Abstract: Dynamic speed can often cause the fluctuations of unmanned agricultural machines in farmland slopes. Agricultural operations can be significantly impacted, such as seeding and fertilizer application. The stability of planting and the precision of fertilizer application can depend mainly on the driving speed of driverless tractors in undulating farmland. Particularly, the absence of visual cues from the driver can lead to inevitable pre-control of the throttle when tractors operating. In this study, the robust control system of steady speed was proposed using the Digital Elevation Model (DEM). An advanced feed-forward control was also utilized at the longitudinal slopes. A systematic investigation was then made to assess the influence of farmland slope on the tractor speed. The occurrence of slope acceleration was induced by gravity during both uphill and downhill maneuvers in the tractor driving. The new control of the tractor was also used to predict the terrain slope ahead. Consequently, the speed of the tractor was adjusted for the target speed, as the terrain slope increased. An extended duration was avoided to reach the preset velocity. Alternatively, the speed readily surpassed the target speed on the decreasing slopes, resulting in frequent braking and adverse effects on the dynamics of vehicles. DEM data was utilized to extract elevation information from the terrain. The longitudinal slope feed-forward control was also introduced to dynamically obtain the pre-scanning distance. The pre-scanning time was configured to integrate with the current driving speed. At the same time, the slope value of the subsequent driving path was calculated to determine the compensation of speed on the slope. Ultimately, effective control of the tractor's speed was achieved to adjust the target speed in real time. The optimal performance and stability were obtained across diverse terrain gradients. DF1204 CVT tractor was taken as a test platform. Three tests were conducted with different target speeds for uphill, flat, and downhill at the Yantai Research Institute of China Agricultural University. The test results showed that when the tractor was traveling at the target speeds of 4, 6, and 8 km/h, the mean values of the measured speeds were 4.03, 5.94, and 7.85 km/h for uphill driving, 4.04, 6.02, and 8.03 km/h for level driving, and 4.00, 6.10, and 8.19 km/h for downhill driving, respectively. The speeds closely approached the target values, indicating the efficacy of the control system to regulate the tractor speeds. The speed accuracy was also improved with the mean speeds of the experimental group notably closer to the target speeds for both uphill and downhill driving scenarios. Moreover, the root mean square errors (RMSE) were reduced under all driving conditions, with decreases of 46.63%, 21.92%, and 37.15% for uphill, level ground, and downhill driving, respectively, compared with the control group. The speed stability was enhanced to reduce the speed fluctuations, thereby improving the overall tractor performance and operational efficiency in agricultural settings. The feedforward control of longitudinal gradient speed also surpassed the original, in terms of the speed stability and path tracking. The steady speeds with high precision were maintained in the autonomous agricultural machinery under undulating farmland environments. The tractor efficiency was then promoted to reduce the operational fluctuations in practical agricultural operations. The optimal speeds can be used to realize smoother and more productive farming in the various tasks of streamlining, such as planting and fertilization. Moreover, the operational disruptions can be minimized to maintain high-performance standards. The finding can greatly contribute to the overall quality and reliability of agricultural machine operations. Technical support can also be offered to improve agricultural productivity and sustainability in sustainable production.