In this paper, the least squares method is used to determine the vertical height of the road space domain. Based on the road estimation method, the active suspension control mode switching model is constructed, and the dynamic characteristics of the vehicle in comfort, safety, and integrated modes are analyzed. The vibration signal is collected by the sensor, and the parameters such as vehicle driving conditions are solved for in reverse. A control strategy for multiple mode switching under different road surfaces and speeds is constructed. At the same time, the particle swarm optimization algorithm (PSO) is used to optimize the weight coefficients of LQR control under different modes, and the dynamic performance of vehicle driving is comprehensively analyzed. The test and simulation results show that the road estimation results under different speeds in the same road section are very close to the results obtained by the detection ruler method, and the overall error is less than 2%. Compared with the active suspension controlled by passive and traditional LQR, the multi-mode switching strategy can achieve a better balance between driving comfort and handling safety and stability, and also improve the driving experience more intelligently and comprehensively.

The safety, comfort, and energy feedback of active suspension in a single control mode is mutually restricted. To meet the needs of drivers and passengers for vehicle driving performance under different road conditions, this paper proposes a multi-mode switching control strategy of an intelligent suspension system, aiming at improving the stability and comfort of vehicles under different road conditions. In this paper, the adaptive Kalman filter algorithm with a forgetting factor is used to estimate the road input. The accuracy of the algorithm in estimating the road input is verified by simulation and experiment. The single-double threshold logic judgment method is used to formulate the switching rules between each working mode. In the controller, the PID control of the BP neural network and the LQR control optimized by GA are used to optimize and adjust the vehicle driving performance indexes in different modes, which effectively solves the problem of limited adaptability of suspension control optimization objectives under different road driving conditions. Based on the data from the vehicle acceleration sensor and road condition sensor, the modeling and simulation of the switching control system are carried out. The simulation results show that the designed control system can effectively improve the comprehensive performance of the vehicle under different road driving conditions. Compared with the traditional active suspension, the body acceleration is increased by 26% on the B-grade road surface, which effectively improves the user’s ride comfort. Compared with the traditional active suspension, the tire dynamic displacement is increased by 24% on the D-grade road, which can significantly improve the overall performance of the vehicle and meet the design requirements of the system.

The detached raft automatic frequency isolation system is a complicated system with high exceptionally nonlinear, high electromagnetic, and multi-source vibration modes. However, it generates a statistical method and it is hard to operate the organization. The fuzzy control algorithm, as an astute control method, can give a keen path to the active management of a complicated system of floating rafts. This study uses a system identification approach to construct mathematical models for a floating raft active vibration isolation system with discrete transfer work. The fuzzy model is used in tests and simulations controller is built using two contributions of acceleration and its variation, as well as a single result of control voltage. The control isolation system is a complicated system with many moving parts. A lot of moving parts profoundly nonlinear, high electromagnetic and multi-source vibration modes, generating a statistical method and it is hard to operate the organization. The fuzzy control algorithm, as a smart control method, can give a keen path to the active management of a sophisticated floating raft system. This research uses an identification strategy to construct a floating raft active vibration isolation technology discrete transfer work mathematical models. The fuzzy controller is then put together using two contributions: acceleration and variation, as well as a single outcome of control voltage for simulations and experiments research.

Inspired by the single-blade hyperboloid, a new type of multi-bar shock absorber was designed, which can recover vibration energy. Its principle is to convert the droop reciprocating vibration of the vehicle in the spatial domain into the reciprocating rotational motion in the plane through the trajectory and force characteristics of the single-blade hyperboloid moving along the space. To improve the efficiency of energy regeneration, a mechanical motion filtering mechanism was designed. Through theoretical derivation, the energy regeneration formula of a new type of multi-rod shock absorber was obtained. After simulation analysis and experimental verification, under the input excitation of 1.82 Hz, the maximum instantaneous output voltage can reach 29 V, the maximum excitation current is 0.58 A, and the maximum power is 16.84 W. The efficient recovery and utilization of energy have been achieved, and the ride comfort of the vehicle has been improved.

Vegetable seeders effectively reduce labor intensity and improve seeding uniformity and operation efficiency. However, existing models suffer from limited functionality, inadequate intelligence, and low energy efficiency. Accordingly, an intelligent vegetable seeder integrating furrow opening, seeding, soil covering, and compaction was developed, with key parameters and working principles of the seed-metering device, furrow opener, and soil-covering and compacting device defined. Its intelligent control system optimized seeding monitoring and execution control via the Kalman filter algorithm. A dual power-supply mode (photovoltaic (PV) + storage battery) ensured operation endurance. Using a Box–Behnken design, the seed-metering device parameters were optimized by response surface methodology, with pocket wheel speed, seed-stripping brush installation angle, and pocket count as factors and seeding qualified rate as the index. The results showed that the factors ranked in descending order of influence were the number of pockets, rotational speed of the pocket wheel, and the installation angle of the seed-stripping brush; the theoretically optimal parameters were pocket wheel speed of 45.70 r·min⁻¹, brush installation angle of 34.71°, and 13.84 pockets, corresponding to a model-predicted peak seeding qualified rate of 99.04%. The proposed seeder achieves precision seeding for small-sized pelleted vegetable seeds, whereas its structural configuration requires further optimization for non-pelleted vegetable seeds.