Output Feedback Research Articles

Random Fourier features based nonlinear recurrent kernel normalized LMS algorithm with multiple feedbacks

The performance of kernel adaptive filtering algorithms (KAFs) with nonlinear recurrent structures surpasses traditional KAFs, attributed to the nonlinear contribution of feedback. Nevertheless, the existing nonlinear recurrent KAFs primarily focus on a single feedback output, potentially limiting their latent filtering capabilities. In this paper, we introduce a novel alternative, named nonlinear recurrent kernel normalized least-mean-square with multiple feedbacks (NR-KNLMS-MF), which leverages the information from multiple feedback outputs. Additionally, to tackle the computational complexity challenges associated with KAFs, we integrate random Fourier features (RFF) into NR-KNLMS-MF, resulting in an efficient variant called as RFF-NR-KNLMS-MF. Furthermore, we conduct a theoretical analysis of the mean-square convergence for RFF-NR-KNLMS-MF. Simulation results on time-series predictions demonstrate the superiority of our proposed algorithms over other competing alternatives, validating their effectiveness.

Dynamic Event-Triggered Control for Sensor–Controller–Actuator Networked Control Systems

We consider the problem of output feedback stabilization of LTI systems under event-triggering implementation. In particular, we assume that both the plant output and the control input are both transmitted over the network in an asynchronous manner. To that end, two independent event-triggering rules are constructed to generate the transmission instants of the submitted signals. The proposed approach is dynamic in the sense that the triggering rules involve internal dynamical variables to allow for further reduction in the communication load. Moreover, the inter-transmission times for both sides of the channel are lower bound by enforced dwell times to prevent the occurrence of Zeno phenomena. The problem is challenging due to mutual interactions between the sampling errors of the plant output and the control input, which requires careful handling to ensure closed-loop stability. The triggering mechanisms are designed by emulation as we first ignore the effect of the network and stabilize the plant in continuous-time. Then, the communication constraints are taken into account to derive the triggering conditions such that the stability of the networked control system is preserved. The required conditions are formulated in terms of a linear matrix inequality. The effectiveness of the technique is demonstrated by numerical simulations.

Robust [formula omitted] output feedback control for polynomial discrete-time systems

This paper aims to design a robust output feedback controller with H∞ performance for polynomial discrete-time systems (PDTS). This is due to the lack of research available on PDTS’ output feedback control especially when uncertainty is considered in the system. To be specific, the norm-bounded uncertainties are considered instead of polytopic uncertainties and then a so-called ‘scaled’ system is established to relate the robust H∞ and the nonlinear H∞ output feedback control problem. The integrator approach is introduced to overcome the nonconvexity issue when the polynomial Lyapunov function is selected. The controller is obtained by solving the sufficient conditions which are formulated in Polynomial Matrix Inequalities (PMIs) which is then converted into Sum of Squares (SOS) form. Semidefinite Programming (SDP) is used to obtain the results. Finally, the efficacy of the method is shown through numerical examples.

Memoryless Dual-Observer-Based Output Feedback Stabilization of Linear Systems With Input and Output Delays.

This article focuses on the dual-observer-based stabilization of linear systems with delays in both the inputs and outputs. By a model reduction approach the system can be converted to an equivalent linear system without delays, for which a dual-observer-based stabilizing controller can be designed. However, such a controller is infinite dimensional or memory-based. To solve such a problem, a modified memoryless dual-observer-based stabilizing controller is designed, and the closed-loop stability is proven under some additional conditions. Compared with the reduced-order observer-based controller, the dimension of the dual-observer-based controller is smaller if the system has more inputs than outputs. At the same time, the design approach is more challenging in proving closed-loop stability, as for example a more intricate Lyapunov-Krasovskii functional has to be constructed associated with the proposed approach. The proposed approach is applicable to both continuous-time and discrete-time systems. Numerical simulations validate the effectiveness of the proposed method.

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.

