Feedback Control Problem Research Articles

A Comprehensive View of the Solvability and Stability of a Feedback Control Problem with a State-Dependent Delay Implicit Pantograph Equation

In this paper, we examine the existence of a unique solution of a feedback control problem with an implicit state-dependent pantograph equation. Additionally, the study implements the problem’s Hyers-Ulam stability and the continuous dependence of the unique solution on the initial data and the parameters. Furthermore, we investigate this problem in the absence of feedback control. We also provide some examples to illustrate our results.

Finite Time Stability Analysis and Feedback Control for Takagi–Sugeno Fuzzy Time Delay Fractional-Order Systems

This study treats the problem of Finite Time Stability Analysis (FTSA) and Finite Time Feedback Control (FTFC), using a Linear Matrix Inequalities Approach (LMIA). It specifically focuses on Takagi–Sugeno fuzzy Time Delay Fractional-Order Systems (TDFOS) that include nonlinear perturbations and interval Time Varying Delays (ITVDs). We consider the case of the Caputo Tempered Fractional Derivative (CTFD), which generalizes the Caputo Fractional Derivative (CFD). Two main results are presented: a two-step procedure is provided, followed by a more relaxed single-step procedure. Two examples are presented to show the reduction in conservatism achieved by the proposed methods. The first is a numerical example, while the second involves the FTFC of an inverted pendulum, which exhibits both symmetrical dynamics for small angular displacements and asymmetrical dynamics for larger deviations.

Observer-based adaptive fuzzy quantized fault-tolerant control of nonstrict-feedback nonlinear systems with actuator and sensor faults

This article emphasises the adaptive quantised fault-tolerant output feedback control problem for one type of nonstrict-feedback nonlinear systems suffering from input and output quantisation as well as actuator and sensor faults. By introducing a bounded time-varying function (BTVF), a generalised intermediate-variable estimator (IVE) is designed to estimate actuator bias fault. For the scaling measurement sensor fault, by applying a projection operator containing the measured output quantised signal, a novel gain-compensated state observer is constructed. In addition, relying on fuzzy logic system (FLS) and intelligent adaptive algorithm, a fuzzy fault-tolerant controller is devised to handle dual faults and quantisation. After theoretical proof, the presented control framework can realise that the closed-loop system is semi-globally uniform ultimately bounded (SGUUB), while the tracking error is within an adjustable area. Lastly, the validity and superiority of the suggested scheme is confirmed by a relevant simulation example.

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.

Polynomial Fuzzy Observer-Based Feedback Control for Nonlinear Hyperbolic PDEs Systems.

This article explores the observer-based feedback control problem for a nonlinear hyperbolic partial differential equations (PDEs) system. Initially, the polynomial fuzzy hyperbolic PDEs (PFHPDEs) model is established through the utilization of the fuzzy identification approach, derived from the nonlinear hyperbolic PDEs model. Various types of state estimation and controller design problems for the polynomial fuzzy PDEs system are discussed concerning the state estimation problem. To investigate the relaxed stability problem, Euler's homogeneous theorem, Lyapunov-Krasovskii functional with polynomial matrices (LKFPM), and the sum-of-squares (SOSs) approach are adopted. The exponential stabilization condition is formulated in terms of the spatial-derivative-SOSs (SD-SOSs). Additionally, a segmental algorithm is developed to find the feasible solution for the SD-SOS condition. Finally, a hyperbolic PDEs system and several numerical examples are provided to illustrate the validity and effectiveness of the proposed results.

Attack-compensated asynchronous output feedback control for stochastic switching systems with sojourn probability

This study addresses the problem of attack-compensated asynchronous output feedback control for stochastic switching systems with sojourn probability. Unlike traditional Markov/semi-Markov models that rely on transition probabilities, a novel switching rule is introduced that focuses on sojourn probability information associated with the target mode and sojourn time, which are easier to obtain than the well-known transition probabilities. Considering the vulnerability of stochastic switching systems to cyber-attacks, where the system state becomes unobservable and difficult to manage, a compensation-based output feedback controller framework is proposed. Using the Lyapunov method and stochastic analysis, sufficient conditions are provided to ensure system stability. Finally, the effectiveness and applicability of the developed approach are demonstrated using an F-404 aircraft engine system model.

