## 4 Key Enabling Technologies
### Multi-Machine Cooperative Scheduling Based on IHHO
Strategy-Based Adaptive Agricultural Machinery Path Tracking Predictive Control Method Based on Multi-Objective Optimization  
The agricultural machinery path tracking system is a key technology in intelligent farming, directly impacting the precision and stability of unmanned operations. This study proposes an adaptive predictive path tracking control method based on multi-objective optimization, utilizing an Improved Arctic Puffin Optimization (APO) algorithm to fine-tune the parameters of MPC [46]. Additionally, an event-triggered warm-start technique is introduced to enhance tracking accuracy, smoothness, and disturbance resistance. As shown in Figure 10, the system integrates key components such as an electric steering wheel, inertial measurement unit, global navigation satellite system, camera, and 5G data transfer unit. It also adopts the ISO 11783 standard for in-vehicle network protocol conversion, enabling multi-device collaborative control to improve the path-tracking performance and operational stability of agricultural machinery [47]. The core objective of the path tracking task is to minimize the deviation between the agricultural machinery's actual trajectory and the target path. In this study, a nonlinear kinematic model of agricultural machinery is used to describe the vehicle's dynamic characteristics, forming the foundation for an MPC control framework. The state variables include lateral error, heading error, and velocity state, while the control variables consist of the front wheel steering angle and velocity increment. The constraints consider factors such as the maximum steering angle, minimum turning radius, and operational speed limits. The optimization objectives are defined as follows:  
\[minJ=\sum_{k=0}^{N_{p}}\left(Qe_{k}^{2}+Ru_{k}^{2}\right) \tag{7}\]  
where \(Np\) represents the prediction horizon and \(k\) denotes the index of the update sequence. \(e_{k}\) is the state error at step \(k\), and \(u_{k}\) is the control input at step \(k\). \(Q\) and \(R\) are the weight matrices for the state error and control input, respectively. To improve the adaptive adjustment of MPC parameters, this study utilizes an improved APO algorithm to optimize the prediction horizon, control horizon, and weight matrices of the MPC framework. The optimization strategy involves the following steps:  
Figure 10: Experimental agricultural machinery platform. 1. Population initialization: Latin Hypercube Sampling (LHS) is employed to ensure a uniform distribution across the search space [48]. 2. Adaptive search mechanism: Levy flight is integrated to enhance global exploration, coupled with a velocity factor for dynamically adjusting the optimization direction [49]. 3. Strategy-based early stopping mechanism: Convergence is halted early based on fitness memory and a dynamic threshold. The APO algorithm follows these steps: First, the population size and search space are defined, and LHS is employed for parameter initialization. Then, Levy flight and velocity adjustment strategies dynamically update the optimal solution. Based on the fitness trend, an early stopping mechanism is applied to minimize redundant computations. Finally, the optimal parameters are selected to fine-tune the MPC controller. In the MPC optimization process, in addition to minimizing tracking errors, it is essential to ensure path smoothness and disturbance resistance. Therefore, a multi-objective optimization strategy is introduced:  
Here, \(\Delta\delta_{f}\) denotes the variation in the front wheel steering angle, which is minimized to reduce oscillations and improve path smoothness. Equation (8) aims to optimize path smoothness by minimizing the rate of change in the steering angle. \[min\ J_{1}=\sum_{k=0}^{N_{p}}\left(\Delta\delta_{f}(k)\right)^{2} \tag{8}\]  
Noise resistance optimization: implement adaptive compensation for sensor errors and environmental disturbances to enhance system robustness.