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Off-road vehicles always experience serious uncertain longitudinal and lateral slips when running on soft and slope terrains, and some parameters of the vehicles, such as the cornering stiffness and the slip of wheels, are always not constants. In this paper, control strategies for the torque of each wheel and the rear-wheel-steering angle are proposed to maintain a stable velocity and approach an ideal reference model for the off-road vehicle by using second-order sliding mode (SOSM) techniques. An observer is constructed to estimate the actual sideslip angle of the vehicle with the consideration of uncertainties and disturbances of the system. Then, with conditions of bounded uncertainties and disturbances, composite super-twisting (ST) controllers combined with a velocity controller are designed to generate the total torque, the differential torque, and the rear-wheel-steering angle. On this basis, the proposed controllers have been verified to lead good robustness for maintaining the stable velocity and approaching the ideal reference model by using an optimal torque allocation controller at a lower layer. In comparison with conventional yaw moment controllers without the rear-wheel-steering control, the proposed controllers are shown to be more effective. [ABSTRACT FROM AUTHOR]

The lunar surface is a complicated and unknown terrain, and hence, astronauts must be trained to travel on that surface by using a prototype vehicle on the Earth's terrain. However, the gravity of the Earth is six times that of the Moon; thus, the acceleration of a prototype vehicle on the Earth will differ from that of a manned lunar rover on the Moon, when the same acceleration handling input is provided. To obtain the same acceleration in the prototype vehicle on the Earth as that obtained on the Moon, an imitation handling ratio describing the relationship between the acceleration handling input and the wheel rolling speed is proposed in order to replicate the lunar gravitational conditions on the Earth. First, a whole-vehicle acceleration model was established based on the mechanical model of a deformable wheel with a mesh surface. Thereby an acceleration imitation algorithm was derived. A prototype vehicle for use on the Earth is proposed for the purpose of imitating the manned lunar rover under four different vehicle conditions. Fitted functions were used in order to obtain the imitation handling ratio. Finally, experiments were conducted to verify the imitation method; the results revealed that the prototype vehicle on the Earth could effectively approximate the same accelerations experienced by a manned lunar rover, when equivalent acceleration handling inputs were provided. [ABSTRACT FROM AUTHOR]

As an indispensable tool for astronauts on lunar surface, the lunar roving vehicle (LRV) is of great significance for manned lunar exploration. An LRV moves on loose and soft lunar soil, so the mechanical property of its wheels directly affects the mobility performance. The wheels used for LRV have deformable and mesh pattern, therefore, the existing mechanical theory of vehicle wheel cannot be used directly for analyzing the property of LRV wheels. In this paper, a new mechanical model for LRV wheel is proposed. At first, a mechanical model for a rigid normal wheel is presented, which involves in multiple conventional parameters such as vertical load, tangential traction force, lateral force, and slip ratio. Secondly, six equivalent coefficients are introduced to amend the rigid normal wheel model to fit for the wheels with deformable and mesh-pattern in LRV application. Thirdly, the values of the six equivalent coefficients are identified by using experimental data obtained in an LRV’s single wheel testing. Finally, the identified mechanical model for LRV’s wheel with deformable and mesh pattern are further verified and validated by using additional experimental results. [ABSTRACT FROM AUTHOR]

A lunar roving vehicle (LRV) is an important and indispensable detection tool that can not only transport astronauts a long distance from the lunar module but also ensures their safety when moving around the lunar surface. Thus, it is very important to train astronauts to drive the LRV on Earth. However, astronauts will experience different properties and driving sensations for the same vehicle owing to the difference in the gravities of Earth and the Moon. Thus, it is hard to train them on Earth using a normal LRV. Therefore, an approach is used to produce a vehicle that has the same steering properties and driving sensations as the LRV on the lunar surface. This approach involves a control algorithm that is developed in this paper using the H ∞ control theory. The math calculation and visual simulation results show that a four-wheel steering imitating LRV, which uses an imitating control algorithm, with either a higher stiffness torsion bar spring or lower payload than the LRV has a high imitation accuracy for the yaw speed, slip angle, and lateral acceleration at the driver’s position. Finally, a highly accurate approach for imitating the steering of an LRV on the lunar surface has been realized, which lays the foundation for training astronauts to drive an LRV. [ABSTRACT FROM AUTHOR]

To investigate and improve the mobility of the Lunar Roving Vehicle (LRV), it is necessary to consider the mechanical properties of the interaction between the wheels and the ground. In this paper, a new solution method, the forced-slip solution method, which uses a semi-empirical approach, was presented. That is, given the wheel’s vertical load and drawbar pull or driving torque as known input values, the unknown slip ratio can be resolved. The alternative method involves predicting the mechanics for a given slip ratio. The proposed method correlates better with actual wheel movements, and by studying a single wheel, this solution method can also be used to resolve the mechanical properties of the front and rear wheels in a four-wheel-drive (4WD) LRV configuration. It can also be used to consider the multi-pass effect of the rear wheels on lunar soil. The calculation results show that the 4WD LRV driving efficiency varies with the position of the center of mass. Thus, the LRV driving efficiency can be optimized by adjusting the position of its center of mass. [ABSTRACT FROM AUTHOR]

