My name is Congcong Sun, I am an engineer of Computer Science and Automatic Control. After joining Wageningen University from May of 2021, I have been focusing on Intelligent Control System for optimal, sustainable and autonomous agro-food production.
Intelligent Control System
Intelligent control system is a class of control methods which use various artificial intelligence approaches like neural networks, Bayesian probability, fuzzy logic, reinforcement learning, evolutionary computation and genertic algorithm. Comparing with classical and modern control approach, intelligent control system empowers dynamic learning capacities into control process and explore optimal solutions out of boundaries set by classical and modern control approaches. Such as, intelligent control can based on data, does not always need a good model, intelligent control can explore optimal solutions from a wider state space.
The motivation of using intelligent control is because, comparing with other domains, agricultural and food production is complex full of dynamics, uncertainties and variations. Intelligent control system has potentials to achieve optimal, reliable and robust control. As a complete control system used to involve sensing, modelling, control and planning different aspects, so that my contributions to intelligent control can be elaborated in sensing, modelling, control and planning four different pieces.
As intelligent control is mostly based on data, sufficient and high quality data input is crutial for performance of modelling and control processes. In the sensing part, I am contributing to design and develop optimal sensing systems using green sensors, soft sensing techniques, optimal sensor placement and optimal sensor usage methods, etc. The objective of the sensing system is mainly collecting and providing high quality data for modelling and control applications, in an efficient and sustainable way. In this part, Prof. Eldert van Henten and I have won a 4TU Green Sensors project, where we are planning to develop and apply biodegradable soil sensors for sustainable agriculture.
Besides sensing, intelligent control can also contribute to develop different models, like data-based model, hybrid model using netural networks, etc. My ongoing projects include learning animal behaviors in livestock buildings for better animal robot interaction. This applications is involved in the NWO DurableCase project.
As presented previously, there are different intelligent control methods including fuzzy logic, reinforcement learning and genetic algorithms. In the control section, I am currently working on projects of climate control in greenhouse, vertical farm and plant factories to have efficient crop cultivation; environment control of livestock building for animal welfare and emission mitigation. As well as optimal control of irrigation systems to have efficient usage of water resource, etc.. Reinforcement learning and Genetic algorithms are my most commenly used approaches.
Intelligent control can also contribute to planning. In NWO DurableCase project, I am now working on optimal logistics planning of multi-agent harvesting robots in order to achieve autonomous harvesting, with optimal energy usage, and less soil compaction. In the PDeng Robotic Interactions in Livestock Systems project, I am now exploring optimal design of mission and maneuver planning for collaborative manure removing robots in dairy barns.