Output-feedback controllers with guaranteed ℒ2-gain for continuous-time Lur'e systems using noncausal Zames–Falb multipliers

This paper focuses on the problem of dynamic output feedback control for continuous-time Lur'e systems with slope-bounded nonlinearities, particularly addressing the closed-loop L 2 gain. The synthesis framework is derived using integral quadratic constraints. The following contributions are highlighted: (i) assessment of stability and L 2 gain through causal, anticausal, and noncausal Zames–Falb multipliers; (ii) technical enhancements to improve efficiency and to reduce the conservativeness of two different classes of multipliers regarding the L 1 norm evaluation; (iii) the synthesis conditions are presented in terms of an iterative procedure based on linear matrix inequalities, allowing arbitrary order for both the controller and the multipliers. The proposed achievements are validated through numerical examples, demonstrating the efficacy and flexibility of the approach compared to existing methods in the literature.

Adaptive control for a class of stochastic nonlinear time-delay systems with unknown control coefficients

This paper mainly investigates the adaptive state feedback control problem for a class of stochastic nonlinear systems with time-delay and unknown control coefficients. The proposed control scheme leverages the dynamic gain technology and Nussbaum function to develop a state feedback controller, aiming to simplify its implementation and eliminate the influence of unidentified time-varying control coefficients. Through the utilization of suitable Lyapunov–Krasovskii (L–K) functionals and stochastic stability theories, it is demonstrated that the designed control mechanism guarantees the boundedness in probability for all closed-loop signals, ultimately regulating the state of the plant to zero. The availability and superiority of the developed adaptive management strategy are further certified through simulation results.

Terminal Synergetic Control for Plate Heat Exchanger

Terminal synergetic control (TSC) is proposed as a control strategy for the temperature management of a plate heat exchanger. The controller is designed by incorporating a selected macro variable with a time-varying sliding surface. The primary objective is to maintain precise control over the outlet temperature of the cold water. To assess the convergence characteristics of the newly proposed TSC approach, the simulation results achieved using TSC featuring a time-varying macro variable are compared to those obtained from the conventional synergetic control (SC) method. With an appropriate macro variable, the simulation results indicate a notable improvement in the convergence rate provided by our designed TSC method, compared to the conventional one. The desirable property of control input, the chattering-free condition, achieved by both TSC and SC approaches emphasizes the advantage of the synergetic control-based techniques over the conventional sliding mode controller. In conclusion, synergetic control-based techniques offer superior potential solutions for nonlinear feedback control problems.

Robust stability and memory feedback control for uncertain switched TS fuzzy systems with multiple time-varying delays

This paper focuses on the robust stability and the memory feedback control problems of uncertain switched fuzzy systems with mode-dependent multiple time-varying delays. Thus, by constructing a novel Lyapunov functional, the aggregation techniques, and the Borne and Gentina criterion, new algebraic robust stability and stabilization conditions under arbitrary switching have been established. Compared with existing results, the proposed criteria are explicit and simple to apply without searching a Common Lyapunov Function (CLF) through Linear Matrix Inequalities (LMIs), considered a difficult matter in this case. Finally, the practicality and effectiveness of the proposed theory are demonstrated through both a simulation example and a real example (robot arm).

Bilateral synchronization control of networked teleoperation robot system

AbstractBilateral synchronization control for Network Teleoperation Robot System (NTRS) in discrete domain is discussed in this paper, where time delay, data loss and disorder, and quantization error coexist. Firstly, it is assumed that time delay and data loss are asymmetric and randomly vary in the master–slave channel and slave–master channel according to different Markov jump change rules. By introducing the virtual variables, a clever normalization method is proposed for time delay or data loss. It not only uniforms time delay and data loss into a same framework, but also effectively handles the problem of data disorder. And then, a logarithmic quantizer is designed to dispose quantization error. Meanwhile, utilize the sector bound method to describe the quantization error and transform the quantization feedback control problem into a robust control problem, so the familiar robust control methods can be adopted to solve quantization problem. In addition, human and environmental forces are treated as external disturbances and processed using neural network techniques. Subsequently, stochastic stability and synchronization control property are guaranteed by the designed analogous PD controller that consists of proportion, derivative, and uncertainty estimation items. Finally, validity of the proposed method is certified by some simulation examples.