Observer-based robust servomechanism control of a three-phase induction motor drive

Induction motors (IMs) are widely used in industrial applications, but harmonic distortion in IM drives can lead to inefficiencies and reduced lifespan. Traditional control strategies and filtering methods have limitations in mitigating lower-order harmonics, particularly the 5th harmonic, which can generate reverse torque and increase overheating risk. Existing solutions often struggle to fully address these issues, especially in high-power applications with dynamic loading conditions. This paper proposes a control strategy to reduce voltage and current harmonics in a Z-source inverter (ZSI)-fed IM drive. The system incorporates an observer-based output feedback robust servomechanism controller, designed using Linear Quadratic Regulator (LQR) and Linear Matrix Inequality (LMI) techniques. It ensures closed-loop stability, precise tracking regulation, and effective disturbance rejection, while also featuring a speed adaptation mechanism for sensorless operation. Simulation results demonstrate significant harmonic mitigation, with the controller achieving 1.01% lower voltage THD and 1.03% lower current THD compared to conventional approaches. This improvement aligns with industry standards and outperforms several existing techniques. The proposed method contributes a comprehensive solution for harmonic mitigation in high-power IM drives, applicable to various industrial applications requiring high-quality power and precise speed control.

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.

Output synchronization of a class of complex dynamic networks: A reinforcement learning method

In this paper, to achieve the synchronization control for a class of complex dynamic networks with completely unknown system dynamics, a reinforcement learning output feedback algorithm based on state reconstruction is proposed. Given the high cost and complexity associated with obtaining the full state information, an output-based node state reconstruction method is employed for the first time in complex dynamic networks. The proposed method utilizes a sequence composed of a finite number of output data to reconstruct the current state. At the same time, the overall error system is constructed to handle the coupling relationship between nodes, to facilitate the controller design. Thereafter, considering the system dynamics are unknown, an algorithm based on reinforcement learning is proposed to ensure rapid synchronization of node outputs, and the convergence of proposed method is proven. Finally, the feasibility of proposed algorithm is corroborated through a simulation example and a multi-vehicle system.

Geometric stabilization of discrete‐time systems via dissipativity approach and output feedback control

AbstractGeometric stabilizability is studied for nonlinear discrete‐time systems (DTS) utilizing linear static output feedback (SOF). Initially, a linear SOF controller is designed such that nonlinear DTS achieve geometric stabilizability. Utilizing the concept of geometric QSR‐dissipativity (GQSR‐D), a condition both sufficient and necessary is proffered to guarantee geometric stabilizability for nonlinear DTS. Subsequently, the necessary conditions of linear matrix inequality (LMI) are delineated to ascertain the stability of a system employing linear SOF. A new sufficient and necessary condition is provided aiming to stabilize the linear time‐invariant (LTI) DTS based on GQSR‐D. Lastly, examples are furnished to elucidate the efficacy of the results, thereby reinforcing their practical applicability.

Distributed output feedback fuzzy secure consensus control for nonlinear MASs against DoS attacks

The multi-agent systems (MASs) exchange the communication information by the wireless sensor network and communication network, which are vulnerable to denial-of-service (DoS) attacks. Since the DoS attacks can interrupt the sensor channels and communication channels, and result in the instability of MASs, to study the distributed adaptive fuzzy secure consensus control (SCC) problem for nonlinear MASs under DoS attacks is momentous. This article investigates the distributed adaptive fuzzy SCC issue for nonlinear MASs with DoS attacks for sensor channels and communication channels. Because the sensor channels and communication channels are interrupted by DoS attacks, the system states, the leader and its high-order derivatives are unavailable, a switching-type fuzzy state observer and distributed observer are first established to estimate them. Based on the proposed switching-type fuzzy state observer and distributed observer, a distributed adaptive SCC approach is proposed by backstepping control design theory and fuzzy logic systems (FLSs). It is proved that the errors of the designed observers converge to zero's a small neighborhood and the controlled nonlinear MASs are stable. Moreover, each follower is able to track the leader. Finally, we apply the proposed control approach to multiple unmanned surface vehicles (USVs), the simulation results verify its effectiveness.

Dynamic Event-Triggered Prescribed-Time Consensus Tracking of Nonlinear Time-Delay Multiagent Systems by Output Feedback

Dynamic Event-Triggered Prescribed-Time Consensus Tracking of Nonlinear Time-Delay Multiagent Systems by Output Feedback

Finite-time adaptive control based on output feedback and auxiliary variables for time-varying parameters and disturbances

This paper presents a finite-time adaptive control scheme tailored for a diverse range of unknown discrete-time systems, with a specific emphasis on BLDC motor position control. The approach leverages an auxiliary variable within an output feedback framework, adeptly circumventing the causality problem without necessitating the use of state or disturbance observers. Through the application of finite-time convergence techniques, the control law demonstrates robust performance even in the presence of time-varying parameters and compact disturbances. Additionally, the proposed time-varying estimator, based on multi-input fuzzy emulated network (MiFREN), offers a practical solution for handling unknown dynamics and disturbances, making it well-suited for real-world applications where accurate mathematical models may be unavailable. Extensive experimental validation is conducted to evaluate the efficacy of the proposed scheme, including comparisons with existing controllers. The results highlight the superiority of the approach, particularly in terms of tracking accuracy and robustness against disturbances.

