Experimental comparison of parameter estimation methods in adaptive robot control

In the literature on adaptive robot control a large variety of parameter estimation methods have been proposed, ranging from tracking-error-driven gradient methods to combined tracking- and prediction-error-driven least-squares type adaptation methods. This paper presents experimental data from a comparative study between these adaptation methods, performed on a two-degrees-of-freedom robot manipulator. Our results show that the prediction error concept is sensitive to unavoidable model uncertainties. We also demonstrate empirically the fast convergence properties of least-squares adaptation relative to gradient approaches. However, in view of the noise sensitivity of the least-squares method, the marginal performance benefits, and the computational burden, we (cautiously) conclude that the tracking-error driven gradient method is preferred for parameter adaptation in robotic applications

Composite Disturbance Filtering: A Novel State Estimation Scheme for Systems With Multi-Source, Heterogeneous, and Isomeric Disturbances

State estimation has long been a fundamental problem in signal processing and control areas. The main challenge is to design filters with ability to reject or attenuate various disturbances. With the arrival of big data era, the disturbances of complicated systems are physically multi-source, mathematically heterogenous, affecting the system dynamics via isomeric (additive, multiplicative and recessive) channels, and deeply coupled with each other. In traditional filtering schemes, the multi-source heterogenous disturbances are usually simplified as a lumped one so that the "single" disturbance can be either rejected or attenuated. Since the pioneering work in 2012, a novel state estimation methodology called {\it composite disturbance filtering} (CDF) has been proposed, which deals with the multi-source, heterogenous, and isomeric disturbances based on their specific characteristics. With the CDF, enhanced anti-disturbance capability can be achieved via refined quantification, effective separation, and simultaneous rejection and attenuation of the disturbances. In this paper, an overview of the CDF scheme is provided, which includes the basic principle, general design procedure, application scenarios (e.g. alignment, localization and navigation), and future research directions. In summary, it is expected that the CDF offers an effective tool for state estimation, especially in the presence of multi-source heterogeneous disturbances

Output-based disturbance rejection control for non-linear uncertain systems with unknown frequency disturbances using an observer backstepping approach

This study is concerned with the output feedback control design for a class of non-linear uncertain systems subject to multiple sources of disturbances including model uncertainties, unknown constant disturbances, harmonic disturbances with unknown frequency and amplitude. The total disturbances and uncertainties are delicately represented by a compact exogenous model first. By incorporating the adaptive internal model principle, a set of dynamic estimators are developed for both state and disturbance observations. By means of observer backstepping technique, a composite output feedback controller is constructed based on the disturbance and state estimations. The stability of the closedloop system is rigorously established based on Lyapunov stability criterion. A missile roll stabilisation example is finally investigated to validate the effectiveness of the proposed control approach

Disturbance rejection for nonlinear uncertain systems with output measurement errors: Application to a helicopter model

As a virtual sensor, disturbance observer provides an alternative approach to reconstruct lumped disturbances (including external disturbances and system uncertainties) based upon system states/outputs measured by physical sensors. Not surprisingly, measurement errors bring adverse effects on the control performance and even the stability of the closed-loop system. Toward this end, this paper investigates the problem of disturbance observer based control for a class of disturbed uncertain nonlinear systems in the presence of unknown output measurement errors. Instead of inheriting from the estimation-error-driven structure of Luenberger type observer, the proposed disturbance observer only explicitly uses the control input. It has been proved that the proposed method endows the closed-loop system with strong robustness against output measurement errors and system uncertainties. With rigorous analysis under the semiglobal stability criterion, the guideline of gain choice based upon the proposed structure is provided. To better demonstrate feature and validity of the proposed method, numerical simulation and comparative experiments of a helicopter model are implemented

MIMO nonlinear PID predictive controller

A class of nonlinear generalised predictive controllers (NGPC) is derived for multi-input multi-output (MIMO) nonlinear systems with offset or steady-state response error. The MIMO composite controller consists of an optimal NGPC and a nonlinear disturbance observer. The design of the nonlinear disturbance observer to estimate the offset is particularly simple, as is the associated proof of overall nonlinear closed-loop system stability. Moreover, the transient error response of the disturbance observer can be arbitrarily specified by simple design parameters. Very satisfactory performance of the proposed MIMO nonlinear predictive controller is demonstrated for a three-link nonlinear robotic manipulator example

Generalized extended state observer based control for systems with mismatched uncertainties

The standard extended state observer based control (ESOBC) method is only applicable for a class of single-input-single-output essential-integral-chain systems with matched uncertainties. It is noticed that systems with nonintegral-chain form and mismatched uncertainties are more general and widely exist in practical engineering systems, where the standard ESOBC method is no longer available. To this end, it is imperative to explore new ESOBC approach for these systems to extend its applicability. By appropriately choosing a disturbance compensation gain, a generalized ESOBC (GESOBC) method is proposed for nonintegral-chain systems subject to mismatched uncertainties without any coordinate transformations. The proposed method is able to extend to multi-input-multi-output systems with almost no modification. Both numerical and application design examples demonstrate the feasibility and efficacy of the proposed method

Constrained anti-disturbance control for a quadrotor based on differential flatness

The classical control design based on linearised model is widely used in practice even to those inherently nonlinear systems. Although linear design techniques are relatively mature and enjoy the simple structure in implementations, they can be prone to misbehaviour and failure when the system state is far away from the operating point. To avoid the drawbacks and exploit the advantages of linear design methods while tackling the system nonlinearity, a hybrid control structure is developed in this paper. First, the model predictive control is used to impose states and inputs constraints on the linearised model, which makes the linearisation satisfy the small-perturbation requirement and reduces the bound of linearisation error. On the other hand, a combination of disturbance observer based control and H1 control, called composite hierarchical anti-disturbance control, is constructed for the linear model to provide robustness against multiple disturbances. The constrained reference states and inputs generated by the outer-loop model predictive controller are asymptotically tracked by the inner-loop composite anti-disturbance controller. To demonstrate the performance of the proposed framework, a case study on quadrotor is conducted