In this work, a distributed indirect adaptive controller is designed for a group of robotic agents cooperatively manipulating a common payload. Uncertainty on the model of the manipulated object and the limited actuation capabilities of the single agents can significantly impact the overall behavior of the control system. An indirect adaptive control scheme is proposed in this article to address these shortcomings. In particular, model uncertainty and loss of effectiveness of the actuators are handled in a unifying fashion by an adaptive control architecture that preserves physical consistency of the estimated inertial parameters of the manipulated object, while simultaneously providing an antiwindup mechanism for the estimated inertial parameters in case of actuator saturation. In addition, a dynamic input allocation strategy is proposed to distribute the control effort among the agents in such a way that the intrinsic input redundancy of the overall setup is exploited for dynamic optimization of additional performance criteria, including optimization of the control efforts on each agent. The stability of the closed-loop system is proven theoretically, and the performance and robustness of the control system are validated by means of comparative simulations with respect to a baseline state-of-the-art controller.

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This article addresses the output regulation problem for linear systems whose model is not known a priori but are internally stable or have achieved internal stability through feedback. The article provides a perspective that complements classical internal model-based methodologies. The paradigm we follow emphasizes adaptive feedforward control, signifying all those design methodologies that position a model of the signal to be tracked or rejected by a controlled system outside the primary feedback loop responsible for system stabilization.

The work considers the design of an indirect adaptive controller for a satellite equipped with a robotic arm manipulating an object. Uncertainty on the manipulated object can considerably impact the overall behavior of the system. In addition, the dynamics of the actuators of the base satellite are non-linear and can be affected by malfunctioning. Neglecting these two phenomena may lead to excessive control effort or degrade performance. An indirect adaptive control approach is pursued, which allows consideration of relevant features of the actuators dynamics, such as loss of effectiveness. Furthermore, an adaptive law that preserves the physical consistency of the inertial parameters of the various rigid bodies comprising the system is employed. The performance and robustness of the controller are first analyzed and then validated in simulation.

This article examines the properties of output-redundant systems, that is, systems possessing a larger number of outputs than inputs, through the lense of the geometric approach of Wonham et al. We begin by formulating a simple output allocation synthesis problem, which involves "concealing" input information from a malicious eavesdropper having access to the system output, while still allowing for a legitimate user to reconstruct it. It is shown that the solvability of this problem requires the availability of a redundant set of outputs. This very problem is instrumental to unveiling the fundamental geometric properties of output-redundant systems, which form the basis for our subsequent constructions and results. As a direct application, we demonstrate how output allocation can be employed to effectively protect the input information from certain output eavesdroppers with guaranteed results.

In this paper, we tackle the classical problem of estimating the parameters of an algebraic linear parameter model with the objective of solving the long-standing problem of guaranteeing boundedness of the output error independently from the growth of the regressors. Two solutions are presented. The first solution provides global results under the assumption that the time derivative of the regressor is available. The other solution disposes of the knowledge of the derivative of the regressor, and yields results that are valid in a semi-global sense, under the assumption that the regressor has a bounded growth. Simulation results provides an illustration of the proposed techniques in comparison with standard unnormalized and normalized gradient laws.

In the IEEE Transactions on Automatic Control, the IEEE Control Systems Society publishes high-quality papers on the theory, design, and applications of control engineering.Two types of contributions are regularly considered:1) Papers: Presentation of significant research, development, or application of control concepts.

In this paper, a novel joint unknown input observer (JUIO) is proposed for a class of descriptor systems. The unknown input (UI) to be estimated injects additively into both the state and output equations in a state space model. To the best of our knowledge, only a few contributions in existing work address this problem directly. To begin with, by introducing an auxiliary UI, the original system is transformed into a normal form in which the output is no longer affected by UI. In this way, the negative effect brought by the UI occurring in the output measurement is removed. An interval observer is developed to obtain upper and lower boundary estimates of the output of the reformulated system. After that, an algebraic relationship between the auxiliary UI and the states is established, and a UI reconstruction (UIR) method is developed. Based on the UIR, a JUIO comprising the UIR and a Luenberger-like state observer is developed to achieve asymptotic estimations of the UI and state simultaneously. Verifiable conditions for the existence of the proposed JUIO are given with respect to the original descriptor system. Finally, a simulation example is presented to verify the effectiveness of the proposed method.

In this work, a distributed indirect adaptive controller is designed for a group of robotic agents cooperatively manipulating a common payload. Uncertainty on the model of the manipulated object and limited actuation capabilities of the single agents can significantly impact the overall behavior of the control system. An indirect adaptive control scheme is proposed in this paper to address these shortcomings. In particular, model uncertainty and loss of effectiveness of the actuators are handled in a unifying fashion by an adaptive control architecture that preserves physical consistency of the estimated inertial parameters of the manipulated object, while simultaneously providing an anti-windup mechanism for the estimated inertial parameters against actuator saturation. The stability of the closed loop system is proven theoretically and the performance and robustness of the control system are validated by means of comparative simulations with respect to a baseline state-of-the-art controller.

