David Naso

Closed loop control of dielectric elastomer actuators based on self-sensing displacement feedback

This paper describes a sensorless control algorithm for a positioning system based on a dielectric elastomer actuator (DEA). The voltage applied to the membrane and the resulting current can be measured during the actuation and used to estimate its displacement, i.e., to perform self-sensing. The estimated displacement can be then used as a feedback signal for a position control algorithm, which results in a compact device capable of operating in closed loop control without the need for additional electromechanical or optical transducers. In this work, a circular DEA preloaded with a bi-stable spring is used as a case of study to validate the proposed control architecture. A comparison of the closed loop performance achieved using an accurate laser displacement sensor for feedback is also provided to better assess the performance limitations of the overall sensorless scheme.

A genetic approach for adaptive multiagent control in heterarchical manufacturing systems

In this paper, we apply genetic algorithms to adapt the decision strategies of autonomous controllers in a part-driven heterarchical manufacturing system. The control agents use pre-assigned decision rules only for a limited amount of time, and obey a rule replacement policy propagating the most successful rules to the subsequent populations of concurrently operating agents. The twofold objective of this approach is to automatically optimize the performance of the control system during the steady-state unperturbed conditions of the manufacturing floor, and to improve the reactions of the agents to unforeseen disturbances (e.g., failures, shortages of materials) by adapting their decision strategies. Results on a detailed discrete event model of a multiagent heterarchical manufacturing system confirm the effectiveness of the approach.

Self-sensing in dielectric electro-active polymer actuator using linear-in-parametes online estimation

This paper presents a self-sensing methodology for Dielectric ElectroActive Polymer actuators. The proposed approach is based on using DEAP voltage and current to estimate electrical resistance and capacitance, and using the latter to reconstruct the actuator deformation. For the estimation of the electrical parameters, the performance of two standard linear regression algorithms are compared, i.e. standard Least Mean Squares (LMS) and Recursive Least Squares (RLS). Some filtering techniques are also suggested in order to improve the quality of the estimation. The full algorithm is first illustrated in detail and then validated on an experimental actuator prototype, consisting in a DEAP membrane combined with a bi-stable biasing element which enables large actuation stroke.

Comparison of Model-free and Model-based Control Techniques for a Positioning Actuator based on Magnetic Shape Memory Alloys

This paper addresses the control issue of a precise positioning system based on Magnetic Shape Memory Alloys (MSMAs). This family of smart materials exhibits a hysteresis phenomenon that needs to be properly addressed in order to build effective devices. A model-free control scheme is compared with two different model-based approaches which exploit an accurate hysteresis model to perform hysteresis cancellation or feedforward compensation. All the control schemes are based on a PID controller which is automatically tuned by solving a set of Linear Matrix Inequalities (LMIs) able to guarantee a desired exponential rate for the error convergence to zero. Finally, the comparison of model-free and model-based control schemes is performed using an experimental set-up to emphasize both the advantages and disadvantages of the different control strategies.

DYNAMIC ELECTROMECHANICAL MODELING OF A SPRING-BIASED DIELECTRIC ELECTROACTIVE POLYMER ACTUATOR SYSTEM

This paper presents a dynamic electromechanical model for an actuator system based on a Dielectric Electro-Active Polymer (DEAP) membrane biased with a linear spring. The motion is generated by the deformation of the membrane caused by the electrostatic compressive force between two compliant electrodes applied on the surface of the polymer. A mass and a linear spring are used to pre-load the membrane, allowing stroke in the out-of-plane direction. The development of mathematical models which accurately describe the nonlinear system dynamics is a fundamental step in order to design model-based, high-precision position control algorithms. In particular, knowledge of the nonlinear electrical dynamics of the actuator driving circuit can be exploited during the control system design in order to achieve desirable features, such as self-sensing or control energy minimization. This work proposes an electromechanical physical model of the DEAP actuator system. By means of numerous experiments, it is shown that the model can be used to predict the current by measuring deformation and voltage (electrical dynamics), as well as predicting deformation and current by measuring the voltage (electromechanical dynamics).

Self-sensing at low sampling-to-signal frequency ratio: An improved algorithm for dielectric elastomer actuators

This paper presents a new self-sensing algorithm for Dielectric Elastomer actuators. The method allows to obtain accurate estimations of material capacitance and electrodes resistance from voltage and current measurements, by means of online identification algorithms, e.g., RLS. While the capacitance permits to reconstruct the actuator displacement (self-sensing), the resistance can be used to extract further information on the actuator state, e.g., fatigue (self-monitoring). The new self-sensing method is presented and compared with a different algorithm previously developed by the authors. Simulations and experiments show how capacitance and resistance predicted by the new algorithm are in agreement with the values measured with an LCR meter. Moreover, it is shown how the accuracy of the new method does not deteriorate when reducing the sampling-to-signal frequency ratio (the method is tested up to a ratio of 2.5). This result enables achieving reliable self-sensing without a significant amount of online computation effort.

An overview on innovative mechatronic actuators based on smart materials

In recent years, smart materials have proven to represent an effective means for developing a novel generation of miniaturized electro-mechanical transducers. Thanks to their many features such as high energy density and efficiency, low power requirement, low cost, scalability, and high compactness, smart material can help improving the performance of several mechatronics systems, ranging from industrial applications to biomedical and bio-inspired ones. A wide spectrum of different smart materials, each one having unique features and limitations, is currently available. This paper aims at presenting three specific types of smart materials which have shown to be particularly suitable for micropositioning applications, i.e., shape memory alloys, magnetic shape memory alloys, and dielectric elastomers. These materials exhibits complementary characteristics in terms of stress, strain, and bandwidth which make them particularly suitable for different applications. In this paper, these three smart materials are discussed in details, and major features, challenges, and applications are highlighted.

Genetic algorithm based on the Lagrange method for the non-convex Economic Dispatch Problem

This paper considers the non-convex Economic Dispatch Problem (EDP) with power losses, prohibited operating zones, and generation cost functions modeling both valve-point loading effects and multiple fuel options. This constrained problem is stated as an unconstrained problem by using the augmented Lagrange formulation, while introducing Lagrange multipliers and penalty parameters. Then, a genetic algorithm (GA) relying on two iterative loops is described: the inner loop executes a GA with fixed penalty parameters and Lagrange multipliers, while the outer loop updates such parameters when required. The effects of four different mutation operators based on the Gaussian and Cauchy distributions is also investigated. Finally, the effectiveness of the proposed approach is shown by numerical simulations on two practical test systems.