Output Feedback Control Method Research Articles

Robust H∞ Static Output Feedback Control for TCP/AQM Routers Based on LMI Optimization

This paper proposes a new static output feedback control method to address the congestion control problem in transmission control protocol networks using active queue management routers. Based on linear matrix inequality optimization, this method determines a static output feedback control law to minimize the norm of the transfer function between the controlled queue length of the buffer and the exogenous disturbance affecting the available link bandwidth. A linear matrix inequality formulation is presented as a sufficient condition to guarantee the closed-loop system’s asymptotic stability while maintaining disturbance rejection within a specified level, regardless of round-trip time delays. The proposed robust static output feedback control eliminates the need to measure or estimate all system states, thus simplifying practical implementation. The effectiveness of the proposed design method is demonstrated by applying it in a practical process, as illustrated through a numerical example.

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

Rapid and restricted swing control via adaptive output feedback for 5-DOF tower crane systems

Crane swing & relevant vibration are critical safety and efficiency issues to be addressed in construction automation, involving various disturbance and uncertainties. This paper presents for the first time a controller designed for 5-DOF tower crane systems with no velocity signals, and its effectiveness is experimentally verified. Compared to previous controllers, the new design utilizes an adaptive output feedback control method with accurate online gravity compensation, which solves the problems of unavailability velocity, noise amplification by numerical difference, and steady state error of cable in tower crane control. Additionally, the saturated control inputs are guaranteed by employing bounded functions. The results of this study unveil that the controller proposed in this paper can simultaneously achieve accurate trolley/jib/cable positioning, rapid payload swing suppression and elimination, as well as precise payload mass estimation, and guarantee control inputs constraints. This study provides a new approach for accurate control of tower crane systems.

A single-dynamic-gain scaling control method for nonlinear systems with measurement uncertainty

A single-dynamic-gain scaling output feedback control method is proposed with the novelty lying in (i) constructing a couple of observer-controller with the modification of scaling-gain exponent and (ii) introducing time-varying coupled matrix inequalities to deal with the large measurement uncertainty. By combining with the non-identification adaptive mechanism, the proposed dynamic output feedback controller is applicable to a class of nonlinear systems in the absence of the knowledge of upper bounds of unknown parameters in the nonlinearities and output equation, and global state asymptotical regulation is achieved with a bounded adaptive gain. Especially, the uncertain parameters in the nonlinearities and (non-differentiable) output function are both handled by a single-scaling gain. Moreover, the control method of this paper is non-backstepping and all design parameters are easy to obtain.

New results on dynamic output state feedback stabilization of some class of time-varying nonlinear Caputo derivative systems

In modern control theory applications, input–output feedback often arises and plays a crucial role in stabilizing any target control systems. However, in many practical applications, the knowledge of full information on the state of systems may not be available in general due to measurements. In order to control such types of systems, this paper uses a universal concept of real order calculus to stabilize by the method of dynamic output feedback control. Is it possible to design a dynamic output feedback stabilization for time-varying real order control systems? This work develops a new dynamic output feedback control method and proposes linear homogeneous time-varying control law to stabilize incommensurate nonlinear time-varying real order systems under the action of Caputo derivative with an addendum of ultimate random initial-time. We utilize the ideas of the comparison method and establish order-dependent asymptotic results associated with uniform Lipschitz continuous functions that provide stabilization by means of the dynamic output state feedback control method. We provide three examples that include a practical system and show the importance of some theoretical results in the demonstration to stabilize the problems by means of proposed control method.

Observer-Based Adaptive Control for Trajectory Tracking of AUVs with Input Saturation

In this paper, an observer-based adaptive control method is investigated for the horizontal trajectory tracking of autonomous underwater vehicles (AUV) with input saturation and system disturbances. Firstly, the desired surge speed and trajectory angle are established, which could decouple the tracking error subsystem and avoid the complex form. Secondly, the input saturation is approximated by a smooth function, and a nonlinear extended states observer (NESO) is designed for estimating system disturbances. Based on the command filtered backstepping technique, which can avoid the explosion caused by the derivative of the virtual control, an observer-based adaptive output feedback control method is developed, and an auxiliary system is applied to compensate for filtered tracking errors, input saturation bias, and observer errors. Finally, simulation results show the proposed method has good robustness in the face of system uncertainties, and the error is nearly 33.3% smaller than that of other control methods when meeting sudden trajectory changes. A good control performance is guaranteed.

