Adaptive Output Feedback Control Research Articles

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.

Design and Implementation of an L1 Adaptive Proportional Output Feedback Controller

A new approach for output feedback L1 adaptive control based on a proportional adaptation law is presented. The effectiveness of this design is assessed in simulation and validated through real-time testing on an airfoil pitch control wind tunnel experimental rig. Experimental evaluation of the robustness of the controllers, assessed by introducing various disturbances into the control signals, shows that the adaptive control has a better performance compared to PID control, particularly in scenarios with reduced control effectiveness and time-varying disturbances. The experimental results demonstrate the efficacy of the proposed method in practical applications.

Learning-Based Adaptive Fuzzy Output Feedback Control for MIMO Nonlinear Systems With Deception Attacks and Input Saturation

Learning-Based Adaptive Fuzzy Output Feedback Control for MIMO Nonlinear Systems With Deception Attacks and Input Saturation

Secure Control for Switched Nonlinear Systems With DoS Attacks: A Switching Event-Triggered Adaptive Output-Feedback Control Method

Secure Control for Switched Nonlinear Systems With DoS Attacks: A Switching Event-Triggered Adaptive Output-Feedback Control Method

Adaptive output feedback control of stochastic systems with mismatched uncertainties input–output quantization

Inspired by the fact that remote control systems always demand to be quantified, and considering that there are always stochastic disturbances in the actual situation, this study discusses a sort of systems with unknown constants due to the existence of parameters that are untoward to measure. Therefore, in this research, a sort of stochastic nonlinear systems with input–output quantization mismatched uncertainties is studied by utilizing backstepping method. The difficulty is that the output signal becomes non-differential after being quantized. In addition, only the quantized output signal in the system can be regarded as known during the analysis. Therefore, the update of unknown parameters and the design of controller will grow arduous. In order to solve the above dilemma, a state observer and a projection operator are introduced. Moreover, in the design of the controller, the power form of Lyapunov function and differential method is employed to avoid the derivation of non-differentiable functions. Since the controller proposed in this study contains a small number of parameters that need to be designed, it is practical and can eliminate the problem of computational explosion. Simulation results demonstrate the effectiveness of the proposed scheme.

Adaptive output-feedback control for switched nonlinear systems with measurement sensitivity based on double-side event-triggering mechanisms

This article concentrates on the problem of adaptive event-triggered control with double-side event-triggering mechanisms for a family of switched nonlinear systems with measurement sensitivity. In order to circumvent the negative effect of intermittent transmission and sensor measurement sensitivity on the output, an extended system is constructed by introducing a first-order output filter. Then, an input-driven neural switched observer is designed to estimate unmeasurable system states. To deal with the mutual interaction between the triggering and the switching mechanisms and their negative effect on the positive lower bound of inter-execution intervals, an incremental compensation is incorporated in the event-triggering mechanism (ETM) at the output sensor side. At last, a novel event-triggered control scheme built on both sides of the controller and output is proposed, which exhibits superior efficacy compared to the one-side event-triggered control scheme. By using the methods of the average dwell time (ADT) and the backstepping design, it is guaranteed that all signals of the switched closed-loop system are semiglobally uniformly ultimately bounded (SGUUB), and the Zeno phenomenon is excluded. The efficacy of the proposed methodology is validated by using a numerical simulation and a one-link robot system.

Event-triggered adaptive output feedback control for hyperbolic PDEs: swapping-based design

Event-triggered adaptive output feedback control for hyperbolic PDEs: swapping-based design

Adaptive disturbance rejection neural output feedback control of hydraulic manipulator systems

This paper proposes an adaptive disturbance rejection neural output feedback control (ADRNC) scheme for multi-degree-of-freedom (n-DOF) hydraulic manipulator systems, subjected to unknown nonlinearities, external disturbances and unmeasured system states. The controller design is formulated by integrating Radial Basis Function Neural Networks (RBFNNs) with state and disturbance observers using the backstepping method. The RBFNNs are synthesized to handle unknown nonlinear functions and the residual estimate error, coupled with external disturbances, is estimated through the combination of state observer and disturbance observer. The unique features of the proposed controller lies in its capability to estimate both matched and unmatched lumped disturbances. The auxiliary disturbance estimation law is guided by the neural learning weights and estimated system states provided by state observers. By effectively utilizing neural networks to approximate and mitigate most nonlinear uncertainties, the workload of the disturbance observer is substantially reduced. High-gain feedback is therefore avoided and improved tracking performance can be expected. Moreover, to avoid the tedious analysis and the problem of “explosion of complexity” in the conventional backstepping method, we employ a first-order sliding-mode differentiator. Rigorous analysis via Lyapunov methods establishes the stability of the entire closed-loop system, ensuring guaranteed and satisfactory tracking performance under the integrated influence of unknown nonlinearities, unmeasured states, and external disturbances. Extensive simulations are conducted to verify the effectiveness of the nested control strategy.

