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This paper discusses algorithms for large displacement analysis of interconnected flexible and rigid multibody systems. Hydrostatic and hydrodynamic loads for systems being submerged in water are also considered. The systems may consist of cables and beams and may combine very flexible parts with rigid parts. Various ways of introducing structural joints are discussed. A special implementation of the Hilber‐Hughes‐Taylor time integration scheme for constrained non‐linear systems is outlined. The formulation is general and allows for displacements and rotational motion of unlimited size. Aspects concerning efficient solution of constrained dynamic problems are discussed. These capabilities have been implemented in a general purpose non‐linear finite element program. Applications involving static and dynamic analysis of a bi‐articulated tower and a floating tripod platform kept in place by three anchor lines are discussed.

Wind turbines are of growing importance for the production of renewable energy. The kinetic energy of the blowing air induces a rotary motion and is thus converted into electricity. From the mechanical point of view the complex dynamics of wind turbines become a matter of interest for structural optimization and optimal control in order to improve stability and energy efficiency. The purpose of this paper therefore is to present a mechanical model of a three‐blade wind turbine with a momentum and energy conserving time integration of the system.

The authors present a mechanical model based upon a rotationless formulation of rigid body dynamics coupled with flexible components. The resulting set of differential‐algebraic equations will be solved by using energy‐consistent time‐stepping schemes. Rigid and orthotropic‐elastic body models of a wind turbine show the robustness and accuracy of these schemes for the relevant problem.

Numerical studies prove that physically consistent time‐stepping schemes provide reliable results, especially for hybrid wind turbine models.

The application of energy‐consistent methods for time discretization is intended to provide computational robustness and to reduce the computational costs of the dynamical wind turbine systems. The model is aimed to give a first access into the investigation of fluid‐structure interaction for wind turbines.

This paper presents algorithms for computing the angular velocities of the bodies of a multibody system. A multibody system is any collection of connected bodies. The focus is upon multibody systems consisting of spherically pinned rigid bodies which do not form closed loops. Simple formulae are presented for computing the angular velocities. It is shown that once the angular velocities are known the entire kinematical description and hence, the dynamics of the system, may be developed routinely and in automated fashion. Extension to more general multibody systems follows without conceptual change in the procedures.

The purpose of this paper is to present the radial basis function (RBF) networks‐based adaptive robust control for an omni‐directional wheeled mobile manipulator in the presence of uncertainties and disturbances.

First, a dynamic model is obtained based on the practical omni‐directional wheeled mobile manipulator system. Second, the RBF neural network is used to identify the unstructured system dynamics directly due to its ability to approximate a nonlinear continuous function to arbitrary accuracy. Using the learning ability of neural networks, RBFNARC can co‐ordinately control the omni‐directional mobile platform and the mounted manipulator with different dynamics efficiently. The implementation of the control algorithm is dependent on the sliding mode control.

Based on the Lyapunov stability theory, the stability of the whole control system, the boundedness of the neural networks weight estimation errors, and the uniformly ultimate boundedness of the tracking error are all strictly guaranteed.

In this paper, an adaptive robust control scheme using neural networks combined with sliding mode control is proposed for crawler‐type mobile manipulators in the presence of uncertainties and disturbances. RBF neural networks approximate the system dynamics directly and overcome the structured uncertainty by learning. Based on the Lyapunov stability theory, the stability of the whole control system, the boundedness of the neural networks weight estimation errors, and the uniformly ultimate boundedness of the tracking error are all strictly guaranteed.

The main purpose of this paper is to find convenient methods to solve the differential‐algebraic equations which have great importance in various fields of science and engineering.

The paper applies a semi‐analytical approach, using both the homotopy analysis method (HAM) and the modified homotopy analysis method (MHAM) for finding the solution of linear and nonlinear DAEs.

The results show that the new modification can effectively reduce computational costs and accelerates the rapid convergence of the series solution.

Some high index DAEs are investigated to present a comparative study between the HAM and the MHAM.

For most practical control system problems, the state variables of a system are not often available or measureable due to technical or economical constraints. In these cases, an observer-based controller design problem, which is involved with using the available information on inputs and outputs to reconstruct the unmeasured states, is desirable, and it has been wide investigated in many practical applications. However, the investigation on a discrete-time singular Markovian jumping system is few so far. This paper aims to consider an observer-based control problem for a discrete-time singular Markovian jumping system and provides a set of easy-used conditions to the proposed control law.

According to the connotation of the separation principle extended from linear systems, a mode-dependent observer and a state-feedback controller is designed and carried out independently via two sets of derived necessary and sufficient conditions in terms of linear matrix inequalities (LMIs).

A set of necessary and sufficient conditions for an admissibility analysis problem related to a discrete-time singular Markovian jumping system is derived to be a doctrinal foundation for the proposed design problems. A mode-dependent observer and a controller for such systems could be designed via two sets of strictly LMI-based synthesis conditions.

The proposed method can be applied to discrete-time singular Markovian jumping systems with transition probability p_ij > 0 rather than the ones with p_ii = 0.