Adaptive output feedback time‐varying formation tracking of multi‐agent system with a leader of unknown input

AbstractThis paper researches the time‐varying formation tracking (TVFT) problem of linear multi‐agent systems (MASs). By designing a compensator, the problem of time‐varying formation can be considered as the output regulation problem. Thereby, the distributed output feedback controller combined with an adaptive technique is proposed. With this controller, follower agents achieve the desired time‐varying formation and follow the trajectory of the leader agent. Furthermore, extending the designed controller to the case where the leader agent equips with unknown control input. Using Lyapunov stability theory, it is demonstrated that under proper conditions the given protocol is implementable. Simulation example is presented at the end of the paper to illustrate the effectiveness of designed control mechanism.

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.

Robust static output feedback stabilization of stochastic discrete-time systems using stochastic geometric dissipativity

This paper delves into the realm of geometrically mean square stable in probability (GMSSiP) for stochastic discrete-time systems (SDTS). To achieve this, we construct a stochastic geometric QSR-dissipativity (SG-QSR-D) inequalities and devise a linear static output feedback (SOF) controller. This framework is instrumental in establishing GMSSiP and asymptotical stability in probability. Additionally, we establish sufficient and necessity conditions for GMSSiP of the SDTS by using SG-QSR-D. Expanding on this foundation, a linear SOF controller is developed by establishing connections between GMSSiP and SG-QSR-D. Specifically, we demonstrate that the asymptotical stability in probability of the closed-loop linear time-invariant (LTI) SDTS is ensured through a linear matrix inequality (LMI) condition and a linear SOF controller. Two illustrative examples are given to show the effectiveness of the obtained results.

Observer‐based output feedback control for a rotating beam with a general exosystem

Observer‐based output feedback control for a rotating beam with a general exosystem

Fractional‐order fixed‐time sliding mode control of robotic manipulators with robust exact differentiator

AbstractThis paper introduces a robust position tracking control structure of exoskeleton robots (ERs). An open environment and body flexibility are the main challenges in ER control system design. First, a fractional‐order nonsingular terminal sliding mode (FONTSM) controller is proposed for a robotic manipulator with uncertainty, where lumped unknown dynamics are computed by time‐delay estimation (TDE). Secondly, a robust exact differentiator (RED) is employed as the output feedback observer for the velocity sensorless robotic system. Thirdly, the stability of the FONTSM control system is demonstrated. It is proved that the system error can move to the specified value along the sliding mode surface at a fixed time and can reach the sliding mode surface after a finite time. Finally, simulations on a 2‐DOF robotic manipulator show the effectiveness of the fixed‐time control strategies. When a combined trigonometric function disturbance is applied, the joint angle error is reduced to 1% by the model‐free controller based on output feedback within approximately 1 s.

Global output feedback control for uncertain nonlinear systems with input quantisation and sensor gain perturbation

A new quantised control method via output feedback is proposed by establishing two pairs of time-varying coupled matrix inequalities. Compared to relevant quantised control results, our control method has the novelty: (i) the sensor gain perturbation is allowed to be large and nondifferentiable; (ii) the growth constraint of system nonlinearities is relaxed to depend on unmeasured states and x 1 -polynomial function; (iii) a dynamic scaling technique, instead of backstepping design, is introduced to reduce the computational complexity of controller design. In particular, by using a symbolic function (or hyperbolic tangent function), global asymptotic stability is achieved under any hysteresis quantiser.

Output feedback stabilization of logical dynamical systems with state‐dependent control constraints

Output feedback stabilization of logical dynamical systems with state‐dependent control constraints

Output feedback control of anti‐linear systems using adaptive dynamic programming

Output feedback control of anti‐linear systems using adaptive dynamic programming