This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

Induction heating (IH) technology is nowadays the heating technology of choice in many industrial, domestic, and medical applications due to its advantages regarding efficiency, fast heating, safety, cleanness, and accurate control. Advances in key technologies, i.e., power electronics, control techniques, and magnetic component design, have allowed the development of highly reliable and cost-effective systems, making this technology readily available and ubiquitous. This paper reviews IH technology summarizing the main milestones in its development and analyzing the current state of art of IH systems in industrial, domestic, and medical applications, paying special attention to the key enabling technologies involved. Finally, an overview of future research trends and challenges is given, highlighting the promising future of IH technology.

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Induction Heating Technology and Its Applications: Past Developments, Current Technology, and Future Challenges

A systematic design methodology for state observers for a large class of nonlinear systems with bounded exogenous inputs (disturbance inputs and sensor noise) is proposed. The nonlinearities under consideration are characterized by an incremental quadratic constraint parameterized by a set of multiplier matrices. Linear matrix inequalities are developed to construct observer gains, which ensure that a performance output based on the state estimation error satisfies a prescribed degree of accuracy. Furthermore, conditions guaranteeing estimation of the unknown inputs to arbitrary degrees of accuracy are provided. The proposed scheme is illustrated with a numerical example, which does not satisfy the so-called “matching conditions.”

State and Unknown Input Observers for Nonlinear Systems With Bounded Exogenous Inputs

We propose the correlated orienteering problem (COP) as a novel nonlinear extension to the classic orienteering problem (OP). With the introduction of COP,  it becomes possible to model the planning of informative tours for the persistent monitoring of a spatiotemporal field with time-invariant spatial correlations using autonomous mobile robots, in which the robots are range- or time-constrained. Our focus in this paper is QCOP, a quadratic COP  instantiation that looks at correlations between neighboring nodes in a node network. The main feature of QCOP  is a quadratic utility function capturing the said spatial correlation. We solve QCOP using mixed integer quadratic programming, with the resulting anytime algorithm capable of planning multiple disjoint tours that maximize the quadratic utility. In particular, our algorithm can quickly plan a near-optimal tour over a network with up to $150$ nodes. Beside performing extensive simulation studies to verify the algorithm's correctness and characterize its performance, we also successfully applied QCOP  to two realistic persistent monitoring tasks: 1) estimation over a synthetic spatiotemporal field and 2) estimating the temperature distribution in the state of Massachusetts in the United States.

Correlated Orienteering Problem and its Application to Persistent Monitoring Tasks

Multiple unmanned aerial vehicles (UAVs) are increasingly applied for surveillance tasks such as disaster management and environmental monitoring. Due to limited battery capacity and bounded wireless communication, small-scale UAVs pose fundamental challenges for achieving persistence. We propose an offline path planning algorithm that ensures that the UAVs can always reach the base station to replace their batteries and that each UAV is always connected with the base station via a single or multi-hop link. We focus on heterogeneous UAVs with different flight times. The single base station scenario is compared with several extensions for multiple base stations based on the maximum time a sensing location has not been visited.

Persistent multi-UAV surveillance with energy and communication constraints

Persistent coverage control for a team of agents with collision avoidance

The goal of this paper is twofold. First, we aim at investigating the potentials of reset observers (ReOs) applied to process control. Second, we aim to overcome the existing performance limitations of the temperature control in domestic cookers. To this end, we present an adaptive simmering control for induction cookers, whose parameters are updated online, depending on the estimates provided by a multiple-model ReO (MMReO). This new observer results in extending the idea of multiple models to the state observer framework. The MMReO consists of a reinitialized ReO and of multiple fixed identification models. The resultant control scheme satisfies the user requirements such as quick heating up, accurate temperature control, and fast disturbance rejection, outperforming previous results. Moreover, the proposed control scheme reduces energy consumption, and consequently, it can increase the efficiency of the whole cooking process. Additionally, a fixed robust quantitative feedback theory (QFT)-based controller is designed, and it is also used for comparison purposes. Several verification tests are carried out in real induction hobs to emphasize the effectiveness of our proposal compared with the QFT-based controller.

Adaptive Simmering Control for Domestic Induction Cookers

This technical note proposes a novel kind of state estimator called reset adaptive observer (ReO). A ReO is an adaptive observer consisting of an integrator and a reset law that resets the output of the integrator depending on a predefined condition. The main contribution of this technical note is that the reset element theory is applied for the first time to the nonlinear adaptive observer framework. The introduction of the reset element in the adaptive law can decrease the overshooting and settling time of the estimation process without sacrificing the rising time. The stability and convergence LMI-based analysis of the proposed ReO is addressed and, additionally, an easily computable method to determine the L2 gain of the ReO dealing with noise-corrupted systems is presented.

Reset Adaptive Observer for a Class of Nonlinear Systems

A new inductive sensor for temperature measurement in domestic induction hobs and results of its applications are presented. It consists of a coil and a resonant electronic circuit which measures the impedance variation of ferromagnetic pots when their temperature changes. This sensor has promising applicability in induction hobs because it has instantaneous response, it is cheap, and contactless. The performance of the sensor is tested in the cooking range from 20°C to 220° C with pots of different materials, revealing a measurement error lower than 6°C. This error is much lower than that obtained from measurements with current sensing systems installed in induction hobs, namely a thermistor measuring the temperature of the glass in the cooking zone. The proposed sensor will allow fire prevention in cooking processes with a cost comparable to that of conventional measurement systems.

Inductive Sensor for Temperature Measurement in Induction Heating Applications

https://www.tpbin.com/Uploads/Subjects/6e9c6f5e-3f55-465b-926b-a459f121c2fc.pdf

C. Franco

Papers

Persistent coverage control for a team of agents with collision avoidance

Adaptive Simmering Control for Domestic Induction Cookers

Reset Adaptive Observer for a Class of Nonlinear Systems

Inductive Sensor for Temperature Measurement in Induction Heating Applications

Reset observers applied to MIMO systems