This paper proposes the compact differential evolution (cDE) algorithm. cDE, like other compact evolutionary algorithms, does not process a population of solutions but its statistic description which evolves similarly to all the evolutionary algorithms. In addition, cDE employs the mutation and crossover typical of differential evolution (DE) thus reproducing its search logic. Unlike other compact evolutionary algorithms, in cDE, the survivor selection scheme of DE can be straightforwardly encoded. One important feature of the proposed cDE algorithm is the capability of efficiently performing an optimization process despite a limited memory requirement. This fact makes the cDE algorithm suitable for hardware contexts characterized by small computational power such as micro-controllers and commercial robots. In addition, due to its nature cDE uses an implicit randomization of the offspring generation which corrects and improves the DE search logic. An extensive numerical setup has been implemented in order to prove the viability of cDE and test its performance with respect to other modern compact evolutionary algorithms and state-of-the-art population-based DE algorithms. Test results show that cDE outperforms on a regular basis its corresponding population-based DE variant. Experiments have been repeated for four different mutation schemes. In addition cDE outperforms other modern compact algorithms and displays a competitive performance with respect to state-of-the-art population-based algorithms employing a DE logic. Finally, the cDE is applied to a challenging experimental case study regarding the on-line training of a nonlinear neural-network-based controller for a precise positioning system subject to changes of payload. The main peculiarity of this control application is that the control software is not implemented into a computer connected to the control system but directly on the micro-controller. Both numerical results on the test functions and experimental results on the real-world problem are very promising and allow us to think that cDE and future developments can be an efficient option for optimization in hardware environments characterized by limited memory.

IgA Nephropathy (IgAN) is a worldwide disease that affects kidneys in human beings and leads to end-stage kidney disease (ESKD) thus requiring renal replacement therapy with dialysis or kidney transplantation. The need for new tools able to help clinicians in predicting ESKD risk for IgAN patients is highly recognized in the medical field. In this paper we present a software tool that exploits the power of artificial neural networks to classify patients’ health status potentially leading to ESKD. The classifier leverages the results returned by an ensemble of 10 networks trained by using data collected in a period of 38 years at University of Bari. The developed tool has been made available both as an online Web application and as an Android mobile app. Noteworthy to its clinical usefulness is that its development is based on the largest available cohort worldwide.

This paper presents a distributed algorithm based on auction techniques and consensus protocols to solve the nonconvex economic dispatch problem. The optimization problem of the nonconvex economic dispatch includes several constraints such as valve-point loading effect, multiple fuel option, and prohibited operating zones. Each generating unit locally evaluates quantities used as bids in the auction mechanism. These units send their bids to their neighbors in a communication graph that supports the power system and which provides the required information flow. A consensus procedure is used to share the bids among the network agents and resolves the auction. As a result, the power distribution of generating units is updated and the generation cost is minimized. The effectiveness of this approach is demonstrated by simulations on standard test systems.

This papers deals with PI and PID control of second order systems with an input hysteresis described by a modified Prandtl-Ishlinskii model. The problem of the asymptotic tracking of constant references is re-formulated as the stability of a polytopic linear differential inclusion. This offers a simple linear matrix inequality condition that, when satisfied with the chosen PI or PID controller gains, ensures the tracking of constant reference and also allows the design to establish a performance index. The validation of the approach is performed experimentally on a Magnetic Shape Memory Alloy micrometric positioning system

This paper describes a precise positioning system based on magnetic shape memory alloys (MSMAs). This new type of material shows an interesting potential in the area of mechatronics due to its outstanding magnetically-induced strain, which is significantly larger than the one exhibited by other common active materials such as piezoelectric ceramics. However, MSMAs still have not found their way into industrial applications mainly due to their high hysteretic behavior and the strong sensitivity to temperature changes. The aim of this paper is to present the main challenges of using MSMAs for precise positioning systems by means of a simple yet effective experimental prototype. In particular, this paper examines the problem of effectively controlling the device in closed-loop. The performance of an adaptive hysteresis compensator based on the Preisach-like Krasnosel'skii-Pokrovskii model is analyzed and evaluated in the presence of temperature changes. Experiments confirm that the undesirable effects of temperature on the precision of the device can be partially addressed with an adaptive model-based algorithm devised to cope with time-varying nonlinearities.

n this paper we develop an algorithm for adaptive compensation class of methods. Literature offers a wide control of unconventional actuators based on Prandtl-Ishlinskii models and Lyapunov design. The chosen family of models is general enough to capture the strongly variable shapes of the hysteresis exhibited by some electro-active materials and has an inverse model that can be computed analytically. The approach proposed in this paper adapts the parameters of the model with a learning law based on the minimization of the tracking error, has the useful property of allowing the analytical and handles the parameters having a nonlinear influence on the output of the model by means of linearization. An outer position loop is then introduced to compensate the residual compensation error and further improve the tracking performance. The advantages and limitations of the approach are discussed and confirmed by experiments on a mechatronic position actuator based on magnetic shape memory alloys.

