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Barge-type offshore floating wind turbine (OFWT) commonly exhibits an under-actuated phenomenon in an offshore environment, which leads to a potential vibration-damping hazard. This article aims to provide a new robust output feedback anti-vibrational control scheme for the novel translational oscillator with rotational actuator (TORA) based five-degrees of freedom (5-DOF) barge-type OFWT in the presence of unwanted disturbances and modeling uncertainties.

In this paper, an active control technique called TORA has been used to design a 5-DOF barge-type OFWT model, where the mathematical model of the proposed system is derived by using Euler–Lagrange's equations. The robust hierarchical backstepping integral nonsingular terminal sliding mode control (HBINTSMC) with an adaptive gain is used in conjunction with extended order high gain observer (EHGO) to achieve system stabilization in the presence of unwanted disturbances and modeling uncertainties. The numerical simulations based on MATLAB/SIMULINK have been performed to demonstrate the feasibility and effectiveness of the proposed model and control law.

The numerical simulation results affirm the accuracy and efficiency of the proposed control law for the TORA based OFWT system. The results demonstrate that the proposed control law is robust against unwanted disturbances and uncertainties. The unknown states are accurately estimated by EHGO which enables the controller to exhibit improved stabilization performance.

A new mathematical model of the 5-DOF barge-type OFWT system based on TORA is the major contribution of this research paper. Furthermore, it provides a new adaptive anti-vibration control scheme by incorporating the EHGO for the proposed model.

To propose novel predictor‐corrector time‐integration algorithms for pseudo‐dynamic testing.

The novel predictor‐corrector time‐integration algorithms are based on both the implicit and the explicit version of the generalized‐α method. In the non‐linear unforced case second‐order accuracy, stability in energy, energy decay in the high‐frequency range as well as asymptotic annihilation are distinctive properties of the generalized‐α scheme; while in the non‐linear forced case they are the limited error near the resonance in terms of frequency location and intensity of the resonant peak. The implicit generalized‐α algorithm has been implemented in a predictor‐one corrector form giving rise to the implicit IPC‐ρ_∞ method, able to avoid iterative corrections which are expensive from an experimental standpoint and load oscillations of numerical origin. Moreover, the scheme embodies a secant stiffness formula able to approximate closely the actual stiffness of a structure. Also an explicit algorithm has been implemented, the EPC‐ρ_b method, endowed with user‐controlled dissipation properties. The resulting schemes have been tested experimentally both on a two‐ and on a six‐degrees‐of‐freedom system, exploiting substructuring techniques.

The analytical findings and the tests have indicated that the proposed numerical strategies enhance the performance of the pseudo‐dynamic test (PDT) method even in an environment characterized by considerable experimental errors. Moreover, the schemes have been tested numerically on strongly non‐linear multiple‐degrees‐of‐freedom systems reproduced with the Bouc‐Wen hysteretic model, showing that the proposed algorithms reap the benefits of the parent generalized‐α methods.

Further developments envisaged for this study are the application of the IPC‐ρ_∞ method and of EPC‐ρ_b scheme to partitioned procedures for high‐speed pseudo‐dynamic testing with substructuring.

The implicit IPC‐ρ_∞ and the explicit EPC‐ρ_b methods allow a user to have defined dissipation which reduces the effects of experimental error in the PDT without needing onerous iterations.

The paper proposes novel time‐integration algorithms for pseudo‐dynamic testing. Thanks to a predictor‐corrector form of the generalized‐α method, the proposed schemes maintain a high computational efficiency and accuracy.

This paper presents an evolutionary algorithm (EA) for Sony legged robots to learn good walking behaviours with little or no interaction with the designers. Once the learning method is put into place, the module can learn through its interaction with the real world.

An EA for developing locomotion gaits of quadruped walking robots is presented in this paper. It is based on a hybrid approach that changes the probability of genetic operators in respect to the performance of the operator's offspring.

The mutating and combination behaviours of the genetic algorithms allow the process to develop a useful behaviour over time. The resulting gait from this training proved to be a better solution than the non‐interference training for movements over all types of surfaces, pointing to a local optima being discovered in the non‐environmental interference situation.

The behaviour of these algorithms is stochastic so that they may potentially present different solutions in different runs of the same algorithm. The mechanism described here has several features that should be noted. It allows rapid parameterisation of operator probabilities across the range of potential genetic algorithms and operator set. It is tailored to a steady state reproduction scheme. It would not be literally applicable to problems with noisy evaluation functions.

Provides novel application of genetic algorithms to a potentially practical application area.

The purpose of this study is to provide the full-state mathematical model and devise a nonlinear controller for a balloon-supported unmanned aerial vehicle (BUAV).

Newtonian mechanics is used to establish the nonlinear mathematical model of the proposed vehicle assembly which incorporates the dynamics of both balloon and quadrotor UAV. A controllable form of the nine degrees of freedom model is derived. Backstepping control is designed for the proposed model and simulations are performed to assess the tracking performance of the proposed control.

The results show that the proposed methodology works well for smooth trajectories in presence of wind gusts. Moreover, the final mathematical model is affine and various nonlinear control techniques can be used in the future for improved system performance.

Multi-rotor unmanned aerial vehicles (MUAVs) are equipped with controllers but are constrained by smaller flight endurance and payload carrying capability. On the contrary, lighter than air (LTA) aerial vehicles have longer flight times but have poor control performance for outdoor operations. One of the solutions to achieve better flight endurance and payload carrying capability is to augment the LTA balloon to MUAV. The novelty of this research lies in full-order mathematical modeling along with transformation to controllable form for the BUAV assembly.

This study proposes a predictive neural network model reference decoupling control method for the coupling problem between the leg joints of hydraulic quadruped robots, and verifies its decoupling effect..

The machine–hydraulic cross-linking coupling is studied as the coupling behavior of the hydraulically driven quadruped robot, and the mechanical dynamics coupling force of the robot system is controlled as the disturbance force of the hydraulic system through the Jacobian matrix transformation. According to the principle of multivariable decoupling, a prediction-based neural network model reference decoupling control method is proposed; each module of the control algorithm is designed one by one, and the stability of the system is analyzed by the Lyapunov stability theorem.

The simulation and experimental research on the robot joint decoupling control method is carried out, and the prediction-based neural network model reference decoupling control method is compared with the decoupling control method without any decoupling control method. The results show that taking the coupling effect experiment between the hip joint and knee joint as an example, after using the predictive neural network model reference decoupling control method, the phase lag of the hip joint response line was reduced from 20.3° to 14.8°, the amplitude attenuation was reduced from 1.82% to 0.21%, the maximum error of the knee joint coupling line was reduced from 0.67 mm to 0.16 mm and the coupling effect between the hip joint and knee joint was reduced from 1.9% to 0.48%, achieving good decoupling.

The prediction-based neural network model reference decoupling control method proposed in this paper can use the neural network model to predict the next output of the system according to the input and output. Finally, the weights of the neural network are corrected online according to the predicted output and the given reference output, so that the optimization index of the neural network decoupling controller is extremely small, and the purpose of decoupling control is achieved.