Search

The coloured noise is ubiquitous in industrial processes. This paper addresses the identification problem for the nonlinear feedback systems with coloured noise. Firstly, a direct identification scheme based on the least squares principle is developed to estimate the whole parameters of the nonlinear feedback systems and the convergence analysis is carried out through the stochastic stability theory. Secondly, for the purpose of improving the estimation accuracy, a filtering-based identification framework is proposed by constructing a linear filter for filtering the input data, output data and the coloured noise, and the coloured noise is transformed into a white noise. This identification scheme based on the filtering technique can effectively reduce the adverse effects caused by coloured noise and parameter estimation accuracy is enhanced compared with the direct least squares algorithm. Meanwhile, the convergence analysis of the filtering-based identification algorithm is given to provide a theoretical analysis. Finally, the simulation example is carried out by performance test and comparison analysis and simulation results show the effectiveness of the proposed identification methods. [ABSTRACT FROM AUTHOR]

This paper presents the key components in the Universal(U)-control framework for designing dynamic control systems from model-based to model-free paradigms, in which the pillars include U-control system configuration, open-loop dynamic inversion, closed-loop dynamic inversion, general model-free cancellation of nonlinear-dynamic-coupling effect, model-free-sliding-mode control (MFSMC) and model-free composite nonlinear feedback (CNF) control. This paper is devoted to delivering intuitive and easy-to-understand explanations for the involved approaches with a series of schematic diagrams. Readers can refer to the corresponding publications for more details on the theoretical analyses. In addition, some showcase examples are provided in this paper to facilitate the understanding of certain important concepts. [ABSTRACT FROM AUTHOR]

The modelling uncertainties, external disturbance and actuator saturation issues will degrade the performance and even the safety of flight. To improve control performance, this study proposes an adaptive U-model based double sliding control (UDSMC) algorithm combined with a radial basis function neural network (RBFNN) for a nonlinear two-degrees-of-freedom (2-DOF) helicopter system. Firstly, the adaptive RBFNN is designed to approximate the system dynamics with unknown uncertainties. Furthermore, two adaptive laws are designed to deal with unknown external disturbances and actuator saturation errors. The global stability of the proposed helicopter control system is rigorously guaranteed by the Lyapunov stability analysis, realizing precise attitude tracking control. Finally, the comparative experiments with conventional SMC and adaptive SMC algorithms conducted on the Quanser Aero2 platform demonstrate the effectiveness and feasibility of the proposed 2-DOF helicopter control algorithm. [ABSTRACT FROM AUTHOR]

This study presents a generalised framework for model-free robust decentralised control (MFRDC) of interconnected MIMO dynamic systems, with the goal of significantly reducing complexity in model-based design. A model-free robust sliding mode control, by Lyapunov differential inequality, is presented to achieve simultaneous nonlinear, dynamic, interaction/coupling inversion/cancellation (NDII) for such MIMO systems, which treats the plant/process as a total uncertainty from input to output. The U-control platform is presented, to integrate separately independently designed NDII and an invariant controller (IC) into a complete double loop control system. The associated robust stability and other properties are analysed to provide reference for applications. Two simulated tracking control tests are presented for functionally numerical demonstration, validation of the analytical results and illustration of the transparent procedure for general expansion/applications. These are: a coupled inverted pendulum and a two-input and two-output (TITO) non-affine nonlinear dynamic plant. [ABSTRACT FROM AUTHOR]