The rise of vehicle usage causes roads to reach capacity limits. When capacity is reached, traffic jams and accidents are more likely to occur. By raising the efficiency of traffic, the capacity of roads can benefit without the need to expand infrastructure. Automating longitudinal controls of vehicles and creating vehicle platoons show potential to raise traffic efficiency. A platoon consists of multiple vehicles travelling closely behind each other. Automating the longitudinal controls shows promise to decrease distances between these vehicles, while maintaining safety and raising traffic throughput. This smaller distance is below that of conventional human control, thus improving traffic density. Most modern cars already have the option of being equipped with longitudinal control. The most advanced being Adaptive Cruise Control (ACC) which can either hold a predefined velocity or adapt that velocity to maintain distance to a preceding vehicle. However, due to the limitation of using only on-board sensors, these systems do not guarantee safety or increase traffic throughput. Research is being done on expanding ACC with Vehicle to Vehicle (V2V) communication creating Cooperative Adaptive Cruise Control (CACC). The addition of communication provides more precise data of neighbouring vehicles than conventional on-board sensing can provide and allows for vehicles to communicate intent. Optimization control for CACC systems has been researched, but it rarely incorporates passengers comfort or the future intent of the preceding vehicles. The goal of this thesis is to implement a decentralized optimization algorithm on a real-time simulated platoon of three vehicles, which takes into consideration safety, propagation of errors through the platoon, and comfort. These three aspects are subsequently implemented and evaluated on a real-time simulation platform in a distributed fashion.

With the decrease in sensor and actuator costs decentralized control strategies have become increasingly attractive, aiming to use multiple simpler robots for achieving a global objective. The problem of reaching the global objective generally results in a consensus problem requiring communication amongst the agents. The cooperative manipulation problem, where a payload is manipulated using multiple robots, poses an attractive alternative: By using the payload's motion as the means of communication, the agents can reach consensus without using explicit communication. The advantage being that no additional bandwidth is required as the number of participating agents increases and all to all communication is effectively achieved.

Whereas previous works considered only the translation dynamics this thesis work considers the use of the full rigid body motion as a means of communication, such that the agents reach consensus on the desired wrench and the payload is stabilized at any desired configuration. As a possible application the towing of a payload by multiple UAV via cables is considered. This brings the additional challenge of underactuation from the perspective of each agent, since only forces can be used to control the full payload's motion.

The result is a decentralized nonlinear control law for the forces applied to a payload such that consensus is reached amongst the agents, the leader's control action is amplified and the payload is stabilized at any desired configuration. Proofs are constructed via Lyapunov arguments and the applicability of the control design to the aerial towing problem is validated in simulation.

The capacity of public roads has become a serious problem all over the world. The traffic is constantly increasing, while the capacity remains almost the same. This causes traffic jams and road accidents. An effective way to increase the road throughput employs vehicular platoons, which allow to decrease inter-vehicle distances without compromising road safety.

The urgent necessity to increase road capacity has lead to the substantial interest in Cooperative Adaptive Cruise Control (CACC), which uses measurements of on-board sensors and inter-vehicle communication to provide safe platooning. Numerous theoretical works and extensive experiments have proved that the possibility to exchange certain parameters via wireless communication allows for a significant decrease of the inter-vehicle distance.

Cooperative Adaptive Cruise Control systems should comply with several requirements. One of the requirements is so-called string stability, which prevents amplification of disturbances, propagating along the string of vehicles. To simplify the design of CACC, vehicles in a platoon are often assumed to have identical and fully known dynamics. These assumptions in practice are too restrictive. The heterogeneity of vehicles increases the complexity of CACC controller design problem.

In this master thesis a decentralized CACC algorithm is implemented based on continuous sliding-mode control and adaptation laws, which estimate uncertain vehicle’s parameters. Evaluation of Cooperative Adaptive Cruise Control algorithm has been conducted on a vehicle simulator DYNACAR.