Dielectric elastomer generators (DEGs) are a class of capacitive solid-state devices that employ highly stretchable dielectrics and conductors to convert mechanical energy into high-voltage direct-current electricity. Their promising performance in terms of convertible energy and power density has been mostly proven in quasi-static experimental tests with prescribed deformation. However, the assessment of their ability in harvesting energy from a dynamic oscillating source of mechanical energy is crucial to demonstrate their effectiveness in practical applications. This paper reports a first demonstration of a DEG system that is able to convert the oscillating energy carried by water waves into electricity. A DEG prototype is built using a commercial polyacrylate film (VHB 4905 by 3M) and an experimental campaign is conducted in a wave-flume facility, i.e. an artificial basin that makes it possible to generate programmed small-scale waves at different frequencies and amplitudes. In resonant conditions, the designed system demonstrates the delivery of a maximum of 0.87 W of electrical power output and 0.64 J energy generated per cycle, with corresponding densities per unit mass of dielectric elastomer of 197 W kg ⁻¹ and 145 J kg ⁻¹ . Additionally, a notable maximum fraction of 18% of the input wave energy is converted into electricity. The presented results provide a promising demonstration of the operation and effectiveness of ocean wave energy converters based on elastic capacitive generators.

Dielectric elastomers (DE) are incompressible rubberlike solids whose electrical and structural responses are highly nonlinear and strongly coupled. Thanks to their coupled electromechanical response, intrinsic lightness, easy manufacturability, and low-cost, DEs are perfectly suited for the development of novel solid-state polymeric energy conversion units with capacitive nature and high-voltage operation, which are more resilient, lightweight, integrated, economic, and disposable than traditional generators based on conventional electromagnetic technology. Inflated circular diaphragm dielectric elastomer generators (ICD-DEG) are a special embodiment of polymeric transducer that can be used to convert pneumatic energy into usable electricity. Potential application of ICD-DEG is as power take-off system for wave energy converters (WEC) based on the oscillating water column (OWC) principle. This paper presents a reduced, yet accurate, dynamic model for ICD-DEG that features one kinematic degree of freedom and which accounts for DE visco-elasticity. The model is computationally simple and can be easily integrated into existing wave-to-wire models of OWCs to be used for fast analysis and real-time applications. For demonstration purposes, integration of the considered ICD-DEG model with a lumped-parameter hydrodynamic model of a realistic OWC is also presented along with a simulation case study.

This paper introduces the analysis and design of a wave energy converter (WEC) that is equipped with a novel kind of electrostatic power take-off system, known as dielectric elastomer generator (DEG). We propose a modelling approach which relies on the combination of nonlinear potential-flow hydrodynamics and electro-hyperelastic theory. Such a model makes it possible to predict the system response in operational conditions, and thus it is employed to design and evaluate a DEG-based WEC that features an effective dynamic response. The model is validated through the design and test of a small-scale prototype, whose dynamics is tuned with waves at tank-scale using a set of scaling rules for the DEG dimensions introduced here in order to comply with Froude similarity laws. Wave-tank tests are conducted in regular and irregular waves with a functional DEG system that is controlled using a realistic prediction-free strategy. Remarkable average performance in realistically scaled sea states has been recorded during experiments, with peaks of power output of up to 3.8 W, corresponding to hundreds of kilowatts at full-scale. The obtained results demonstrated the concrete possibility of designing DEG-based WEC devices that are conceived for large-scale electrical energy production.

This paper studies neural network models of vehicle dynamics. We consider both models with a generic layer architecture and models with specialised topologies that hard-wire physics principles. Network pre-wiring is limited to universal laws; hence it does not limit the network modelling abilities on one side but allows more robust and interpretable models on the other side. Four different network types (with and without pre-wired structure, recursive and non-recursive) are compared for the longitudinal dynamics of a car with gears and two controls (brake and engine). Results show that pre-wiring effectively improves the performance. Non-recursive networks also look to be preferable for several reasons.

This letter presents a novel algorithm for the continuous control of dynamical systems that combines Trajectory Optimization (TO) and Reinforcement Learning (RL) in a single framework. The motivations behind this algorithm are the two main limitations of TO and RL when applied to continuous nonlinear systems to minimize a non-convex cost function. Specifically, TO can get stuck in poor local minima when the search is not initialized close to a “good” minimum. On the other hand, when dealing with continuous state and control spaces, the RL training process may be excessively long and strongly dependent on the exploration strategy. Thus, our algorithm learns a “good” control policy via TO-guided RL policy search that, when used as initial guess provider for TO, makes the trajectory optimization process less prone to converge to poor local optima. Our method is validated on several reaching problems featuring non-convex obstacle avoidance with different dynamical systems, including a car model with 6D state, and a 3-joint planar manipulator. Our results show the great capabilities of CACTO in escaping local minima, while being more computationally efficient than the Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO) RL algorithms.