adaptive output-feedback control design with unknown gain for pure-feedback switched nonlinear systems via average dwell time

This paper studies the problem of adaptive fuzzy tracking control for a class of pure-feedback switched nonlinear systems with unknown gain. First, the studied system is handled by using the mean value theorem, the initial pure-feedback nonlinear systems become affine nonlinear systems. Then, the unknown signal is handled through a linear transformation, the Nussbaum-type functions are used to design an effective controller for the processed system. To avoid the issue of ‘explosion of complexity’ caused by mean value theorem and backstepping procedure, a first-order sliding-mode differentiator is employed to simplify the calculation. Combined with the average dwell time (ADT) method, a set of switching signals are given to sure the stability of the system. The Lyapunov theorem is used to verify that all signals are semiglobal uniformly ultimately boundness (SGUUB), and the errors can be regulated arbitrarily small. Finally, simulation results show the effectiveness of the proposed method.

Adaptive event-triggered PDE control for load-moving cable systems

Motivated by lateral vibration suppression of a mining cable elevator, which is a load-moving cable system, where the load moves along a viscoelastic guideway whose stiffness and damping coefficients are unknown, we present event-triggered adaptive output-feedback boundary control design of a hyperbolic PDE–ODE coupled system using the measurements at the PDE actuated boundary and the ODE, where the PDE subsystem is a class of 2 × 2 coupled hyperbolic PDEs with spatially-varying coefficients and on a time-varying domain, and a high uncertainty exist in the system matrix of the ODE subsystem at the uncontrolled boundary of the PDE. A continuous-in-time observer-based adaptive backstepping control law is designed where the control gains can be self-tuned to adjust the system matrix of the ODE into a given target system matrix, based on which an observer-based dynamic event-triggering mechanism is built and the existence of a minimal dwell-time is proved. The asymptotic stability of the overall adaptive event-based output-feedback closed-loop system is proved via Lyapunov analysis. In numerical simulation, the performance of the proposed controller is verified in lateral vibration suppression of a mining cable elevator.

Adaptive Dynamic Surface Control for a Class of Dead-Zone Nonlinear Systems via Output Feedback

The paper aims to address output feedback problem for a class of nonlinear systems subjected to unknown dead-zone. High-gain K-filters is firstly designed such that unavailable states of system can be reconstructed. Then, with the help of dynamic surface control (DSC) method, an adaptive output feedback controller design is established, which can alleviate undesired influence of the dead-zone and guarantee the semi-global stability of the system. Furthermore, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L_{\infty }}$ </tex-math></inline-formula> performance of tracking error is achieved by means of initialization technique. Ultimately, numerical and practical examples clarify the efficiency of the proposed solution.

Robust adaptive fuzzy control for non-strict feedback switched nonlinear systems with unmodeled dynamics

In this paper, a novel fuzzy adaptive output-feedback control design scheme is concerned for a class of non-strict feedback switched nonlinear systems. The considered nonlinear system contains arbitrary switching, unmodeled dynamics, unmeasured states, and unknown nonlinearities. In the design process, fuzzy logic systems (FLSs) are utilised to identify the unknown nonlinear functions. A state observer without depending on the switching signal is constructed to deal with the unmeasured states. By combining adaptive backstepping with dynamic surface control (DSC) technique, a fuzzy adaptive control scheme is presented to overcomes the ‘explosion of complexity’ problem. It is proved that all the signals of the closed-loop system can be bounded by the common Lyapunov function theory. Finally, a simulation example is provided to elaborate the feasibility and effectiveness of the proposed control method.

Adaptive singularity-free controller design of constrained nonlinear systems with prescribed performance

This paper investigates the controller design for the tracking problem of uncertain nonlinear systems with constraints on input and output. First, to avoid the full-state measurement, the high-gain observer is designed to estimate the unmeasured state. Compared to existing observer design for nonlinear systems, the high-gain observer design only requires a modified version of the Lipschitz condition. The output feedback controller is further presented on this basis. Second, to consider the transient constraints on the tracking performance, a barrier Lyapunov function with the user-defined time-varying performance is developed. In addition, the proposed controller design is shown to be free of control direction singularity. The convergence of the learning scheme and the boundedness of all the closed-loop signals during the learning phase are discussed theoretically. Finally, two simulation examples are conducted to verify the advantage of the proposed adaptive output feedback controller design.

Fuzzy Adaptive Output Feedback Control for MIMO Switched Nontriangular Structure Nonlinear Systems With Unknown Control Directions

This paper investigates an adaptive fuzzy output feedback control design problem for the switched multiple-input and multiple-output (MIMO) nontriangular structure nonlinear systems. The discussed system contains the unknown nonlinear dynamics, the unknown backlash hysteresis, the immeasurable states, and the unknown control directions under a class of switching signals. The fuzzy logic systems are utilized to learn the unknown nonlinear dynamics and construct an MIMO fuzzy switched nonlinear observer. By combining the property of the Nussbaum gain function with the Prandtl-Ishlinskii models, a novel observer-based fuzzy adaptive backstepping schematic design algorithm is presented. Furthermore, the stability of the whole controlled system is proved via Lyapunov stability theory and the average dwell time method. The simulation results are presented to verify the validity of the proposed control scheme.

Traffic congestion control for Aw–Rascle–Zhang model

This paper develops boundary feedback control laws to reduce stop-and-go oscillations in congested traffic. The macroscopic traffic dynamics are governed by Aw–Rascle–Zhang (ARZ) model, consisting of second-order nonlinear partial differential equations (PDEs). A criterion to distinguish free and congested regimes for the ARZ traffic model leads to the study of hetero-directional hyperbolic PDE model of congested traffic regime. To stabilize the oscillations of traffic density and speed in a freeway segment, a boundary input through ramp metering is considered. We discuss the stabilization problem for freeway segments respectively, upstream and downstream of the ramp. For the more challenging upstream control problem, our full-state feedback control law employs a backstepping transformation. Both collocated and anti-collocated boundary observers are designed. The exponential stability in L2 sense and finite time convergence to equilibrium are achieved and validated with simulation. In the absence of relaxation time and boundary parameters’ knowledge, we propose adaptive output feedback control design. Control is applied at outlet and the measurement is taken from inlet of the freeway segment. We use the backstepping method to obtain an observer canonical form in which unknown parameters multiply with measured output. A parametric model based on this form is derived and gradient-based parameter estimators are designed. An explicit state observer involving the delayed values of the input and the output is introduced for state estimation. Using the parameter and state estimates, we develop an adaptive output feedback control law which achieves convergence to the steady regulation in the L2 sense.

Fuzzy adaptive output feedback control for a class of switched non-triangular structure nonlinear systems with time-varying delays

ABSTRACTThis paper investigates an adaptive fuzzy output feedback control design problem for switched nonlinear system in non-triangular structure form. The discussed system contains unknown nonlinear dynamics, unmeasured states and unknown time-varying delays under a batch of switching signals. Fuzzy logic systems are utilised to learn unknown nonlinear dynamics and construct a fuzzy switched nonlinear observer. By combining the property of fuzzy basis function with Lyapunov–Krasovskii functional and the command filter, a novel observer-based fuzzy adaptive backstepping schematic design algorithm is presented. Furthermore, the stability of the closed-loop control system is proved via Lyapunov stability theory and average dwell time method. The simulation results are presented to verify the validity of the proposed control scheme.