Physical compliance has been increasingly used in robotic legs, due to its advantages in terms of the mechanical regulation of leg mechanics and energetics and the passive response to abrupt external disturbances during locomotion. This article presents a review of the exploitation of physical compliance in robotic legs. Particular attention has been paid to the segmented, electrically actuated robotic legs, such that a comparable analysis can be provided. The utilization of physical compliance is divided into three main categories, depending on the setting locations and configurations, namely, (1) joint series compliance, (2) joint parallel compliance, and (3) leg distal compliance. With an overview of the representative work related to each category, the corresponding working principles and implementation processes of various physical compliances are explained. After that, we analyze in detail some of the structural characteristics and performance influences of the existing designs, including the realization method, compliance profile, damping design, and quantitative changes in terms of mechanics and energetics. In parallel, the design challenges and possible future works associated with physical compliance in robotic legs are also identified and proposed. This article is expected to provide useful paradigmatic implementations and design guidance for physical compliance for researchers in the construction of novel physically compliant robotic legs. [ABSTRACT FROM AUTHOR]

Power generation is an important energy conversion process. A prominent feature of wind power plant compared with traditional power plants is that the equipment utilization has great intermittency and uncertainty. Fortunately, with the popularity of converter-based wind turbines, doubly-fed induction generator wind power plants are able to not only employ wind turbines in converting wind power to electrical power, but also use the converter capacity to produce reactive power. Traditionally, P Q curves considering current constraints are used for active and reactive power distribution, and therein the fixed maximum slip in power control is conservative for the utility of converters. To fully extract the power generation capacity, a novel power control realizing adaptive variation of maximum slip is proposed for each wind turbine to dynamically expand the reactive power capacity in the high active power region. In the meanwhile, the dispatch scheme of the wind power plant based on the conventional P Q curve has to be upgraded accordingly. Given that the unfixed maximum slip makes it impossible to obtain the P Q curve with explicit expression, an online learning method based on neural network is adopted and further developed in a distributed architecture to be consistent with natural distributed characteristics of the large-scale wind power plant. With the help of communication among agents through consensus protocol, the algorithm convergence can be guaranteed even under distinct information fragments. Case studies demonstrate that the proposed scheme is able to achieve up to 30% expansion of reactive power capacity and 16.3% voltage sag alleviation under certain conditions. • A dynamic-slip-limit controller realizing up to 30% reactive power capacity expansion. • A distributed neutral network training developed for a full-frame PQ curve acquisition. • A consensus-based control architecture achieving an improved grid voltage support. [ABSTRACT FROM AUTHOR]

This paper considers distributed optimisation problems with black-box functions using surrogate-assisted methods. Since the cost functions and their derivatives are usually impossible to be expressed by explicit functions due to the complexity of modern systems, function calls have to be performed to obtain those values. Moreover, the cost functions are often expensive to evaluate, and therefore designers prefer to reduce the number of evaluations. In this paper, surrogate-based methods are utilised to approximate the true functions, and conditions for constructing smooth and convex surrogates are established, by which the requirements for explicit functions are eliminated. To improve the quality of surrogate models, a distance-based infill strategy is proposed to balance the exploitation and exploration, which guarantees the density of the decision sequence in a compact set. Then, a distributed optimisation algorithm is developed to solve the reformulated auxiliary sub-problems, and the convergence of the proposed algorithm is established via Lyapunov theory. Simulation examples are provided to validate the effectiveness of the theoretical development and demonstrate the potential significance of the framework. [ABSTRACT FROM AUTHOR]

Blade breakages in wind turbines are serious threats to reliable power generation. However, blade breakage detection is still challenging owing to varying circumstances and the questionable assumption that distinct health conditions result in inherently separable measurements. To address these challenges, a novel method is proposed for blade breakage detection based on environment-adapted contrastive learning. Specifically, nonparametric regression models between operational and environmental variables are established, and the variable adjustment is conducted to suppress the influence of variational circumstances on SCADA measurements. In addition, an improved strategy of contrastive learning is developed to learn valid data representations, guaranteeing the assumed separability of different samples. Furthermore, normal models of healthy operations are constructed, and online monitoring is implemented based on likelihood estimation and control charts. Actual data from commercial wind farms have been employed to demonstrate the effectiveness and superiority of the proposed method. Experimental results confirm that the correlation degree between operational and environmental variables can be reduced by 28.7 %, and blade breakages can be detected more than 23 h in advance. [ABSTRACT FROM AUTHOR]

This paper proposes a novel control scheme with a three-layer hierarchical structure to improve the cornering stability of the dual-motor rear-wheel drive (RWD) vehicles with the electronic differential system (EDS). The proposed hierarchical structure for the control system includes the observing layer, control layer, and actuation layer. In the observing layer, the driver model is designed to obtain the nominal steering angle, and the state observer is designed to obtain the yaw angle which cannot be easily measured. Then, particle swarm optimization (PSO) and second order sliding mode control (SOSMC) are employed in the control layer. The SOSMC part is used to design the control law to eliminate the chattering problem in the sliding mode algorithm, and the PSO part is used to obtain the optimal weights in the sliding mode surface to meet the minimum sideslip angle error and yaw rate error. The actuation layer allocates the corrected yaw moment by distributing the driving force to each independent driving wheel. Finally, the numerical tests are carried out under the double line change (DLC) maneuver. The results show that the proposed control system can effectively improve the cornering stability of the dual-motor RWD vehicles and reduce their motor power consumption. [ABSTRACT FROM AUTHOR]