Near insensitivity of linear feedback systems

In this paper, the notion of near insensitivity with respect to disturbance and parameter variations is introduced as a performance measure for linear feedback systems and conditions for near insensitivity are derived. It is shown that near insensitivity is attainable with high gain feedback provided that certain geometric conditions relating the system matrices and the parameter variation matrices are satisfied.     

Subspace predictive control with the data-driven event-triggered law for linear time-invariant systems

In this paper, a subspace predictive control (SPC) method with a novel data-driven event-triggered law is proposed for linear time-invariant systems with unknown model parameters. Based on the conventional SPC method, the event-triggered law is introduced to substitute the typical receding horizon optimization, which reduces the data computation load of the traditional SPC method. The key parameters of the event-triggered law are derived by the Q-learning method via system data and the input-to-state stability of the system can be ensured with the designed event-triggered law. The simulation results illustrate the effect and merits of the proposed method with comparisons.     

Reset output feedback control of cluster linear multi-agent systems

In this study, the distributed output consensus control issue is investigated for a class of linear cluster multi-agent systems (CMASs) under the control strategy of the reset observer. We consider a communication network consisting of several clusters, each of which is directed and contains a leader. The interactions among agents include continuous-discrete hybrid communication. Specifically, an instantaneous connectivity only exists between the clusters at discrete moments, called the reset time sequence. At the reset time, an instantaneous fixed directed network is formed such that only the leaders will consider the available information of neighboring leaders to reset their own states. During non-reset intervals, only the intra-clusters are connected while the inter-clusters are equivalent to a disconnected network topology. Considering that in practice, the state information may be partially unavailable, only the relative output information is utilized to estimate the unavailable state and thus control protocols are developed with the help of the reset full-order and reduced-order observers, respectively. The stability of the closed-loop CMAS at both the reset time and non-reset intervals is studied based on Lyapunov analysis. The consensus value depends only on the initial conditions and the network topology involved, and not on the reset time sequence. Finally, numerical simulations are provided to illustrate the theoretical results.     

State feedback uniform decoupling problem of linear time-varying analytic systems

A new approach to the input-output uniform decoupling problem of linear time-varying analytic systems via proportional state feedback is presented. A major feature of the proposed approach is that it reduces the solution of the uniform decoupling problem to that of solving a linear algebraic system of equations. This system of equations greatly facilitates the solution of the three major aspects of the decoupling problem: the necessary and sufficient conditions, the general analytical expressions for the controller matrices, and the structure of the uniformly decoupled closed-loop system.     

Output feedback stabilization of switching discrete-time linear systems with parameter uncertainties

This paper deals with observer-based control design for a class of switched discrete-time linear systems with parameter uncertainties. The main contribution of the paper is to propose a convenient way based on Finsler’s lemma to enhance the synthesis conditions, expressed in terms of Linear Matrix Inequalities (LMIs). Indeed, this judicious use of Finsler’s lemma provides additional decision variables, which render the LMIs less conservative and more general than all those existing in the literature for the same class of systems. Two numerical examples followed by a Monte Carlo evaluation are proposed to show the superiority of the proposed design technique.     

Regional eigenvalue-clustering robustness of linear uncertain multivariable output feedback PID control systems

This paper proposes a time domain approach to deal with the regional eigenvalue-clustering robustness analysis problem of linear uncertain multivariable output feedback proportional-integral-derivative (PID) control systems. The robust regional eigenvalue-clustering analysis problem of linear uncertain multivariable output feedback PID control systems is converted to the regional eigenvalue-clustering robustness analysis problem of linear uncertain singular systems with static output feedback controller. Based on some essential properties of matrix measures, a new sufficient condition is proposed for ensuring that the closed-loop singular system with both structured and mixed quadratically-coupled parameter uncertainties is regular and impulse-free, and has all its finite eigenvalues retained inside the same specified region as the nominal closed-loop singular system does. Two numerical examples are given to illustrate the application of the presented sufficient condition.     

Fault-tolerant control for a class of linear interconnected hyperbolic systems by boundary feedback

This paper considers a fault-tolerant control problem for a class of interconnected linear hyperbolic partial differential equation systems. Both subsystem faults and coupling faults are considered. Firstly, the well-posedness of the faulty system is analyzed by using semigroup theory. Secondly, for the fault-free case, a stabilizing boundary feedback control based on small-gain theorem is proposed. Consequently, in the presence of faults, fault recoverability conditions are established that maintain the stability of the faulty systems. The fault-tolerant control strategies are also provided. A heat exchanger example is taken to illustrate the effectiveness and practicality of the proposed methods.     