Abstract-This paper considers the problem of decentralized task assignment in a network of heterogeneous robots. We introduce a new algorithm named heterogeneous robots consensus-based allocation (HRCA), which can be viewed as a possible extension of the recently proposed consensus-based bundle algorithm (CBBA) for homogeneous robot networks. The HRCA is based on a two stage decentralized procedure. In the first stage, similarly to CBBA, an initial assignment based on market-based decision strategies and local communication is determined, disregarding possible constraints on the maximum number of tasks assignable to each robot. Constraint violations are handled in the second stage, in which an iterative procedure is used by the robots to redistribute the tasks exceeding their individual capacity with minimal losses in terms of score function. Numerical simulations are used to evaluate the performance of the HRCA in a set of randomly generated scenarios, which include some examples of homogeneous networks to allow a comparison with CBBA.

This paper considers a control strategy for systems affected by time-varying hysteretic phenomena, such as those observed in magnetic shape memory alloys subject to temperature variations. The proposed controller is based on a scheme that combines feed-forward cancellation of the hysteresis using a Modified Prandtl-Ishlinskii inverse model with a closed-loop control law designed to address the cancellation errors. Both the inverse hysteresis model and the closed-loop law feature adjustable parameters that are adapted on-line by means of learning laws based on Lyapunov design tools. The effectiveness of the proposed approach is confirmed by experiments on a prototypical micro-metric positioning system containing a bar of magnetic shape memory alloy as main actuating element.

In this paper we develop an adaptive version of the so-called modified Prandtl-Ishlinskii model of hysteresis. This model is able to capture various shapes of hysteresis and, moreover, it admits an explicit mathematical formulation for the inverse model. The Lyapunov-based adaptation is then used to compensate the hysteretic nonlinearities of an unconventional actuator based on magnetic shape memory alloys.

This paper considers the problem of assigning a set of tasks to a set of heterogeneous agents under the additional assumptions that some tasks must be necessarily allocated and therefore are critical for the assignment problem, and that each agent can execute a limited number of tasks. In order to solve this problem in a decentralized way (i.e., without any form of central supervision), we develop an extension of an algorithm proposed in the recent literature. After analyzing convergence and communication requirement of the algorithm, a set of numerical simulations is provided to confirm the effectiveness of the proposed approach. © 2013 Elsevier B.V. All rights reserved.

This paper considers the task allocation problem for a network of heterogeneous agents under the additional assumptions that each agent can execute a limited number of tasks due to its physical limitations and that some tasks considered critical (i.e., mandatory) for the assignment problem must be necessarily assigned. In order to solve this problem in a decentralized way, we propose an algorithm that builds an initial assignment through a market-based approach and resolves conflicts with a consensus procedure based on local communications between neighboring agents. Numerical simulations are performed to evaluate the effectiveness of the proposed approach.""

A distributed algorithm is presented to solve the economic power dispatch with transmission line losses and generator constraints. The proposed approach is based on two consensus algorithms running in parallel. The first algorithm is a first-order consensus protocol modified by a correction term which uses a local estimation of the system power mismatch to ensure the generation-demand equality. The second algorithm performs the estimation of the power mismatch in the system using a consensus strategy called consensus on the most up-to-date information. The proposed approach can handle networks of different size and topology using the information about the number of nodes which is also evaluated in a distributed fashion. Simulations performed on standard test cases demonstrate the effectiveness of the proposed approach for both small and large systems.

This paper deals with the stability analysis of PI and PID control of dynamic systems with an input hysteresis described by a modified Prandtl-Ishlinskii model. The problem of the asymptotic tracking of constant references is reformulated as the stability of a polytopic linear differential inclusion. This offers a simple linear matrix inequality condition that, when satisfied with the chosen PI or PID controller gains, ensures the tracking of constant references, allows the designer to establish a performance index and allows using powerful analysis and design tools for the controller. The validation of the approach is performed experimentally on a Magnetic Shape Memory Alloy micrometric positioning system.

This paper proposes an approach to deal with the control of a class of dynamical systems affected by hysteresis, which is particularly common in applications of smart materials to motion control. The controlled plant is assumed to be a combination of a linear system with a hysteretic operator that can appear either in series or in a feedback path with respect to the linear component, while the controller is defined as a linear combination of the tracking error, its integral and derivatives. This paper mainly focuses on tracking behavior with constant references, and formulates the output regulation as a problem of stability of a polytopic linear differential inclusion, which does not require the identification of an accurate (direct or inverse) model of the hysteresis. The resulting conditions allow the user to seek for controller parameters that guarantee the achievement of a predefined control goal by solving a linear matrix inequality problem. Beside validation through numerical simulation, the method is successfully applied to control a challenging and innovative system, which uses two bars of magnetic shape memory alloy as the active elements of a multistable precise positioning device.