Low Gain Feedback Laws Research Articles

An exploration of the Razumikhin stability theorem with applications in stabilization of delay systems

In this paper, we establish a new evolution property of Lyapunov functions that satisfy the Razumikhin Stability Theorem conditions. The derived property is shown to be a valid alternative criterion to prove the asymptotic stability of a delay system without calculating the derivative of the Lyapunov function, and it is especially useful in solving stabilization problems for time delay systems when the delay is unknown. As an example application of this derived property, we investigate the stabilization of continuous-time linear systems with an unknown time-varying delay, by proposing a switched low gain feedback control technique that utilizes the derived property. Existing literature on stabilization of continuous time delay systems all require some knowledge, such as an upper bound, of the delay. In this work, by proposing a switched low gain feedback control law utilizing the derived property, we are able to achieve asymptotic stabilization of the system without any knowledge of the delay. Furthermore, the proposed switched low gain feedback law also significantly improves the closed-loop system performance in terms of overshoot and convergence speed, in comparison with the traditional fixed gain low gain feedback design. Simulation study verifies the theoretical results.

An off‐policy approach for model‐free stabilization of linear systems subject to input energy constraint and its application to spacecraft rendezvous

SummaryThis note is concerned with the problem of stabilizing a class of linear continuous‐time systems with completely unknown system dynamics subject to input energy constraint. To deal with this problem, a model‐based low gain feedback law is designed firstly by establishing a special algebraic Riccati equation. Such a low gain feedback law can semiglobally stabilize the linear systems subject to input energy constraint with the exact system model. In order to relax the assumption that the system model is exactly known, an off‐policy reinforcement learning approach is designed to solve the same problem without requiring the completely knowledge of the system dynamics. Finally, in order to verify the effectiveness of the proposed model‐free approach, simulation result on the spacecraft rendezvous problem is introduced.

Stabilization of Discrete-Time Linear Systems With an Unknown Time-Varying Delay by Switched Low-Gain Feedback

In this note, we investigate the stabilization of discrete-time linear systems with an unknown time-varying delay. Existing literature on the stabilization of time-delay systems require some knowledge, such as an upper bound, of the delay. In this paper, by proposing a switched low-gain feedback control law, we are able to achieve asymptotic stabilization of the system without any knowledge of the delay. In addition, the proposed switched low-gain feedback law also significantly improves the closed-loop system performance in terms of overshoot and convergence speed in comparison with the traditional fixed gain design. Simulation study illustrates the effectiveness of our theoretical results.

Time-varying low gain feedback for linear systems with unknown input delay

In this paper, the traditional low gain feedback design with a time-invariant feedback parameter is generalized to time-varying parameter design for linear systems with delayed input. For an unknown delay with a known upper bound, a time-varying low gain feedback law, constructed by using the parametric Lyapunov equation based approach, globally regulates a system with all open loop poles at the origin as long as the time-varying low gain parameter has a continuous second derivative and approaches a sufficiently small constant with its derivative approaching zero as time goes to infinity. Improvement of the closed-loop performance is addressed in a convergence rate analysis and then observed in simulation compared with the traditional constant parameter low gain design.

A Lyapunov Inequality Characterization of and a Riccati Inequality Approach to $L_{\infty}$ and $L_{2}$ Low Gain Feedback

This paper is concerned with a Lyapunov inequality characterization of the eigenstructure assignment-based low gain feedback laws. With this characterization and our earlier characterizations of other low gain feedback design approaches, all existing low gain feedback designs are unified under this Lyapunov inequality framework, which in turn implies that all of these low gain feedback laws are both $L_{\infty}$ and $L_{2}$ low gain feedback. This Lyapunov inequality characterization also leads to a quadratic Lyapunov function for the closed-loop system, which is expected to play an important role in solving other control problems. This characterization also motivates a new Riccati inequality-based low gain feedback design, which not only possesses the appealing features of the existing low gain designs but also is computationally easy to carry out.

Stabilization of a Class of Linear Systems With Input Delay and the Zero Distribution of Their Characteristic Equations

This paper is concerned with stabilization of linear systems with arbitrarily large but bounded time-varying delay in the input. A pole assignment based low gain feedback design is adopted to solve the problem. Both delay-dependent and delay-independent results are presented and a series of sufficient conditions for guaranteeing the stability of the closed-loop system are established. By using properties of this class of low gain feedback laws and the properties of certain transcendental equations, distribution of the zeros of the closed-loop characteristic equation is described. As a result, a necessary and sufficient condition is identified that guarantees the stability of the closed-loop system. A numerical example is given to illustrate the effectiveness of the proposed approach.

A parametric Lyapunov equation approach to low gain feedback design for discrete-time systems

Low gain feedback, a parameterized family of stabilizing state feedback gains whose magnitudes approach zero as the parameter decreases to zero, has found several applications in constrained control systems, robust control and nonlinear control. In the continuous-time setting, there are currently three ways of constructing low gain feedback laws: the eigenstructure assignment approach, the parametric ARE based approach and the parametric Lyapunov equation based approach. The eigenstructure assignment approach leads to feedback gains explicitly parameterized in the low gain parameter. The parametric ARE based approach results in a Lyapunov function along with the feedback gain, but requires the solution of an ARE for each value of the parameter. The parametric Lyapunov equation based approach possesses the advantages of the first two approaches and results in both an explicitly parameterized feedback gains and a Lyapunov function. The first two approaches have been extended to discrete-time setting. This paper develops the parametric Lyapunov equation based approach to low gain feedback design for discrete-time systems.