Reinforcement learning informs optimal treatment strategies to limit antibiotic resistance

Antimicrobial resistance was estimated to be associated with 4.95 million deaths worldwide in 2019. It is possible to frame the antimicrobial resistance problem as a feedback-control problem. If we could optimize this feedback-control problem and translate our findings to the clinic, we could slow, prevent, or reverse the development of high-level drug resistance. Prior work on this topic has relied on systems where the exact dynamics and parameters were known a priori. In this study, we extend this work using a reinforcement learning (RL) approach capable of learning effective drug cycling policies in a system defined by empirically measured fitness landscapes. Crucially, we show that it is possible to learn effective drug cycling policies despite the problems of noisy, limited, or delayed measurement. Given access to a panel of 15 [Formula: see text]-lactam antibiotics with which to treat the simulated Escherichia coli population, we demonstrate that RL agents outperform two naive treatment paradigms at minimizing the population fitness over time. We also show that RL agents approach the performance of the optimal drug cycling policy. Even when stochastic noise is introduced to the measurements of population fitness, we show that RL agents are capable of maintaining evolving populations at lower growth rates compared to controls. We further tested our approach in arbitrary fitness landscapes of up to 1,024 genotypes. We show that minimization of population fitness using drug cycles is not limited by increasing genome size. Our work represents a proof-of-concept for using AI to control complex evolutionary processes.

Output feedback H∞$$ {H}_{\infty } $$ control of linear systems with infinite distributed input delays

AbstractThis article investigates the output feedback control problem of linear systems with infinite distributed input delays. An observer‐based output feedback (OBOF) controller is proposed via the predictor feedback approach, and a sufficient condition for the existence of the OBOF controller is given in terms of a set of linear matrix inequalities. It is shown that the resulting closed‐loop system is globally exponentially stable with a prescribed performance. The obtained results include some results on the same problem for linear systems with bounded distributed input delays as special cases. Two numerical examples are finally presented to illustrate the effectiveness of the proposed controller.

Uncertainty-resilient constrained rendezvous trajectory optimization via stochastic feedback control and unscented transformation

This paper proposes an innovative approach for constrained rendezvous trajectory optimization (CRTO) using stochastic optimal feedback control and unscented transform (UT) uncertainty quantification. The method overcomes limitations of traditional deterministic CRTO solutions that suffer from uncontrolled terminal state error growth and severely deteriorated path constraint satisfaction when initial state uncertainty, dynamics uncertainty, and navigation uncertainty are considered. The approach involves constructing a stochastic optimal feedback control (SOFC) problem with chance constraints and introducing linear feedback control to regulate both the mean and variance of terminal state errors. UT is employed to approximately quantify the state errors and their propagation, transforming the SOFC problem into an unconstrained deterministic optimization (UDO) problem. Differential dynamic programming (DDP) is then used to solve the UDO problem. The obtained stochastic optimal solution provides a robust rendezvous trajectory and a corresponding explicit closed-loop guidance law, improving terminal accuracy and path constraint satisfaction in the presence of various considered uncertainties. The influence of different term weights in the objective function on terminal accuracy and path constraint satisfaction is also studied. The effectiveness of the proposed approach is verified through Monte Carlo simulation, demonstrating the robustness of the closed-loop control strategy. The results highlight the potential of the method in enhancing terminal accuracy and path constraint satisfaction in uncertain rendezvous scenarios.

Event‐triggered output feedback H∞$$ H\infty $$ control for Markovian jump networked control systems with signal quantization

AbstractThis article studies the output feedback control problem for event‐triggered Markovian jump networked control systems with signal quantization. First, a Lyapunov–Krasovskii function is constructed, which contains time delay, upper bound of time delay and conditional transition probability information of Markovian jump systems. Second, when using the Wirtinger's inequality and the Reciprocally convex inequality to estimate the upper bound of the delay of the weak infinitesimal generator operator of the Lyapunov–Krasovskii function, the event‐triggered scheme and the signal quantization scheme are introduced. Finally, the stability criterion of the closed‐loop system of event‐triggered Markovian jump networked control systems and the design method of the output feedback controller of disturbance attenuation level are given in the form of linear matrix inequalities (LMIs). The effectiveness of the proposed method is verified by two numerical examples.

