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This paper deals with the problem of disturbance rejection for uncertain LTI SISO systems perturbed by an unmeasurable external disturbance under the framework of output regulation. The system is assumed to be minimum phase and internally stable, but the model parameters are completely unknown. In addition, no knowledge of the external disturbance, including frequency, amplitude and phase is required to be known in advance. A novel high-order sliding mode-based Unknown Input Observer(UIO) is developed to stabilize the system and reconstruct the external disturbance. The main feature distinguishing the proposed method from the existing ones is that we do not need to integrate a frequency estimator into the adaptive controller or update the frequency estimation in a hybrid manner. Instead, the disturbance is directly duplicated by the aforementioned unknown input observer. The boundedness of states and asymptotic convergence properties are rigorously proved. Finally, the effectiveness of the proposed technique is illustrated by a numerical example.

The problem considered in this paper is dual to the control problem for over-actuated systems found in the literature. We show that, due to a certain notion of weak output redundancy, there always exists an unobservability subspace containing the input subspace, which ensures that the original system can be partitioned into two subsystems, one of which is not affected by actuator faults. We use this fact to estimate the disturbance and the fault in a cascaded fashion: we first design a discrete-time filter on a properly designed residual signal, that can reconstruct the disturbance. The estimated disturbance can then be used to perform fault detection and estimation in a cascaded fashion. A numerical example verifies the efficacy of the proposed strategy.

We propose a state estimation methodology using a network of distributed observers. We consider a scenario in which the local measurement at each node may not guarantee the system's observability. In contrast, the ensemble of all the measurements does ensure that the observability property holds. As a result, we design a network of observers such that the estimated state vector computed by each observer converges to the system's state vector by using the local measurement and the communicated estimates of a subset of observers in its neighborhood. The proposed estimation scheme exploits sensor redundancy to provide robustness against faults in the sensors. Under suitable conditions on the redundant sensors, we show that it is possible to mitigate the effects of a class of sensor faults on the state estimation. Simulation trials demonstrate the effectiveness of the proposed distributed estimation scheme.

Disturbance rejection for uncertain systems is a longstanding problem of both theoretical and practical importance. In this paper, we propose a novel switching-based Adaptive Feedforward Controller (AFC) to remove a priori information on the sign of the plant transfer function at the frequency of interest (the so-called SPR-like condition), which is an undesirable but inevitable requirement for the existing AFC approaches. A distinctive feature of the work presented herein is improving the transient behavior by adopting a new switching mechanism based on an unnormalized adaptation law. Furthermore, the dimension of the overall controller is kept relatively low regardless of the number of candidate controllers. Boundedness of the trajectory of the closed-loop system and asymptotic zeroing of the output are rigorously proved. The effectiveness of the proposed technique is illustrated by numerical examples.

This paper presents a novel solution to the problem of designing an implementable (i.e., differentiator-free) model-reference output-feedback direct-adaptive controller for single-input-single-output linear time-invariant systems with relative degree possibly larger than one. The new paradigm is based on a version of the Dynamic Certainty Equivalence (DyCE) principle. The approach proposed in this work consists in realizing the DyCE control through surrogate parameter derivatives, made available by a Nonlinear Parameter Filter (NPF), instead of feeding the DyCE controller with the derivatives of the estimates produced High-Order Tuner (HOT). The proposed adaptive controller does not require error augmentation or normalization, allowing the use of large adaptation gains for fast convergence speed. Moreover, the proposed architecture can be easily equipped with well-known robust modifications of tuning laws. The performance of the proposed algorithm is demonstrated via comparative simulations with an error augmentation-based method and a simplified HOT algorithm.

In this work, we present a novel fixed-time state observer for LTI systems based on a time-varying coordinate transformation yielding the cancellation of the effect of the unknown initial conditions from the state estimates. This coordinate transformation allows one to map the state of the original system into that of an auxiliary system that evolves from initial conditions that are known by definition. After a stable observer is designed in the transformed coordinates, an estimate for the state of the original system can be obtained by inverting the above-mentioned map. The invertibility of the map is guaranteed for any time strictly greater than zero, so that the convergence time can be made arbitrarily small in nominal conditions. The robustness of the observer with respect to bounded measurement disturbances is characterized in terms of both transient and norm bounds on the asymptotic state-estimation error. Compared to existing finite- and fixed-time approaches, the proposed method does not require high-gain output-error injection, state augmentation, delay operators, or moving-windows. The dimensionality of the observer matches that of the observed system, and its realization takes the form of an LTV system.