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In this paper, we propose a domain-independent nonlinear thermoelastic coupling element to enable the realization of a two-way dynamic coupling between the thermal and elastic domains. The implementation of the proposed coupling element in multi-physical systems modeling will provide reversibly conserved power transactions among the existing physical domains (thermal and elastic domains), which is a favorable demand in coupled field dynamic investigations. To guarantee a conserved and continuous power transmission between the thermal and elastic domains, by means of the Bond Graph notation, a reversible multi-dimensional storage element is designed, and the corresponding multi-dimensional constitutive equations are derived that satisfy the Maxwell reciprocity. The power network of each domain of interest is, also, represented in the Bond Graph notation, and the corresponding governing equation of each domain is extracted on the basis of physical system theory. The reversible connectivity of the thermal and elastic domains is then executed via replacing the storage elements of each of the domains with the proposed coupling element. The ensuing model will provide the thermoelastic behavior of the system from the reversible thermal and elastic domains' energetic interactions, which preserves the fundamental physical natures of such phenomena in the model. The generated coupling element will allow the model of each physical domain to remain independent from other existing domains while they are dynamically coupled, which provides a unique benefit for multi-physical phenomena investigations. [ABSTRACT FROM AUTHOR]

In this study, a novel Multivariable Adaptive Neural Network Controller (MANNC) is developed for coupled model-free n-input n-output systems. The learning algorithm of the proposed controller does not rely on the model of a system and uses only the history of the system inputs and outputs. The system is considered as a 'black box' with no pre-knowledge of its internal structure. By online monitoring and possessing the system inputs and outputs, the parameters of the controller are adjusted. Using the accumulated gradient of the system error along with the Lyapunov stability analysis, the weights' adjustment convergence of the controller can be observed, and an optimal training number of the controller can be selected. The Lyapunov stability of the system is checked during the entire weight training process to enable the controller to handle any possible nonlinearities of the system. The effectiveness of the MANNC in controlling nonlinear square multiple-input multiple-output (MIMO) systems is demonstrated via three simulation studies covering the cases of a time-invariant nonlinear MIMO system, a time-variant nonlinear MIMO system, and a hybrid MIMO system, respectively. In each case, the performance of the MANNC is compared with that of a properly selected existing counterpart. Simulation results demonstrate that the proposed MANNC is capable of controlling various types of square MIMO systems with much improved performance over its existing counterpart. The unique properties of the MANNC will make it a suitable candidate for many industrial applications. [ABSTRACT FROM AUTHOR]

Understanding the true nature of viscoelastic behaviors in multi-physical systems has always been a challenging issue in system dynamic investigations, as each existing physical subdomain of the system may follow a different attenuation pattern during the dynamic process. In this study, to generate a viscoelastic model suitable for multi-physical domain dynamic investigations, a physical combined viscoelastic model is proposed. To this aim, by means of the bond graph approach, an energy-based conventional viscoelastic model is first generated, and its embedded dispersive mechanisms are interpreted physically. By including the interpreted dissipative mechanisms into the relative subdomains of an elastic domain, an energy-based combined viscoelastic model is then proposed. The obtained simulation results indicate that the proposed viscoelastic model is able to capture a variety of viscoelastic behaviors in the system with respect to the true physical nature of the system. [ABSTRACT FROM AUTHOR]

In this paper, a distributed shell-space decomposition (DSSD) scheme is proposed for rendezvous trajectory planning of multiple autonomous marine vehicles (AMVs); this category of vehicle includes both autonomous underwater vehicles and autonomous surface vessels. The DSSD extends the concept of shell-space decomposition (SSD) by generating multiple sets of shells radiating out from the starting position of each vehicle to the rendezvous destination, enabling each vehicle to generate its trajectory within its own SSD subset. This scheme is combined with an optimized mass-center rendezvous-point selection scheme, together with a B-spline-based quantum particle swarm optimization technique to find optimal rendezvous trajectories for multiple AMVs with minimal travel time and simultaneous time of arrival for all the participating vehicles. The path planner identifies the optimal rendezvous location and generates the corresponding rendezvous trajectories based on the capabilities of each vehicle and the dynamics of the ocean environment. Simulation results show that the proposed DSSD method, combined with a novel optimized mass-center rendezvous-point selection scheme, is able to find trajectories for multiple AMVs that ensure that they reach their common destination simultaneously and with optimized time/energy consumption. A set of representative Monte Carlo simulations were run to analyze the performance of these path planners for multiple AMVs rendezvous. The results demonstrate the inherent robustness and superiority of the proposed planner based on the combined DSSD method and optimized mass-center rendezvous-point selection scheme, in comparison with other techniques. [ABSTRACT FROM AUTHOR]

An imperialist competitive algorithm (ICA) is introduced for solving the optimal path planning problem for autonomous underwater vehicles (AUVs) operating in turbulent, cluttered, and uncertain environments. ICA is a new sociopolitically inspired global search metaheuristic based on a form of competition between “imperialist” forces and opposing colonies. In this study, ICA is applied to optimize the coordinates of a set of control points for generating a curved spline path. The ICA-based path planner is tested to find an optimal trajectory for an AUV navigating through a variable ocean environment in the presence of an irregularly shaped underwater terrain. The genetic algorithm (GA) and quantum-behaved particle swarm optimization (QPSO) are described and evaluated with the ICA for the path optimization problem. Simulation results show that the proposed ICA approach is able to obtain a more optimized trajectory than the GA- or QPSO-based methods. Monte Carlo simulations demonstrate the robustness and superiority of the proposed ICA scheme compared with the GA and QPSO schemes. [ABSTRACT FROM AUTHOR]