Singularity-free adaptive control of discrete-time linear systems without prior knowledge of the high-frequency gain

This paper proposes a new output feedback model reference adaptive control (MRAC) method for discrete-time linear time-invariant systems with arbitrary relative degrees. The proposed method does not require any prior knowledge of the high-frequency gain represented by kp, thereby completely eliminating the common design condition in the traditional discrete-time MRAC framework: the sign of kp is known, as well as an upper bound on |kp|. Specifically, an output feedback adaptive control law is developed, which incorporates a time-varying gain function to effectively address the singularity issue. The developed adaptive control law leads to the derivation of a linear estimation error equation for the closed-loop system. Consequently, a gradient algorithm based parameter update law is directly formulated by utilizing the estimation error and some other available signals without requiring any prior knowledge of kp. In comparison to the traditional MRAC and Nussbaum function-based methods, it does not necessitate any additional design conditions or involve any transient performance issues, while still ensuring closed-loop stability and asymptotic output tracking for any given bounded reference signal. The simulation study showcases the design procedure and evaluates the efficacy of the proposed control method.

Non-negative scaled edge-consensus of saturated networked systems via adaptive output-feedback control

This paper investigates fully distributed scaled non-negative edge-consensus problems of networked systems with actuator saturation and unmeasurable internal states. By using the adaptive control method, output-feedback-based low-gain technique, and graph theory, a new adaptive algorithm is designed to obtain scaled edge-consensus conditions, under which the difficulties caused by the constraints on edge states and controllers are overcome. There are three interesting characteristics that any global information of networks is not used in this paper’s algorithm and result, including the number of vertexes and edges; the feasible solutions of the scaled edge-consensus conditions exist and can be easily obtained; the convergence rate of the controller is adjustable. Furthermore, the designed algorithm is expanded in the cases without actuator saturation. Finally, three examples are given to verify the theoretical results.

Adaptive asynchronous output feedback control for a class of switched nonlinear systems with a novel aperiodic dynamic sampling rule

Adaptive asynchronous output feedback control for a class of switched nonlinear systems with a novel aperiodic dynamic sampling rule

Finite-Time Adaptive Fuzzy Backstepping Control for Quadrotor UAV With Stochastic Disturbance

This paper proposes an adaptive fuzzy output feedback control approach for quadrotor unmanned aerial vehicles (QUAV) with stochastic disturbances, besides which we consider the unmeasurable states and unknown nonlinear functions. The QUAV system contains stochastic terms which are not bounded and not differentiable. By combining the It <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\hat{\rm \textbf{o}}$</tex-math> </inline-formula> differential equation with finite-time theory, a novel stochastic finite-time stability controller for QUAV is raised for the first time. The fuzzy logic system (FLS) is utilized to approximate the unknown nonlinear functions in the model. Dynamic surface control technology is introduced to reduce the complexity of differential. Furthermore, an observer with FLS is designed through the adaptive backstepping technique to estimate the immeasurable states. It is proved that this control approach can ensure that all states of the closed-loop QUAV system are semi-global and finite-time stable in probability. Meanwhile, the errors of system outputs and observer converge to a small neighborhood of origin. The simulation results show the effectiveness of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In practical applications, many systems are often affected by internal and external disturbances, and not all of them can be described by mathematical models, so they are called stochastic disturbances. Taking the QUAV as an example, when the system is disturbed by internal parameters, external environment, system input, sensor error, and other stochastic factors, the deterministic system can no longer accurately describe the controlled object. In this paper, the QUAV is regarded as a stochastic system for the first time, and a novel controller is designed to solve the above problems. In addition, the QUAV needs fast action in some applications, such as emergency collision avoidance, which has high requirements for the convergence performance of the controller. Therefore, studying the finite-time control for QUAV systems with stochastic disturbances is necessary. At the same time, this paper also considers the problem that the state is not measurable and the system contains unknown nonlinear equations.

