Accurate dynamic model is essential for the model-based control of robotic systems. However, on the one hand, the nonlinearity of the friction is seldom treated in robot dynamics. On the other hand, few of the previous studies reasonably balance the calculation time-consuming and the quality for the excitation trajectory optimization. To address these challenges, this article gives a Lie-theory-based dynamic modeling scheme of multi-degree-of-freedom (DoF) serial robots involving nonlinear friction and excitation trajectory optimization. First, we introduce two coefficients to describe the Stribeck characteristics of Coulomb and static friction and consider the dependency of friction on load torque, so as to propose an improved Stribeck friction model. Whereafter, the improved friction model is simplified in a no-load scenario, a novel nonlinear dynamic model is linearized to capture the features of viscous friction across the entire velocity range. Additionally, a new optimization algorithm of excitation trajectories is presented considering the benefits of three different optimization criteria to design the optimal excitation trajectory. On the basis of the above, we retrieve a feasible dynamic parameter set of serial robots through the hybrid least square algorithm. Finally, our research is supported by simulation and experimental analyses of different combinations on the seven-DoF Franka Emika robot. The results show that the proposed friction has better accuracy performance, and the modified optimization algorithm can reduce the overall time required for the optimization process while maintaining the quality of the identification results.

Evolution has shown that legged locomotion is most adequate for tasks requiring versatile movement on land, allowing animals to traverse a wide variety of environments ranging from natural terrain to artificial, man-made landscapes with great ease. By employing well-designed control schemes, this ability could be replicated for legged robots, enabling them to be used in critical situations that still pose great danger to human integrity, such as search and rescue missions, inspection of hazardous areas, and even space exploration. This work characterizes the quadruped robot and contact dynamics that will compose our in-house simulator to be used for prototyping locomotion control schemes applied to quadruped robots. The proposed simulator computes the robot dynamics using the Recursive Newton-Euler and Composite-Rigid-Body algorithms with a few modifications to make certain aspects relevant for contact detection and control more easily accessible; furthermore, a compliant contact force method alongside stick-slip friction modeled the contact dynamics. To allow the robot to move, a simple PD-independent joint controller was implemented to track a desired leg trajectory. With the same robot and controller implemented using the MuJoCo simulation software, this work evaluates the proposed simulator by comparing characteristic locomotion signals such as the trunk pose and the ground reaction forces. Results showed similar behavior for both simulators, especially with regard to the contact detection, despite the significantly different contact models. Lastly, final remarks to enhance our simulator’s performance are suggested to be explored in future works.

The present article analyzes the use of preposition stranding (the world which we live in) and pied-piping (the world in which we live) in finite WH-relative clauses in twelve varieties of English. In the light of previous studies, it assumes that the strength of processing constraints and formality effects that drive speakers’ constructional choices should correlate with Dynamic Model stages (Schneider 2007). However, drawing on data from the International Corpus of English (ICE) and using mixed-effects logistic regression analysis, the study shows that processing factors affect speakers of all Dynamic Model stages in a very similar way. At the same time, clear differences between variety stages are observed with respect to formality and topic, which strongly affect Phases IV and V but not Phase III. These results are interpreted from a Usage-based Construction Grammar perspective.

A walking robot consisting of double Stewarts parallel legs was designed by our research team in the past time, which was mainly used for the transportation of the wounded after the disaster. In order to promote stability of control locomotion and ensure invariably horizontal state of the robot platform in the process of motion, the central pattern generator (CPG) based on particle swarm optimization (PSO) is presented to optimize the kinematic model. The purpose of optimization is to solve the hysteresis problem of displacement variation among the electric cylinders. Moreover, the dynamic model of the robot is established, which can provide mechanical basis for the feedback of control signal and make the robot move stably. The simulation results show that the displacement hysteresis problem of the electric cylinders is solved well. Meanwhile, compared with simulation results based on GA-CPG method, it is demonstrated that the robot motion planned using PSO-CPG method has better motion stability and can avoid the impact of legs landing during the transition phase of the motion cycle. The experimental results show that the platform on the robot can maintain an invariably horizontal state, and the locomotion is more stable. It verifies the feasibility of PSO-CPG model and the correctness of the dynamic model of the parallel mobile rescue robot.

We use a dynamic model of the U.S. apple industry separated into organic and conventional production to better measure the impacts of pest or disease outbreaks on producers and consumers, along with an equilibrium displacement model to simulate welfare effects from various shocks compared to a baseline. Our results show that the impacts of the outbreaks differ between organic and conventional production methods. Growers’ and consumers’ responses to shocks differ widely across the industry. Farmers and policy makers should use these findings to appropriately respond to different shocks and production methods.

