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This paper presents studies on a robust adaptive output feedback stabilisation for a class of nonlinear systems, which is subjected to parametric uncertainties and disturbances. The disturbances are considered to be general, i.e. both the matched and unmatched ones are present. A novel robust adaptive control is developed for this problem. A technique is employed to avoid the difficulty in handling the nonlinear gain functions associated with the disturbances. For this, a new Lemma is obtained to handle the nonlinear functions that simplify the controller derivation steps. The unknown parameters and disturbances are tackled by deriving the tuning functions using a backstepping design. Finally, simulation is carried out to validate and show the effectiveness of the proposed robust controller. [ABSTRACT FROM AUTHOR]

Numerous approaches have been reported for different control problems of low-order triangular nonlinear systems. Nevertheless, it shows that the dynamic models of some practical plants are high-order nonlinear systems with odd rational powers. Besides, these systems constantly possess a non-triangular form and suffer from the impact of unmodeled dynamics. The related studies of such systems are very few and more challenging. This work concentrates on adaptive control issue of high-order nonlinear system with odd rational powers, unmodeled dynamics, and non-triangular structure. Based on the small-gain theorem, and by employing the adaptive technique, adding a power integrator method, and neural network method, we successfully construct a new adaptive controller, which greatly decreases the use of parameter estimations in the adaptive control of the considered system. An example highlights that the strategy regulates the studied systems well and performs satisfied system responses. • The robust adaptive control problem of uncertain nonlinear system is considered. • The system has odd powers, unmodeled dynamics, and non-triangular structure. • The work uses adaptive adding a power integrator and neural network method. [ABSTRACT FROM AUTHOR]

Decentralized Model Reference Adaptive Control problems are investigated for a class of linear time-invariant two time-scale model, having fast and slow dynamics and unmatched interconnections. Design of full state feedback controller is a critical task to the system having interrelated dynamics and nonlinear interconnections of time varying lags, however, can be addressed by singular perturbation procedures and time scale modeling. In this research, full order observer-based state feedback control is designed to two-time scale system to ensure the stability of the closed loop system. Then, decentralized model reference adaptive controller with the novel reference model is designed to individual subsystems. It is found that the proposed design enforces the system states to track the reference state asymptotically. To investigate the proposed design, an example of drying process is considered. Simulation results are analyzed to confirm the efficacy of the proposed control scheme. [ABSTRACT FROM AUTHOR]

In this paper, we study the performance enhancement of tracking control for a class of nonlinear systems with prescribed performance, by employing a novel pair of prescribed monotone tube boundary functions. Compared to the standard prescribed performance approach, the key merit relies on the capability to assign some quantitative indices (e.g., overshoot, accuracy and convergence time) – which are widely required in industry – in terms of the novel links between these indices and the proposed boundary functions. We provide a constructive solution to achieve the pre-assigned performance, which is characterized by forward invariance and boundedness of the closed loop. [ABSTRACT FROM AUTHOR]

Dynamic regressor extension and mixing is a new technique for parameter estimation with guaranteed performance improvement – with respect to classical gradient or least-squares estimators – that has proven instrumental in the solution of several open problems in system identification and adaptive control. In this brief note we give two interpretations of this parameter estimator in terms of the recent extensions, to the cases of nonlinear systems and observation of linear functionals for time-varying systems, of the classical Luenberger’s state observers. [ABSTRACT FROM AUTHOR]