Improved Nonlinear Extended Observer Based Adaptive Fuzzy Output Feedback Control for a Class of Uncertain Nonlinear Systems With Unknown Input Hysteresis

This study focuses on the problem of adaptive fuzzy dynamic surface output feedback control for a class of uncertain nonlinear systems subjected to unknown input hysteresis. A Prandtl–Ishlinskii (PI) model is applied to the uncertain nonlinear system for describing the unknown input hysteresis, making the controller design feasible. In addition, a nonlinear extended state observer (NESO) is designed for simultaneously estimating the unmeasurable states and generalized disturbances, including the nonlinear hysteresis term of the PI model and external disturbances. In addition, a novel nonlinear function is designed to replace the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$fal(\cdot)$</tex-math></inline-formula> function of the general NESO to address a modification that increases the convergence speed. Considering the incorporation of the improved nonlinear extended state observer (INESO), an adaptive output feedback control scheme is proposed based on fuzzy logic system and dynamic surface techniques. A command filter is employed to avoid the “explosion of complexity” problem inherent in the backstepping technique, while compensating the filtering error caused by adopting the filter. The Lyapunov approach is used to demonstrate the stability of the entire closed-loop system. Experiments regarding a piezoelectric micro–positioning stage are conducted, the results of which illustrate that the proposed adaptive fuzzy output feedback control method can guarantee a satisfactory tracking performance.

Asynchronous Event-Triggered-Based Control for Stochastic Networked Markovian Jump Systems With FDI Attacks

In this article, a new asynchronous event-triggered-based output-feedback control method is proposed for a special type of networked Markovian jump systems susceptible to network delays and attacks. A novel controller is designed to make allowances for four cases that are common in practical situations: 1) a network delay; 2) false data injection (FDI) attacks; 3) asynchrony between system modes and controller modes; and 4) an event-triggered scheme. First, to avoid traditional assumptions about system delays, a novel Lyapunov function is proposed, based on which a new stability theory is formulated to guarantee exponential stability. Second, an FDI attack is represented as a stochastic process, which can be dealt with properly by the designed controller. Third, a new delay-based event-triggered scheme is brought into the controller design to reduce the network bandwidth consumption and save the network transmission resources. Then, an asynchronous controller is engineered with the aid of a hidden-Markov model, which can extend the application scope of the proposed method. Note that we put the above four factors into one framework, which implies a co-design of an anti-attack controller and an event-triggered scheme is developed. Finally, an operational amplifier circuit is simulated to verify the feasibility and superiority of the designed controller.

Global tracking stabilisation via sampled-data output feedback for nonlinear systems with unknown measurement sensitivity

In engineering practice, sensors commonly have limited sensitivity errors, which are allowed in the design process. This paper uses a sampled-data output feedback control method so that the problem of global tracking stabilisation of a class of nonlinear systems with unknown measurement sensitivity can be solved. A sampled-data output feedback control law and a sampled-data observer are constructed with the help of output feedback domination method. By choosing the domination gain and sampling period in place, the resultant control law can globally stabilise the system even under the influence of unknown measurement sensitivity.

Collision-free output-feedback super-twisting control of robotic surface vehicles for coordinated path following with experiments

This paper addresses the coordinated path following (CPF) control of robotic surface vehicles (RSVs) in the presence of multiple obstacles and lumped disturbances over a resource-constrained wireless network. A collision-free output-feedback super-twisting control architecture is proposed for CPF without using velocity measurements. Specifically, robust exact differentiator observers are designed at first to recover the unmeasured velocities, and estimate the lumped disturbances, simultaneously. Next, anti-disturbance output-feedback super-twisting control laws are proposed to achieve individual path following in earth-fixed reference frame, and an adaptive potential field with a user-defined collision hazard degree is introduced to avoid the collisions with obstacles. To reduce the network traffic, self-triggered path updating laws are then designed for avoiding the continuous transmitting and listening of neighboring RSVs. The stability analysis shows that the closed-loop system is input-to-state stable outside the collision avoidance region, and Zeno behavior will not happen. Experiments are finally conducted to validate the efficacy of the proposed collision-free output-feedback super-twisting control method for the self-triggered CPF of RSVs.

Equivalent-input-disturbance rejection-based adaptive motion control for pneumatic artificial muscle arms via hysteresis compensation models

With outstanding flexibility, pneumatic artificial muscles (PAMs) have shown unique advantages in human–robot interaction environments. However, asymmetric hysteresis nonlinearities, unknown transient or persistent time-varying disturbances, etc., seriously degrade control performance/robustness of PAM arms, and increase actuation failure risks. Meanwhile, existing disturbance rejection methods either rarely handle matched/unmatched disturbances, or require known disturbance dynamics. These defects limit the disturbance rejection ability in practical applications. To this end, a new adaptive output feedback control method is proposed in this article, which achieves efficient positioning and tracking control of PAM arms. Without requiring a priori knowledge of inverse plant models and disturbance upper bounds, the proposed method further enhances robustness against uncertainties and disturbances. Specifically, a generalized Bouc–Wen hysteresis loop is used to describe the asymmetric dynamic force of PAMs. Also, a new adaptive law is designed to compensate for the linearized parameters online. In particular, by applying a modified state observer to derive the equivalent-input-disturbance estimation solution, this article firstly realizes state observation, parameter estimation, and unknown transient/persistent external disturbance rejection simultaneously. The rigorous stability analysis demonstrates that the tracking errors converge to small neighborhoods of the origins. Finally, hardware experimental results with comparisons validate the effectiveness and robustness superiority of the proposed method.