Spherical robots (SRs) have the characteristics of nonholonomic constraints, underactuation, nonchain, and strong coupling, which increase the difficulty of modeling and motion control compared with traditional robots. In this study, we develop an adaptive motion control scheme for a nonholonomic SR, in which an omnidirectional dynamic model is carried out by using the Euler–Lagrange method to describe the omnidirectional motion of the SR more accurately. Furthermore, to facilitate the design of the motion controller, the dynamic model is simplified to obtain the state space expression of the SR. Aiming at the problem of poor control effect caused by the change of system model parameters which are influenced by dynamic model reduction, an adaptive motion control law of SR is designed based on MRAC. And the coefficient adjustment of the controller is obtained by the Lyapunov method, with the guaranteed stability of the closed-loop system. Finally, the controller designed in this thesis is compared with four controllers including linear quadratic regulator, Fuzzy PID, PSO-ADRC, and hierarchical SMC. The experimental comparison proves that the control scheme proposed in this study still has good control ability when the motion parameters are disturbed.

The principal objective of the paper is to show the importance of the Hamiltonian in control theory. Instead of using the Lagrangian formulation of electromechanical or robotic systems, our work is focused on robot dynamics by its Hamiltonian. Using the iterative Newton–Euler, we generate the local Hamiltonians and the derivative of the moments at each joint of the robot manipulator. Thus, we can apply decentralized controllers at each joint. We compare and discuss the efficiency of the controllers. We show that the performance of the sliding modes controller is more robust than that of the PD or Bang–Bang controllers.

This article presents a methodology to reduce the energy consumption of an industrial robot. We propose a design for a 3R serial manipulator of general geometry. We show an analytical model aiming to analyze the search space of architectures based on the torsion angles of the robot to determine the optimal architecture that allows the efficient use of energy. The analytical model provides a theoretical estimation of the energy consumption and is validated by monitoring the experimental robot. The numerical calculations obtained with a particular case reduced the energy consumption by approximately 7.5%.

Family feasting during the Spring Festival is a Chinese tradition. However, close contact during this period is likely to promote the spread of coronavirus disease 2019 (COVID-19). This study developed a dynamic infectious disease model in which the feast gatherings of families were considered the sole mode of transmission. The model simulates COVID-19 transmission via family feast gatherings through a social contact network. First, a kinship-based, virtual social contact network was constructed, with nodes representing families and connections representing kinships. Families in kinship with each other comprised of the largest globally coupled network, also known as a clique, in which a feast gathering was generated by randomly selecting two or more families willing to gather. The social contact network in the model comprised of 215 cliques formed among 608 families with 1517 family members. The modelling results indicated that when there is only one patient on day 0, the number of new infections will reach a peak on day 29, and almost all families and their members in the social contact network will be infected by day 60. This study demonstrated that COVID-19 can spread rapidly through continuous feast gatherings through social contact networks and that the disease will run rampant throughout the network.

The traditional identification methods of industrial robots are based on Inverse Dynamic Identification Model (IDIM). Based on the model, input torque, motor-side and link-side motion data are necessary when joint flexibilities are considered. However, it is often unavailable or expensive to general robots which are not equipped with link-side sensors. To solve the problem, a novel dynamic parameter identification method, which only employ input torque and motor-side motion data, is proposed in this report. Based on motor-side dynamics, link-side dynamics are modified as high-order nonlinear functions of input torque and motor-side motion. Then, through different trajectory-load groups and high-order observers, the nonlinear equations can be solved, and dynamic parameters can be estimated with short operation time. The selection rules of the trajectory-load groups are then discussed based on simulation results, so as to promote estimation results. Finally, experiments are conducted to verify the proposed method and exhibit the selection rules of observer gains. As shown in the report, except viscous friction parameters, identification difference between the IDM-based methods and the proposed one is less than 9%.

Glyphosate-tolerant and glyphosate-resistant weeds are becoming increasingly problematic in cotton fields in Australia, necessitating a return from a glyphosate dominated system to a more integrated approach to weed management. The development of an integrated weed management system can be facilitated by identifying the critical period for weed control (CPWC), a model that enables cotton growers to optimize the timing of their weed control inputs. Using data from field studies conducted from 2003 to 2015, CPWC models using extended functions, including weed biomass in the relationships, were developed for the mimic weeds, common sunflower and Japanese millet, in high-yielding, fully irrigated cotton. A multispecies CPWC model was developed after combining these data sets with data for mungbean in irrigated cotton, using weed height and weed biomass as descriptors in the models. Comparison of observed and predicted relative cotton-lint yields from the multispecies CPWC model demonstrated that the model reasonably described the competition from these three very different mimic weeds, opening the possibility for cotton growers to use a multispecies CPWC model in their production systems.

In the present work, the ankle rehabilitation robot (ARR) dynamic model that implements a new series of connection control strategies is introduced. The dynamic models are presented in this regard. This model analyzes the robot LuGre friction model and the nonlinear disturbance model. To improve the ARR system’s rapidity and robustness, a composite 2-degree of freedom (2-DOF) internal model control (IMC) controller is presented. The control performance of the compound 2-DOF IMC controller is simulated and analyzed in the present work. The simulation shows that the composite 2-DOF IMC controller has high following performance. For practical testing purposes, 1-DOF passive training and predetermined trajectory following have been completed for different swing amplitudes and frequencies. Moreover, the thrust and tension torque of the robotic dynamic and static loading characteristics are studied in active control mode. The experimental results show the effectiveness of passive training of the given trajectory and impedance training active control strategy. This paper gives the specific functions of ARR.