Abstract
This paper reviews model predictive control (MPC) and its wide applications to both single and multiple autonomous ground vehicles (AGVs). On one hand, MPC is a well-established optimal control method, which uses the predicted future information to optimize the control actions while explicitly considering constraints. On the other hand, AGVs are able to make forecasts and adapt their decisions in uncertain environments. Therefore, because of the nature of MPC and the requirements of AGVs, it is intuitive to apply MPC algorithms to AGVs. AGVs are interesting not only for considering them alone, which requires centralized control approaches, but also as groups of AGVs that interact and communicate with each other and have their own controller onboard. This calls for distributed control solutions. First, a short introduction into the basic theoretical background of centralized and distributed MPC is given. Then, it comprehensively reviews MPC applications for both single and multiple AGVs. Finally, the paper highlights existing issues and future research directions, which will promote the development of MPC schemes with high performance in AGVs.
Highlights
Autonomous intelligent systems (AISs) are a series of systems that act or react to the environment independently of human control, e.g., autonomous ground vehicles (AGVs) or unmanned aerial vehicles (UAVs)
The related model predictive control (MPC) algorithms in autonomous ground vehicles are reviewed when operating as individuals and as multiple systems
Some open challenges are highlighted for future research to improve the performance of MPC for AGVs
Summary
Autonomous intelligent systems (AISs) are a series of systems that act or react to the environment independently of human control, e.g., autonomous ground vehicles (AGVs) or unmanned aerial vehicles (UAVs). The design of MPC schemes is concerned with stabilizing a set-point of (1) as the control objective For this reason, the stage cost is usually chosen to be positive definite with respect to this set-point. The stage cost is usually chosen to be positive definite with respect to this set-point Even in this case, additional assumptions are, in general, needed to establish closed-loop stability. A centralized controller, e.g., as shown, may not be desirable for these systems, due to a large required communication overhead, it being susceptible to singlepoint failure, or because the centralized MPC problem (2) is computationally intractable In these cases, the development of suitable distributed MPC schemes is of interest
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