No abstract

Smart agriculture represents a significant trend in agricultural development, given its potential to enhance operational efficiency and reduce labor intensity. Despite the adoption of modern greenhouse technologies, such as sensors and automation systems, crop transportation is still largely achieved through manual labor, largely due to the complex environment and narrow terrain of greenhouses. To address this challenge, this work proposes the design of an intelligent vehicle that is capable of transporting crops in a commercial greenhouse, with the aim of improving operational efficiency and reducing labor intensity. To enable the vehicle to navigate the horizontal and rail surfaces within the greenhouse, a novel chassis structure is designed that is capable of simultaneous driving on both ground and rail surfaces. Additionally, the two-dimensional codes is adopted for positioning and navigation, thereby avoiding the need to modify existing greenhouse road surfaces. Through the implementation of a comprehensive system-control strategy, the intelligent vehicle realized various functions, including ground driving, rail driving, moving up and down the rail, and automatic rail changing. Experimental results demonstrate that the designed intelligent vehicle successfully meets the basic requirements for crop transportation in a greenhouse, providing a solid foundation for future unmanned operations.

The steering mechanism of ship steering gear is generally driven by a hydraulic system. The precise control of the hydraulic cylinder in the steering mechanism can be achieved by the target rudder angle. However, hydraulic systems are often described as nonlinear systems with uncertainties. Since the system parameters are uncertain and system performances are influenced by disturbances and noises, the robustness cannot be satisfied by approximating the nonlinear theory by a linear theory. In this paper, a learning-based model predictive controller (LB-MPC) is designed for the position control of an electro-hydraulic cylinder system. In order to reduce the influence of uncertainty of the hydraulic system caused by the model mismatch, the Gaussian process (GP) is adopted, and also the real-time input and output data are used to improve the model. A comparative simulation of GP-MPC and MPC is performed assuming that the interference and uncertainty terms are bounded. Consequently, the proposed control strategy can effectively improve the piston position quickly and precisely with multiple constraint conditions.

Model mismatch is inevitable in robot control due to the presence of unknown dynamics and unknown perturbations. Traditional model predictive control algorithms are usually based on constant value assumptions and are not able to overcome the degradation of controller performance due to model mismatch. In this paper, a model predictive control (MPC) algorithm based on Gaussian process regression (GPR) is proposed. Firstly, the kinematic equations of the mobile robot are established by the mechanistic analysis method; similarly, the dynamics of the mobile robot system are modeled using the second-class Lagrangian equations. Secondly, the problem of stability and reliability degradation due to model mismatch during the operation of mobile robot is considered. This paper uses a MPC algorithm with a main model plus residual model to solve the MPC closed-loop control strategy. The state at each moment is decomposed into a predicted state based on the first-principles model and a residual state. The residual state is learned by GPR in real-time and used to compensate for deviations between the real process model and the predicted model. The proposed method requires fewer data samples, enhancing the technique’s practicality. Finally, the simulation results show that the proposed algorithm is more stable and achieves the desired tracking faster. Compared with the MPC algorithm, the arrival time of the system is reduced by 28% and the speed error is controlled within 0.07.