Regulation of linear systems with both pointwise and distributed input delays by memoryless feedback

This paper is concerned with the stabilization of linear systems with both pointwise and distributed input delays, which can be arbitrarily large yet exactly known. The state vector used in the well-known Artstein transformation is firstly linked with the future state of the system. Pseudo-predictor feedback (PPF) approaches are then established to design memory stabilizing controllers. Necessary and sufficient conditions guaranteeing the stability of the closed-loop system are established in terms of the stability of some integral delay systems. Furthermore, since the PPF still is infinite-dimensional state feedback law and may cause difficulties in their practical implementation, truncated pseudo-predictor feedback (TPPF) approaches are established to design finite dimensional (memoryless) controllers. It is shown that the pointwise and distributed input delays can be compensated properly by the TPPF as long as the open-loop system is polynomially unstable. Finally, two numerical examples, one of which is the spacecraft rendezvous control system, are carried out to support the obtained theoretical results.     

Global exact tracking for uncertain MIMO linear systems by output feedback sliding mode control

This paper presents a solution to the problem of global exact output tracking for uncertain MIMO (multiple-input–multiple-output) linear plants with non-uniform arbitrary relative degree using output feedback sliding mode control. The key idea to overcome the relative degree obstacle is to generalize our previous hybrid estimation scheme to a multivariable version by combining, through switching, a standard linear lead filter with a non-linear one based on robust exact differentiators, achieving uniform global exponential practical stability and exact tracking.     

Solution of linear state-space equations and parameter estimation in feedback systems using laguerre polynomial expansion

This paper analyzes the application of Laguerre polynomial expansion to linear systems. It can be applied to the solution of linear state equations by using an algebraic matrix to determine the coefficients of the Laguerre expansion. It also can be applied to system identification by using the expansion to determine the coefficients in the transfer function. Examples are given to demonstrate the accuracy of finite order expansion by Laguerre polynomials.     

Iterative learning feedback control for linear parabolic distributed parameter systems with multiple collocated piecewise observation

This paper presents a novel iterative learning feedback control method for linear parabolic distributed parameter systems with multiple collocated piecewise observation. Multiple actuators and sensors distributed at the same position of the spatial domain are utilized to perform collocated piecewise control and measurement operations. The advantage of the proposed method is that it combines the iterative learning algorithm and feedback technique. Not only can it use the iterative learning algorithm to track the desired output trajectory, but also the feedback control approach can be utilized to achieve real-time online update. By utilizing integration by parts, triangle inequality, mean value theorem for integrals and Gronwall lemma, two sufficient conditions based on the inequality constraints for the convergence analysis of the tracking error system are presented. Some simulation experiments are provided to prove the effectiveness of the proposed method.     

Quantized H∞ output feedback control for linear discrete-time systems

This paper investigates the robust _H∞$H_{\infty }$ dynamic output feedback control problem for networked control systems (NCSs) with quantized measurements. The measurement losses of the communicated information are considered in an unreliable communication channel. The robust _H∞$H_{\infty }$ dynamic output feedback controllers are designed to handle the measurement losses and mitigate the quantization effects such that the resultant closed-loop NCS is mean-square stochastically stable with a prescribed _H∞$H_{\infty }$ disturbance attenuation performance. The controller existence conditions can be derived in terms of linear matrix inequalities (LMIs). Finally, an example is provided to illustrate the effectiveness of the proposed approach.     

Output feedback gain scheduled control of actuator saturated linear systems with applications to the spacecraft rendezvous

The problem of stabilization of a linear system that is asymptotic null controllable with bounded control is studied in this paper. By combining the parametric Lyapunov equation approach and the gain scheduling technique, a new observer-based output feedback gain scheduling controller is proposed to solve the semi-global stabilization problem for a linear system subject to actuator saturation. By scheduling the design parameters online the convergence rate of the state can be improved. Numerical simulations for a spacecraft rendezvous system show the effectiveness of the proposed approaches.     

On the vibrations of lumped parameter systems governed by differential-algebraic equations

In this paper, we consider the vibratory motions of lumped parameter systems wherein the components of the system cannot be described by constitutive expressions for the force in terms of appropriate kinematical quantities. Such physical systems reduce to a system of differential-algebraic equations, which invariably need to be solved numerically. To illustrate the issues with clarity, we consider a simple system in which the dashpot is assumed to contain a “Bingham” fluid for which one cannot describe the force in the dashpot as a function of the velocity. On the other hand, one can express the velocity as a function of the force.     

Block-balancing of linear systems

A generalization of balanced realizations called “block-balanced” realizations is introduced. A sufficient condition to obtain a block-balanced realization is given and computational advantages over the balanced realizations are pointed out. It is shown that all the properties of balanced realizations are preserved in block-balanced realizations. Finally, a new norm condition to obtain a reduced model is provided.