The formulated problem and proposed methods have extensive applications in various fields such as power systems, electrical circuits, robot systems, chemical systems, networked control systems and interconnected large-scale systems. Take robotic networked control systems for example. It is recognized that the variance phenomena derived from network transmission, such as packets dropout, loss and disorder, are suitable for modeling as a system with Markovian jumping modes, while the dynamics of the robot systems can be described by singular systems. In addition, the packets dropout or loss might result in unreliable transmission signals which motivates an observer-based control problem.

Both of the resultant conditions of analysis and synthesis problems for a discrete-time singular Markovian jumping system are necessary and sufficient, and are formed in strict LMIs, which can be used and implemented easily via MATLAB toolbox.

The purpose of this paper is to solve the tracking problem for free-floating space manipulators (FFSMs) in task space with parameter uncertainties and external disturbance.

In this paper, the novel cerebellar model articulation controller (CMAC) is designed with the feedback controller. More precisely, the parameter uncertainties in the FFSM are considered for achieving the robustness.

By using the dynamically equivalent model, the CMAC can be designed and trained with the desired performance, such that the prescribed trajectory can be followed accordingly. The simulation results are presented for illustrating the validity of the derived results.

Based on the designed CMAC, the tracking error would be approaching zero by choosing appropriate quantization level in CMAC and the corresponding learning rules can be tuned online.

In the post-capture stage, the tumbling target rotates the combined spacecraft system, and the detumbling operation performed by the space robot is required. To save the costly onboard fuel of the space robot, this paper aims to present a novel post-capture detumbling strategy.

Actuated by the joint rotations of the manipulator, the combined system is driven from three-axis tumbling state to uniaxial rotation about its maximum principal axis. Only unidirectional thrust perpendicular to the axis is needed to slow down the uniaxial rotation, thus saving the thruster fuel. The optimization problem of the collision-free detumbling trajectory of the space robot is described, and it is optimized by the particle swarm optimization algorithm.

The numerical simulation results show that along the trajectory planned by the detumbling strategy, the maneuver of the manipulator can precisely drive the combined system to rotate around its maximum principal axis, and the final kinetic energy of the combined system is smaller than the initial. The unidirectional thrust and the lower kinetic energy can ensure the fuel-saving in the subsequent detumbling stage.

This paper presents a post-capture detumbling strategy to drive the combined system from three-axis tumbling state to uniaxial rotation about its maximum principal axis by redistributing the angular momentum of the parts of the combined system. The strategy reduces the thrust torque for detumbling to effectively save the thruster fuel.

Recent research has shown that constrained bistable structures can display negative stiffness behavior and provide extremal vibrational and acoustical absorptive capacity. These bistable structures are therefore compelling candidates for constructing new meta‐materials for noise reduction, anechoic coatings, and backing materials for broadband imaging transducers. To date, demonstrations of these capabilities have been primarily theoretical because the geometry of bistable elements is difficult to construct and refine with conventional manufacturing methods and materials. The purpose of this paper is to leverage the geometric design freedoms provided by selective laser sintering (SLS) technology to design and construct constrained bistable structures with negative stiffness behavior.

A meso‐scale negative stiffness system is designed and fabricated with SLS technology. The system includes a bistable structure in the form of a pre‐compressed/pre‐buckled beam. The dynamic transmissibility of the system is measured, and its behavior is compared to the predictions of analytical models.

Experimental results demonstrate that pre‐compression and pre‐buckling can be used to induce negative stiffness behavior and thereby increase the damping and shift the resonant frequency of an unconstrained beam.

The results support the usefulness of SLS and other additive manufacturing technologies for acoustic and dynamic applications. Specifically, the demonstrated advantages of SLS include the ability to rapidly redesign, functionally 2 prototype, and tune physical models for acoustic and dynamic experimentation. Of significant importance is the ability of SLS to enable consolidation of parts that are traditionally separate, thereby reducing vibrational noise in these systems. In this specific application, SLS enables a proof‐of‐concept comparison of the theoretical and experimental behavior of a meso‐scale negative stiffness system. The demonstrated acoustical and vibrational absorptive capacity of these systems is expected to lead to designs for new structures and materials that offer significantly improved energy absorbing capabilities over a broad range of tunable frequencies without compromising structural stiffness.

The purpose of this paper is to present the use of artificial immune systems (AISs) to solve constrained design optimization problems for active magnetic bearings (AMBs).

This research applies the AIS approach, more specifically, a representative clonal selection-based AIS called CLONALG, to the single-objective structural design optimization of AMBs. In addition, when compared with a genetic algorithm (GA) developed in the previous work, the CLONALG fails to produce best solutions when a nearly zero feasible ratio occurs in an AMB design problem. Therefore, an AIS called ARISCO (AIS for constrained optimization) is proposed to address the above issue.

A total of six AMB design cases are solved by the GA, CLONALG, and ARISCO. Based on the simulation results, in terms of solution quality, the ARISCO is shown to have better overall performance than the CLONALG and GA. In particular, when solving a problem with a nearly zero feasible ratio, the ARISCO and GA perform equally and both outperform the CLONALG.

In summary, the contributions of this paper include: this research applies the AIS approach, more precisely, the CLONALG, to the single-objective structural design optimization of AMBs; the ARISCO overall produces better AMB designs than the CLONALG and a GA developed in the previous work; in situations where a nearly zero feasible ratio occurs, the ARISCO and GA perform equally, and they both outperform the CLONALG.