This paper presents a predictor-based adaptive augmentation scheme to recover the designed behavior of a baseline linear controller in presence of parametric uncertainty. Remarkably, the proposed scheme achieves the recovery of the baseline closed-loop performance without the need for explicit knowledge of the baseline controller states and structure; rather, the adaptive mechanism relies solely on the output of the baseline controller and plant states. We showcase how the proposed adaptive design seamlessly integrates into inner-outer loop control architectures, enhancing the overall performance and robustness while simultaneously reducing the control law complexity compared to available solutions.

The distributed state estimation problem on linear time-invariant systems is addressed in this paper. We consider a scenario where the system outputs are measured via a group of sensors distributed over multiple nodes, where the joint measurements guarantee the detectability of the system. Each node is equipped with an observer exchanging its own state estimates with the neighboring nodes over a communication network, and the objective is to estimate the entire state of the system by each observer. Compared to existing designs requiring information on global graph topology or sensor placement at other nodes, our design is based only on local information. Hence, the proposed scheme is plug-and-play and scalable so that adding and removing nodes to the network or changing sensors at some existing nodes does not require observer redesign at any other node. Simulation results demonstrate the effectiveness of the proposed distributed estimation scheme.

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SYSTEMS TECHNOLOGY, the IEEE Control Systems Society publishes high-quality papers on technological advances in the design, realization, and operation of control systems.Submissions should emphasize novel contributions to the solution of control engineering problems arising in specific application areas.The official scope can be found at the following link: http://ieeexplore.ieee.org/xpl/aboutJournal.jsp?punumber=87#AimsScope.This TRANSACTIONS is published bimonthly.Three types of contributions are regularly considered:1) Papers-Presentation of significant research, development, or application of control systems technology.2) Brief Papers-Concise descriptions of a contribution to a specific aspect of design, realization, or operation of control systems technology.3) Letters-Significant remarks of interest to control systems engineers, and comments on previously published papers.In addition, special papers (tutorials, surveys, and perspectives on the current trends in control systems technology) are solicited.

In the IEEE Transactions on Automatic Control, the IEEE Control Systems Society publishes high-quality papers on the theory, design, and applications of control engineering.Two types of contributions are regularly considered:1) Papers: Presentation of significant research, development, or application of control concepts.

Aircraft noise is a key environmental concern for communities in the proximity of airports. While the engine remains the major source of noise in an aircraft, at low altitude and low speeds a significant role is played by aerodynamic noise generated by airflow over the fuselage. During landing, when the engines operate at reduced power, a prominent source of aerodynamic noise is located at the landing gear bay, due to the occurrence of self-sustained oscillations in a resonant cavity. Suppressions of these oscillations may result in a significant reduction of overall aircraft noise during landing. In this talk, we summarize the research activity of the Gas Dynamics and Turbulence Lab at The Ohio State University that has been devoted to the design and experimental evaluation of model-based feedback controllers for suppressing subsonic cavity resonance. Proper orthogonal decomposition and Galerkin projection techniques were used to obtain a reduced-order model of the flow dynamics from experimental data. The model was made amenable to control design by means of an optimization-based control separation technique, which makes the control input appear explicitly in the equations. An adaptive feedback controller was then employed to suppress the cavity tones from pressure measurements. Experimental results, in qualitative agreement with the theoretical analysis, showed that the controller achieves a significant attenuation of the resonant tone with a redistribution of the energy into other frequencies. The benefits of parameter adaptation over controllers of fixed structure under varying or uncertain flow conditions were also demonstrated experimentally. Finally, an outlook into application of closed-loop strategies for jet noise mitigation is offered.

In this work, we propose a direct-adaptive MRAC for relative-degree-unity SISO systems with unknown control direction. The proposed scheme, employing an original construction of the control law and the use of an adaptive observer, achieves the long-searched objective of injecting, through the input, the unmeasurable derivative of the output error. The output derivative injection is performed by a smart construction of the control input that features a Parameter-dependent Input Normalization (PIN). The PIN scheme does not make use of Nussbaum functions usually invoked in the direct-adaptive setting, does not require persistence of excitation of indirect adaptive schemes, does not require switching between multiple models, does not suffer from singularities and does not require to know a-priori bounds on the norm of the high-frequency gain and on the parameters. Effectiveness of the algorithm is illustrated by a numerical example.

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.

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.