The development of robot technology has received increased interest in three-wheeled mobile robots (TWMR) due to their flexibility and adaptability in various applications. However, due to the limitation of independent motion in all directions, highly nonlinear dynamics, and effects such as noise and wheel slippage, it is difficult to effectively control the motion of TWMR. In this paper, we studied the motion of TWMR through kinematic and dynamic models considering the phenomenon of wheel slippage. The lateral and longitudinal slippage components were added to the dynamic model, thereby designing the position control law using the backstepping method and the speed control law using the SMC method. We also adjusted the parameters of the controllers to help the robot perform trajectory tracking. In addition, controlling the input torque was performed to overcome the lateral and longitudinal slippage phenomena. The method was tested through simulations on MATLAB Simulink with different trajectory cases (circular trajectory, infinite trajectory, spiral trajectory). The results showed the suitability of the controller to the desired trajectory tracking problem of TWMR, and the trajectory tracking error converged to zero. This shows promise and is practical.
Vehicle state estimation accuracy in the Adaptive Unscented Kalman Filter (AUKF) is highly dependent on the proper tuning of the process and measurement noise covariance matrices. Conventional optimization techniques often produce suboptimal covariance values, increasing the risk of estimation divergence and compromising vehicle stability. To overcome this limitation, this paper proposes a Modified Dragonfly Optimization Algorithm-based Adaptive Unscented Kalman Filter (MDOA-AUKF), in which adaptive covariance tuning is formulated as an optimization problem. By incorporating elite solution preservation and multi-behavioral swarm dynamics, the proposed MDOA effectively balances exploration and exploitation, thereby enabling adaptive updates of the process (Q) and measurement (R) noise covariance matrices at each iteration. This improves the convergence, estimation accuracy, and robustness under nonlinear driving conditions and varying vehicle mass. The proposed approach is implemented using a two-degree-of-freedom bicycle model considering lateral and yaw dynamics under a low-friction road condition (tireu2013road friction coefficient of 0.4) and evaluated using Fishhook (FH) and Double Lane Change (DLC) maneuvers. During the FH maneuver, the proposed method achieves root mean squared error (RMSE)/ normalized root mean squared error (NRMSE) values of 0.0008/0.21% and 0.0010/0.03% for the sideslip angle and yaw rate, respectively, while the corresponding values during the DLC maneuver are 0.0011/0.24% and 0.0009/0.02%, respectively. Compared with Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Grey Wolf Optimization (GWO), the proposed approach provides significantly lower estimation errors and faster convergence. Robustness is further validated under vehicle mass variations from 1800 kg to 2300 kg, demonstrating consistently low estimation errors. These results confirm that the proposed MDOA-AUKF is an accurate, robust, and computationally efficient framework for intelligent vehicle state estimation.
In the copper smelting Flue Gas Sulfuric Acid, accurate prediction of the converter inlet temperature is of great engineering significance for ensuring safe operation, reducing energy consumption, and improving system stability. To address the strong coupling, long time delay, and pronounced nonlinearity inherent in this process, we proposed a data-driven soft-sensing method designed for real-world industrial applications. The proposed approach was validated in an actual "two-conversionu2013two-absorption" industrial system. First, the Partial Autocorrelation Function was employed to analyze Distributed Control System data, identifying the most relevant input variables and time windows to provide a high-correlation feature foundation for model construction. Then, a Gated Recurrent Unit neural network was utilized to model temporal dependencies, effectively capturing complex dynamic relationships while suppressing noise interference. Furthermore, an Attention Mechanism was incorporated to enhance the model's focus on key temporal features, improving adaptability to process fluctuations and enhancing the interpretability of prediction results. Finally, the proposed model was implemented and verified in an industrial Flue Gas Sulfuric Acid process. The experimental results demonstrated that the method maintains stable predictive performance and accurately captures the dynamic variation of the first-layer converter inlet temperature. It effectively assists operators in adjusting process parameters in real time, thereby improving system safety, continuity, and operational reliability. The findings verify the engineering feasibility and practical applicability of the proposed method, providing strong technical support for the intelligent operation of copper smelting sulfuric acid production systems.
