Modeling and identification of the hygro-thermal parameters of mechanically-ventilated broiler house using prediction error: Assessment in cold conditions

Indoor air temperature and humidity moisture are of the foremost significance in climate control of broilers houses, and their impacts on poultry health and production depend on accurate control. The main objective of this work is to identify and assess a novel state-space model, to rapidly predict the hygro-thermal behavior of the livestock building. To achieve this analysis, various experimental measurements (e.g., ventilation rate, thermal heating, and air temperature and humidity) of two commercial poultry houses placed in the Mediterranean zone were monitored over cold conditions production cycle. The developed model was estimated and validated against a dataset of 25 days acquired under three different operation ventilation modes (min-ven, power and tunnel modes). Through simulation, the results showed that the predicted model and measured data were achieved a satisfactory accuracy with an averaged coefficients of determination R2 were 0.93 and 0.95, respectively, for the indoor air temperature and humidity models, and a root mean squared error (RMSE) of 0.3213 °C and 0.957 %. Additionally, the predictive model shows satisfying performances for the long horizon prediction with a final prediction error (FPE) equal to 0.084, which will prevent the intensely time-consuming process of getting precise physical parameters in regards the poultry house system.

Slip Estimation for Mars Rover Zhurong Based on Data Drive

China’s Mars rover Zhurong successfully landed on Mars on 15th May 2021, and it is currently conducting an exploration mission on the Red Planet. This paper develops slip estimation models for the Mars rover Zhurong based on the data drive approach. Data were obtained by Zhurong’s validator ground indoor tests, and the test site was equipped with a low-gravity simulation device and simulated Mars soil to simulate the Mars conditions as much as possible. The obtained slip models trained by BP and GA-BP algorithms were applied to estimate Zhurong’s longitudinal (slip_x) and lateral slip (slip_y) on Mars, and the slip estimation values were used to display Zhurong’s actual driving path. The analyzed results prove that the GA-BP slip models perform better than the BP models, and can both be applied for correcting Zhurong’s path. The proposed models have high potential in guiding the path planning and monitoring of the slip for the Mars rover Zhurong.

Calibration and Improvement of an Odometry Model with Dynamic Wheel and Lateral Dynamics Integration

Localization is a key part of an autonomous system, such as a self-driving car. The main sensor for the task is the GNSS, however its limitations can be eliminated only by integrating other methods, for example wheel odometry, which requires a well-calibrated model. This paper proposes a novel wheel odometry model and its calibration. The parameters of the nonlinear dynamic system are estimated with Gauss-Newton regression. Due to only automotive-grade sensors are applied to reach a cost-effective system, the measurement uncertainty highly corrupts the estimation accuracy. The problem is handled with a unique Kalman-filter addition to the iterative loop. The experimental results illustrate that without the proposed improvements, in particular the dynamic wheel assumption and integrated filtering, the model cannot be calibrated precisely. With the well-calibrated odometry, the localization accuracy improves significantly and the system can be used as a cost-effective motion estimation sensor in autonomous functions.

Motion control for a walking companion robot with a novel human–robot interface

A walking companion robot is presented for rehabilitation from dyskinesia of lower limbs in this article. A new human–robot interface (HRI) is designed which adopts one-axis force sensor and potentiometer connector to detect the motion of the user. To accompany in displacement and angle between the user and the robot precisely in real time, the common motions are classified into two elemental motion states. With distinction method of motion states, a classification scheme of motion control is adopted. The mathematical model-based control method is first introduced and the corresponding control systems are built. Due to the unavoidable deviation of the mathematical model-based control method, a force control method is proposed and the corresponding control systems are built. The corresponding simulations demonstrate that the efficiency of the two proposed control methods. The experimental data and paths of robot verify the two control methods and indicate that the force control method can better satisfy the user’s requirements.

Probabilistically valid stochastic extensions of deterministic models for systems with uncertainty

Models capable of capturing and reproducing the variability observed in experimental trials can be valuable for planning and control in the presence of uncertainty. This paper reports on a new data-driven methodology that extends deterministic models to a stochastic regime and offers probabilistic guarantees of model fidelity. From an acceptable deterministic model, a stochastic one is generated, capable of capturing and reproducing uncertain system–environment interactions at given levels of fidelity. The reported approach combines methodological elements from probabilistic model validation and randomized algorithms, to simultaneously quantify the fidelity of a model and tune the distribution of random parameters in the corresponding stochastic extension, in order to reproduce the variability observed experimentally in the physical process of interest. The approach can be applied to an array of physical processes, the models of which may come in different forms, including differential equations; we demonstrate this point by considering examples from the areas of miniature legged robots and aerial vehicles.