Prandtl–Ishlinskii model based event-triggered prescribed control: Design and application to piezoelectric-driven micropositioning stage

Realization of micro-scale tracking accuracy and efficient utilization of the communication channel is crucial to the wide application of piezoelectric-driven micropositioning stages (PDMSs). In view of this, an event-triggered prescribed time output-feedback control method is proposed in this study. As a type of nonlinearity that severely deteriorates the tracking performance of the PDMS, the hysteresis behavior is investigated. To describe the hysteresis characteristics, a modified function-based Prandtl–Ishlinskii (MFPI) model is proposed. Subsequently, a three-order electromechanical model of the PDMS combined with the MFPI model is established, and an event-triggered prescribed time adaptive control (ETPTAC) scheme is developed. In the controller design process, a prescribed settling time regulator (PSTR) is innovatively designed. With the help of the PSTR, the tracking error can converge to a predefined accuracy in a prescribed time. To address the hysteresis nonlinearity, an auxiliary system based on the established MFPI model is introduced. In addition, an event-triggered mechanism is used to save the communication resources. Finally, the modeling and control performances of our proposed approaches are confirmed on the PDMS.

Model-Free H∞ Output Feedback Control of Road Sensing in Vehicle Active Suspension Based on Reinforcement Learning

Abstract An active suspension system ensures the controllability of a vehicle in the vertical direction, which greatly enhances the control redundancy and safety of an intelligent driven vehicle. However, many calibrated model parameters are not conducive to the application of optimal control. To reduce the control cost of active suspension, a model-free H∞ output feedback control method is studied in this research. First, the optimal governing equation of the active suspension is transformed into a zero-sum game problem of two players, and an off-policy reinforcement learning algorithm is established to solve the game algebraic Riccati equation. This method could overcome the disadvantage of constant interactions between Q-learning and the environment. Secondly, with the consideration that some state variables are difficult to measure, a data-driven H∞ output feedback controller is designed using road sensing information and historical measurement data, and the Bellman equation of the system is solved using the least squares method to obtain the optimal control solution of the active suspension. The simulation and rapid prototype experimental results show that the proposed method could produce the optimal control strategy of the system without model parameters, overcome the strong dependence and sensitivity of traditional design methods to model parameters and improve the robust control effect of the active suspension.

Neural network-based robot nonlinear output feedback control method

In order to improve the accuracy of robot terminal pose tracking and the anti-interference performance of robot nonlinear motion path control, a nonlinear output feedback control method based on neural network is proposed. Construct the coordinate transformation matrix of the connecting rod, calculate the linear and angular velocity of the nonlinear motion of the robot, then calculate the sum of the kinetic energy of each connecting rod of the robot, and establish the motion equation of the robot. The structure of BP neural network is analyzed, and the motion equation is solved by BP neural network. Finally, a Fractional Order PID controller is designed and BP neural network is constructed to control the nonlinear motion equation of the robot to complete the output feedback control of the robot. The experimental results show that the end attitude tracking error of this method is the smallest, and it best fits the actual nonlinear trajectory of the robot. It shows that this method can accurately track the end posture of the robot, and can still effectively control the trajectory of the robot in the interference environment.

Fuzzy-Model-Based Lateral Control for Networked Autonomous Vehicle Systems Under Hybrid Cyber-Attacks.

This article addresses the problem of lateral control problem for networked-based autonomous vehicle systems. A novel solution is presented for nonlinear autonomous vehicles to smoothly follow the planned path under external disturbances and network-induced issues, such as cyber-attacks, time delays, and limited bandwidths. First, a fuzzy-model-based system is established to represent the nonlinear networked vehicle systems subject to hybrid cyber-attacks. To reduce the network burden and effects of cyber-attacks, an asynchronous resilient event-triggered scheme (ETS) is proposed. A dynamic output-feedback control method is developed to address the underlying problem. Conditions are derived to obtain the output-feedback controller and resilient asynchronous ETS such that the closed-loop switched fuzzy system is globally exponentially stable. Examples are provided to demonstrate the effectiveness and merits of the proposed new control design techniques.