Event-Triggered Adaptive Fuzzy Neural Network Output Feedback Control for Constrained Stochastic Nonlinear Systems.

This article investigates the problem of command-filtered event-triggered adaptive fuzzy neural network (FNN) output feedback control for stochastic nonlinear systems (SNSs) with time-varying asymmetric constraints and input saturation. By constructing quartic asymmetric time-varying barrier Lyapunov functions (TVBLFs), all the state variables are not to transgress the prescribed dynamic constraints. The command-filtered backstepping method and the error compensation mechanism are combined to eliminate the issue of "computational explosion" and compensate the filtering errors. An FNN observer is developed to estimate the unmeasured states. The event-triggered mechanism is introduced to improve the efficiency in resource utilization. It is shown that the tracking error can converge to a small neighborhood of the origin, and all signals in the closed-loop systems are bounded. Finally, a physical example is used to verify the feasibility of the theoretical results.

A new output feedback adaptive control method for fractional order systems with inaccessible state

In this paper, the output tracking control problem for fractional-order (FO) systems with inaccessible state is investigated, and a new fractional-order model reference adaptive control (FOMRAC) method is proposed. The designed FO adaptive update law is able to adjust the controller parameters online in real time according to the system output information. Compared with the classical output feedback FOMRAC method, the proposed FOMRAC strategy only needs the online updating of a scalar function, which significantly reduces the complexity of the controller design and the online computational burden, and can achieve better control effect. Furthermore, the scheme ensures the boundedness of the closed-loop signals and that the mean value of the squared norm of the output error converges to zero. Finally, simulation results are carried out to verify the effectiveness of the established approach.

Adaptive output-feedback global stabilisation for a class of more general nonlinear systems

This article investigates the global stabilisation problem via adaptive output feedback for a class of uncertain nonlinear systems. It is worth emphasising that the system under investigation possesses function control coefficients, input matching uncertainty and the polynomial-of-output growth rate, which is essentially different from the existing related literature, and hence brings some technical difficulties to the control design. To solve this problem, an adaptive output-feedback controller with dynamic high-gain and extended state observer is proposed. Remarkably, only one dynamic gain is introduced to overcome the function control coefficients, the serious uncertainty and the polynomial-of-output in the system growth rate. Moreover, under the designed controller, the states of the original system globally converge to zero. Two simulation examples are given to demonstrate the effectiveness of the proposed approach.

Adaptive output-feedback event-triggered formation control for underactuated ships with obstacle avoidance mechanism

This paper focuses on the adaptive output-feedback event-triggered control strategy for the formation tracking and obstacle avoidance activity of unmanned surface vessels (USVs) with unmeasurable velocity, constrained communication and actuator fault. By using the neural networks and minimal learning parameter, a novel output-feedback event-triggered controller is deigned, effectively alleviates constraints related to continuous feedback signal acquisition in dynamic systems. Subsequently, by incorporating an adjustable threshold, the event-triggered condition is established, thus facilitating updates to the state and adaptive law solely within the discrete-time domain. Then, only two aperiodic adaptive laws are derived to mitigate actuator faults and system uncertainties. As for the path planning, benefiting from the artificial potential field method, a novel obstacle avoidance mechanism is developed for obstacle avoidance and waypoint-based path navigation. Considerable effort has been made to guarantee the semi-globally uniformly ultimately bounded stability. Finally, two numerical simulations are illustrated to demonstrate the remarkable performance of the proposed control strategy.

An adaptive observer based output feedback prescribed‐time control of a class of nonlinear systems with unknown parameters

SummaryIn this paper, an adaptive observer based output feedback prescribed‐time control is designed for a class of nonlinear systems with unknown parameters. According to the characteristics of the nonlinear systems, a novel adaptive observer is first designed. Then by imposing parametric Lyapunov equations, a time‐varying high‐gain observer based output feedback adaptive controller is constructed, by which, it is proven that the states of both the observer and the original system can be driven to zero within any prescribed time. Moreover, the control signal is proven uniformly bounded by a scalar. Two numerical examples are illustrated to verify the effectiveness of the proposed method.

Inverse Optimal Adaptive Fuzzy Output Feedback Control for Nonlinear Systems With Output Quantization

Inverse Optimal Adaptive Fuzzy Output Feedback Control for Nonlinear Systems With Output Quantization