An Economic Model Predictive Control for Knowledge Transmission Processes in Multilayer Complex Networks.

In this article, we study the optimal feedback control problems of knowledge dissemination processes in multilayer complex networks. First, a node-based model is established in multilayer complex networks and two collaborative control strategies are exerted to increase the scope and speed of knowledge dissemination, forming a closed-loop control system. Then, we develop a two-layer optimal control framework. At the upper level, the optimal solution of the control system is solved and sent to the lower layer. At the lower level, a model predictive controller (MPC) receives input information from the upper level and is formulated to decide on the network and then transmits it to its heterogeneous networks which can reduce control resources and computation complexity. Finally, numerical simulations are conducted to confirm the theoretical results.

Density estimation based soft actor-critic: deep reinforcement learning for static output feedback control with measurement noise

ABSTRACT The state-of-the-art deep reinforcement learning (DRL) methods, including Deep Deterministic Policy Gradient (DDPG), Twin Delayed DDPG (TD3), Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), among others, demonstrate significant capability in solving the optimal static state feedback control (SSFC) problem. This problem can be modeled as a fully observed Markov decision process (MDP). However, the optimal static output feedback control (SOFC) problem with measurement noise is a typical partially observable MDP (POMDP), which is difficult to solve, especially for the continuous state-action-observation space with high dimensions. This paper proposes a two-stage framework to address this challenge. In the laboratory stage, both the states and the noisy outputs are observable; the SOFC policy is converted to a constrained stochastic SSFC policy, of which the probability density function is generally not analytical. To this end, a density estimation based SAC algorithm is proposed to explore the optimal SOFC policy by learning the optimal constrained stochastic SSFC. Consequently, in the real-world stage, only the noisy outputs and the learned SOFC policy are required to solve the optimal SOFC problem. Numerical simulations and the corresponding experiments with robotic arms are provided to illustrate the effectiveness of our method. The code is available at https://github.com/RanKyoto/DE-SAC.

A Multifilters Approach to Adaptive Event-Triggered Control of Uncertain Nonlinear Systems With Global Output Constraint.

This article focuses on the problem of adaptive event-triggered output feedback control for a class of uncertain nonlinear systems under the output constraint. Different from the existing works, the time-varying parameters and the global output constraint are taken into account. First, by means of multifilters, the unmeasurable state variables are reconstructed, under which the unknown time-varying parameters and sensor sensitivity are transformed into the estimation problem of unknown parameters. Second, based on a barrier function, a novel constraint algorithm is established to make the output enter into asymmetric time-varying constraint boundaries, which is independent of the initial value of the output. To avoid continuous sampling of the controller, an event-triggered mechanism is proposed without the Zeno phenomenon. By means of the Lyapunov stability theory, it is strictly proved that the output enters into the pregiven asymmetric constraint boundaries, and never exceeds. Finally, the validity of our proposed control algorithm is illustrated by a numerical simulation.

On the boundedness of solutions of some fuzzy dynamical control systems

<abstract> <p>The asymptotic behavior of solutions of fuzzy control systems is a component of the study of fuzzy control theory. The study of stability for T-S (Takagi-Sugeno) fuzzy systems, which process qualitative data through linguistic expressions, is the subject of this paper. Asymptotic stability is conservative in many real-world applications due to measurement noise and other disruptions. The ultimate limit, which indicates that the mistakes stay in a specific area close to the origin after a long enough amount of time, is a crucial characteristic that is frequently defined for such systems. We are interested with the problem of the state feedback controller for T-S fuzzy models with uncertainties where the global exponential ultimate boundedness of solutions is studied for certain fuzzy control systems. We use common quadratic Lyapunov function and parallel distributed compensation controller techniques to study the asymptotic behavior of the solutions of fuzzy control system in presence of perturbations. An example demonstrating the validity of the main result is discussed.</p> </abstract>

Feedback control problems for a class of backward Riemann-Liouville fractional evolution hemivariational inequalities with dual operators

Feedback control problems for a class of backward Riemann-Liouville fractional evolution hemivariational inequalities with dual operators