An Event-Triggered Predefined Time Decentralized Output Feedback Fuzzy Adaptive Control Method for Interconnected Systems

This article investigates the event-triggered predefined time output feedback control design problem for nonlinear interconnected systems with nonstrict feedback control structures. Compared with the existing event-triggered output feedback control design schemes, the most significant contribution of this article is that the system stability time can be preset directly. Fuzzy logic systems (FLSs) and FLSs-based state observer deal with unknown nonlinear dynamics and unmeasured states. Combining with dynamic surface control technology and event-triggered mechanism based on switching threshold strategy, an event-triggered predefined time decentralized output feedback control method is proposed, in which a predefined time filter is designed to solve the computational complexity problem. In addition, the algebraic loop problem and control singular problem are also solved, respectively, by applying the property of fuzzy basis function and L’Hospital’s rule. Utilizing the predefined time Lyapunov stability theory, the system stability analysis is given. Finally, the effectiveness and practicability of the proposed control method are verified by practical system simulation cases.

ASPR-based Output Feedback Control with Virtual PFC for Output Tracking

Nowadays, the output feedback control method based on the Almost Strictly Positive Real (ASPR) property gets many attentions and has been researched widely. ASPR models can be stabilized by applying simple output feedback control; so the designed controllers have a simple structure. However, the systems have to satisfy quite strict conditions in order to obtain ASPR-ness, although almost all practical systems do not have the ASPR property. Therefore, for relaxing those conditions, the introduction of a Parallel Feedforward Compensator (PFC) has been proposed. This method can render the resulting augmented system ASPR. Up to now, several PFC design methods have been proposed, and one of them is an adaptive PFC design scheme. This technique has a feature that it can design a PFC automatically by utilizing online data. Furthermore, for the purpose of output regulation, the control design methods with an adaptive PFC have been proposed. Unfortunately, however, in almost all schemes, the discussion on the convergence of actual errors has not been conducted. Therefore, in this paper, introducing a virtual PFC model and an auxiliary input for ensuring ASPR-ness, a new ASPR-based output feedback control method is proposed, and the stability analysis and convergence of the actual error are discussed. Finally, the effectiveness of the proposed method is confirmed via numerical simulations.

三重タンクシステムのイベントトリガロバスト出力フィードバック制御

An event-triggered robust output feedback control method is proposed for water level tracking of an uncertain three-tank system by using only the level sensor of the target tank. The system model is transformed into a canonical form with uncertainties by coordinate transformation, and the canonical system's state variables are estimated by a higher-order sliding mode differentiator based on the measurement of the target tank's water level. Then an event-triggered robust output feedback controller is constructed. The triggering condition is derived by taking the control gains and event-triggered sampling errors into account, and the control performance is rigorously analyzed. Furthermore, it is shown that the zeno behavior is also excluded. Finally, the effectiveness of the proposed method is verified by experimental studies on a real three-tank system.

A Cyclic-Small-Gain-Based Adaptive Fuzzy Control of Interconnected Systems

This article investigates a novel adaptive fuzzy decentralized output-feedback control method for a class of uncertain large-scale interconnected nonlinear systems with unknown dynamics and immeasurable states. By employing fuzzy logic systems, the unknown nonlinear dynamics are estimated. Moreover, the immeasurable states are approximated by constructing a fuzzy decentralized state observer. Based on the Lyapunov stability theory and decentralized backstepping technique, the Lyapunov functions are constructed in a recursive way for each subsystem. In the last step of our process, an overall Lyapunov function is proposed to realize the control objective. Differing from other works, the interconnection influence of the large-scale system is compensated with a more relaxed assumption by introducing the cyclic-small-gain condition. Under the proposed control method, the closed-loop system is capable of being semiglobally uniformly ultimately bounded. Furthermore, the observer and stabilizing errors are able to be bounded by an arbitrarily small residual set around the origin. Finally, a simulation with comparison is provided to illustrate the viability of the presented control scheme.

Event-triggered fuzzy finite-time reliable control for dynamic positioning of nonlinear unmanned marine vehicles

This paper studies the fuzzy finite-time reliable dynamic positioning (DP) control for nonlinear unmanned marine vehicles. Firstly, the motion dynamics of nonlinear unmanned marine vehicles is modeled by the Takagi-Sugeno (T-S) fuzzy system. Secondly, a fuzzy state observer is designed to estimate the immeasurable position and velocity of unmanned marine vehicles. In order to decrease the frequency of data transmission of actuators and sensors, a dynamic event-triggered mechanism is presented based on unmanned marine vehicles information and the estimation error. Then, a fuzzy event-triggered reliable output feedback control method is presented by finite-time theory to tackle the actuator faults problem occurring in unmanned marine vehicles. The sufficient finite-time stable conditions in the form of LMIs are established via constructing finite-time Lyapunov-Krasovskii functional. Ultimately, the computer simulation results and comparisons with the existing control methods verify the advantages of the presented control method.