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A novel approximation-free differentiator-based output-feedback controller for uncertain large-scale systems (LSSs) is proposed. The considered LSS has nonautonomous and nonaffine-in-the-control subsystems which is yet to be tackled for decentralized output-feedback controller in the previous researches. The controller adopts a higher-order switching differentiator that can track the time-derivatives of a time-varying signal asymptotically. Through the differentiators, time-derivatives of output tracking errors are estimated and unstructured uncertainties in the controlled subsystems are compensated. The proposed decentralized output-feedback control formulae and the stability analysis are relatively simple in comparison to the previously proposed decentralized controllers. In this case, approximators such as fuzzy systems or neural networks are not required. The proposed controller guarantees that the tracking errors of the subsystems are asymptotically convergent to zeros and all the signals involved in the closed-loop systems are bounded.

A novel output-feedback controller for uncertain nonautonomous nonlinear systems with unknown relative-degree is proposed in this study. With the assumption that the upper bound of the relative degree is known, the proposed control scheme is designed based on the input filter and higher-order switching differentiator(HOSD) with over dimension. Using the overestimated time-derivatives of tracking error by HOSD, the proposed controller compensates for the effects of uncertainties including an unknown relative degree in the controlled system. Assuming that the internal zero dynamics, if they exist, are stable, the asymptotic stability of the closed-loop system is guaranteed.

A novel backstepping controller for uncertain single-input single-output pure-feedback nonaffine and nonautonomous nonlinear systems based on the time-derivative estimator (TDE) is proposed. Using TDEs, time-derivatives of error signals used in virtual control terms are directly estimated in every backstepping design steps. As a result, the control law has a relatively simple form. In addition, convergence of tracking error to a small neighborhood of origin is guaranteed regardless of unstructured uncertainties or unmatched disturbances in the controlled system. It does not require separate adaptive schemes or universal approximators such as neural networks or fuzzy logic systems adaptively tuned online to cope with system uncertainties. Simulation results demonstrated the simplicity and good performance of the proposed approximation-free controller.

In this brief, a new adaptive neurocontrol algorithm for a single-input-single-output (SISO) strict-feedback nonlinear system is proposed. Most of the previous adaptive neural control algorithms for strict-feedback nonlinear systems were based on the backstepping scheme, which makes the control law and stability analysis very complicated. The main contribution of the proposed method is that it demonstrates that the state-feedback control of the strict-feedback system can be viewed as the output-feedback control problem of the system in the normal form. As a result, the proposed control algorithm is considerably simpler than the previous ones based on backstepping. Depending heavily on the universal approximation property of the neural network (NN), only one NN is employed to approximate the lumped uncertain system nonlinearity. The Lyapunov stability of the NN weights and filtered tracking error is guaranteed in the semiglobal sense.

A direct adaptive state-feedback controller is proposed for highly nonlinear systems. We consider uncertain or ill-defined nonaffine nonlinear systems and employ a neural network (NN) with flexible structure, i.e., an online variation of the number of neurons. The NN approximates and adaptively cancels an unknown plant nonlinearity. A control law and adaptive laws for the weights in the hidden layer and output layer of the NN are established so that the whole closed-loop system is stable in the sense of Lyapunov. Moreover, the tracking error is guaranteed to be uniformly asymptotically stable (UAS) rather than uniformly ultimately bounded (UUB) with the aid of an additional robustifying control term. The proposed control algorithm is relatively simple and requires no restrictive conditions on the design constants for the stability. The efficiency of the proposed scheme is shown through the simulation of a simple nonaffine nonlinear system.