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

Surviving disturbances: A predictive control framework with guaranteed safety

Rejecting all disturbances is an extravagant hope in safety-critical control systems, hence surviving them where possible is a sensible objective a controller can deliver. In order to build a theoretical framework starting from surviving all disturbances but taking the appropriate opportunity to reject them, a sufficient condition on surviving disturbances is first established by exploring the relation among steady sets of state, input, and disturbance, followed by an output reachability condition on rejecting disturbances. A new robust safety-critical model prediction control (MPC) framework is then developed by embedding the quartet of pseudo steady input, output, state, and disturbance (IOSD) into the optimisation. Unlike most existing tracking MPC setups, a new and unique formulation is adopted by taking the pseudo steady disturbance as an optimisation decision variable, rather than directly driven by the disturbance estimate. This new setup is able to decouple estimation error dynamics, significantly contributing to the guarantee of recursive feasibility, even if the disturbance or its estimate changes rapidly. Moreover, towards optimal coexistence with disturbances, offset-free tracking of a compromised reference can be achieved, if rejecting the disturbance conflicts with safety-critical specifications. Finally, the benefits of the proposed method have been demonstrated by both numerical simulations and experiments on aerial physical interaction

Disturbance/uncertainty estimation and attenuation techniques in PMSM drives–a survey

This paper gives a comprehensive overview on disturbance/uncertainty estimation and attenuation (DUEA) techniques in permanent magnet synchronous motor (PMSM) drives. Various disturbances and uncertainties in PMSM and also other alternating current (AC) motor drives are first reviewed which shows they have different behaviors and appear in different control loops of the system. The existing DUEA and other relevant control methods in handling disturbances and uncertainties widely used in PMSM drives, and their latest developments are then discussed and summarized. It also provides in-depth analysis of the relationship between these advanced control methods in the context of PMSM systems. When dealing with uncertainties,it is shown that DUEA has a different but complementary mechanism to widely used robust control and adaptive control. The similarities and differences in disturbance attenuation of DUEA and other promising methods such as internal model control and output regulation theory have been analyzed in detail. The wide applications of these methods in different AC motor drives (in particular in PMSM drives) are categorized and summarized. Finally the paper ends with the discussion on future directions in this area

Generalized Dynamic Predictive Control for Nonlinear Systems Subject to Mismatched Disturbances with Application to PMSM Drives

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper investigates a generalized dynamic predictive control (GDPC) strategy with a novel autonomous tuning mechanism of the horizon for a class of nonlinear systems subject to mismatched disturbances. As a new incremental function for the predictive control method, the horizon can be determined autonomously with respect to the system working conditions, instead of selecting a fixed value via experience before, which is able to effectively improve the control performance optimization ability to a certain extent considering different system perturbation levels. To this aim, firstly, a non-recursive composite control framework is constructed based on a series of disturbance observations via higher-order sliding modes. Secondly, by designing a simple one-step scaling gain update mechanism into the receding horizon optimization, the horizon can be therefore adaptively tuned according to its real-time practical operating conditions. A three-order numerical simulation and a typical engineering application of permanent magnet synchronous motor (PMSM) drive system are carried out to demonstrate the effectiveness and conciseness of the proposed GDPC method

On the actuator dynamics of dynamic control allocation for a small fixed-wing UAV with direct lift control

A novel dynamic control allocation method is proposed for a small fixed-wing unmanned aerial vehicle (UAV), whose flaps can be actuated as fast as other control surfaces, offering an extra way of changing the lift directly. The actuator dynamics of this kind of UAVs, which may be sluggish comparing to the UAV dynamics, should also be considered in the control design. To this end, a hierarchical control allocation architecture is developed. A disturbance observer based high-level tracking controller is first designed to accommodate the lagging effect ofthe actuators and to compensate the adverse effect of external disturbances. Then, a dynamic control allocator based on a receding-horizon performance index is developed, which forces the actuator state in the low-level to follow the optimised reference. Compared to the conventional control allocation method that assumes ideal actuators with infinite bandwidths, higher tracking accuracy of the UAV and better energy efficiency can be achieved by the proposed method. Stability analysis and high fidelity simulations both demonstrate the effectiveness of the proposed method, which can be deployed on different fixed-wing UAVs with flaps to achieve better performance.</div

Vision-Based UAV Landing with Guaranteed Reliability in Adverse Environment

open access articleSafe and accurate landing is crucial for Unmanned Aerial Vehicles (UAVs). However, it is a challenging task, especially when the altitude of the landing target is different from the ground and when the UAV is working in adverse environments, such as coasts where winds are usually strong and changing rapidly. UAVs controlled by traditional landing algorithms are unable to deal with sudden large disturbances, such as gusts, during the landing process. In this paper, a reliable vision-based landing strategy is proposed for UAV autonomous landing on a multi-level platform mounted on an Unmanned Ground Vehicle (UGV). With the proposed landing strategy, visual detection can be retrieved even with strong gusts and the UAV is able to achieve robust landing accuracy in a challenging platform with complex ground effects. The effectiveness of the landing algorithm is verified through real-world flight tests. Experimental results in farm fields demonstrate the proposed method’s accuracy and robustness to external disturbances (e.g., wind gusts)

Dual-layer optimization-based control allocation for a fixed-wing UAV

Many existing control allocation methods separate the high-level control design from their low-level allocation design, assuming that the constraints of actuators can be guaranteed by the allocator. This idea may not be suitable for the nonlinear fixed-wing unmanned aerial vehicle studied here, which hence motivates this work. In this paper, we propose a new dual-layer optimization-based control allocation method, in which the proposed allocator, on the one hand, can modify the pre-designed virtual signals from the high-level when the out-layer actuator, i.e., throttle, reaches its constraint. On the other hand, it reverts the conventional constrained allocator when the throttle constraints are inactive. Another feature is that under the proposed framework, the initial state of the augmented actuator dynamics serves as design parameters, bringing more degrees of freedom for allocation design without affecting the nominal stability. Apart from the control allocator, this paper also proposes a high-level flight controller based on the control-oriented model and a combination of nonlinear dynamic inversion and disturbance observer. Disturbance observer provides robustness by estimating the model errors between the control-oriented and true models, and compensating for them in the controller. High-fidelity simulation results under realistic wind disturbances are presented to demonstrate the performance of the proposed method. (C) 2021 Elsevier Masson SAS. All rights reserved