Control Scheme For Discrete-time Systems Research Articles

Optimal Learning Control Scheme for Discrete-Time Systems With Nonuniform Trials.

In reality such as a rehabilitation training, a repetitive control is necessary but the operational lengths may be iteration varying due to health condition. For the issue, this article investigates an intermittent optimal learning control scheme that considers the partially available information for the learning processing. The performance index is to minimize the summation of the quadratic timewise tracking error and the amplified adjacent-iteration timewise inputs drift while the argument is assigned as the iteration-time-varying learning gain. By adopting the latest captured historical timewise input and the tracking error, the optimal learning gain is achieved. Theoretical analysis conveys that the timewise tracking error is asymptotically convergent along the iteration direction. In particular, the tracking error may vanish at some finite iteration if the amplifier is null. Numerical simulations for a permanent magnet linear motor model testify the validity and effectiveness of the proposed scheme.

Data-Driven Fault-Tolerant Control for Discrete-Time Systems based on LMI

Actuator failure of control systems is one of the main factors causing instability and performance degradation. Over the past few decades, fault-tolerant control strategies have been introduced to improve the performance of control systems against failure. The current fault-tolerant control approaches are mostly model-based, however, model identification is time-costly and costly. In this paper, a data-driven fault-tolerant control method for discrete-time systems with actuator faults is designed. Using the input and output data collected directly from the system, the data-driven method develops the fault-tolerant control strategy for discrete-time systems with unknown or complex model. Moreover, the stability condition of the strategy is derived based on linear matrix inequality (LMI). Finally, a simulation is presented to demonstrate the effectiveness of the data-driven fault-tolerant control strategy, using an aircraft dynamic system model with actuator failure.

Convergence properties of two networked iterative learning control schemes for discrete-time systems with random packet dropout

ABSTRACTThis paper addresses convergence issue of two networked iterative learning control (NILC) schemes for a class of discrete-time nonlinear systems with random packet dropout occurred in input and output channels and modelled as 0–1 Bernoulli-type random variable. In the two NILC schemes, the dropped control input of the current iteration is substituted by the synchronous input used at the previous iteration, whilst for the dropped system output, the first replacement strategy is to replace it by the synchronous pre-given desired trajectory and the second one is to substitute it by the synchronous output used at the previous iteration. By the stochastic analysis technique, we analyse the convergence properties of two NILC schemes. It is shown that under appropriate constraints on learning gain and packet dropout probabilities, the tracking errors driven by the two schemes are convergent to zero in the expectation sense along iteration direction, respectively. Finally, illustrative simulations are carried out to manifest the validity and effectiveness of the results.

A new adaptive scheme for a class of discrete-time nonlinear systems

Abstract A new adaptive control scheme for discrete-time systems is proposed. The objective is the tracking of the trajectory. Global boundedness convergence and boundedness are obtained for a certain subclass of nonlinear systems.

Robust implicit self-tuning regulator: Convergence and stability

A family of implicit self-tuning regulators (STR) is presented, based on Lyapunov analysis techniques for the control of a class of multi-input and multi-output (MIMO) dynamical systems. Linearity in the parameters is assumed to hold, but the ‘estimation error’ is considered to be nonzero; this allows control of a larger class of systems and also has the effect of producing a robust controller. Moreover, the certainty equivalence (CE) principle is not used in the STR design, overcoming a major problem in adaptive control. The structure of the STR is naturally derived using a tracking error/passivity approach. The role of persistency of excitation (PE) is explored, and is used to show the boundedness of parameter estimates when gradient-based parameter tuning of STR is performed in nonideal conditions. New on-line tuning algorithms for implicit STR are derived that are similar to the ε-modification approach for the case of continuous-time systems and that include a modification to the adaptation gain and a correction term to the standard gradient-based tuning. These improved parameter tuning algorithms guarantee tracking as well as bounded parameter estimates in nonideal situations, so that PE is not needed. The notions of a passive STR, a dissipative STR and a robust STR are introduced. Finally, this paper provides a comprehensive theory in the development of identification, prediction and adaptive control schemes for discrete-time systems based on Lyapunov analysis.