Control Input Disturbance Research Articles

Neural Sliding Mode Tracking Control of Air-breathing Hypersonic Vehicles

This paper proposes a neural sliding mode control method for the tracking problem of the longitudinal dynamics of air-breathing hypersonic vehicles (ABHV). Considering the input/output feedback linearization, a high-order sliding mode law of the elevator deflection and the fuel equivalence ratio is designed. Moreover, the effect of uncertain model and control input disturbances is approximated with a Radial Basis Function Neural Network (RBFNN). The stability of the closed-loop system is analysed based on Lyapunov theorem. Simulation results shows the good tracking performance of the proposed controller and robustness with parameter uncertainties. All the signals are globally bounded and converged in short time.

Iterative Learning and Extremum Seeking for Repetitive Time-Varying Mappings

In this paper, we develop an extremum seeking control method integrated with iterative learning control to track a time-varying optimizer within finite time. The behavior of the extremum seeking system is analyzed via an approximating system - the modified Lie bracket system. The modified Lie bracket system is essentially an online integral-type iterative learning control law. The paper contributes to two fields, namely, iterative learning control and extremum seeking. First, an online integral type iterative learning control with a forgetting factor is proposed. Its convergence is analyzed via $k$-dependent (iteration- dependent) contraction mapping in a Banach space equipped with $\lambda$-norm. Second, the iterative learning extremum seeking system can be regarded as an iterative learning control with "control input disturbance." The tracking error of its modified Lie bracket system can be shown uniformly bounded in terms of iterations by selecting a sufficiently large frequency. Furthermore, it is shown that the tracking error will finally converge to a set, which is a $\lambda$-norm ball. Its center is the same with the limit solution of its corresponding "disturbance-free" system (the iterative learning control law); and its radius can be controlled by the frequency.

Hard Deadlines in Real-Time Control Systems

In a real-time control system where the control input is computed by a controller computer, the transient computer failures caused by an electromagnetic interference may seriously affect system stability. The faulty controller computer causes either a long delay in the feedback loop thus failing to update the control input for one or more sampling intervals, or control input disturbances by updating the control inputincorrectly until the fault is handled properly. If the period of this abnormal behavior exceeds a certain limit called a hard deadline, either the necessary conditions for system stability will be violated or the system willleave the allowed state space. In such a case a dynamic failure is said to occur in the system. We present a method for deriving hard deadlines for linear time-invariant control systems by examining the stability of the state difference equations resulting from the modification of the original state equations with an assumed maximum delay and several random sequences that represent the effects of stationary occurrences of the disturbances to, as well as the random delays of, the control input. Moreover, a one-shot event model, in which a single long-lasting fault causes a dynamic failure, is presented based on the state trajectory and the allowed state space.