TY - GEN
T1 - A hybrid model predictive controller for path planning and path following
AU - Zhang, Kun
AU - Sprinkle, Jonathan
AU - Sanfelice, Ricardo G.
PY - 2015/4/14
Y1 - 2015/4/14
N2 - The use of nonlinear model-predictive methods for path planning and following has the advantage of concurrently solving problems of obstacle avoidance, feasible trajectory selection, and trajectory following, while obeying constraints 011 control inputs and state values. However, such approaches are computationally intensive, and may not be guaranteed to return a result in bounded time when performing a non- convex optimization. This problem is an interesting application to cyber-physical systems due to their reliance 011 computation to carry out complex control. The computa-tional burden can be addressed through model reduction, at a cost of potential (bounded) model error over the prediction horizon. In this paper we introduce a metric called uncontrollable divergence, and discuss how the selection of the model to use for the predictive controller can be addressed by evaluating this metric, which reveals the divergence between predicted and true states caused by return time and model mismatch. A map of uncontrollable divergence plotted over the state space gives the criterion to judge where reduced models can be tolerated when high update rate is preferred (e.g. at high speed and small steering angles), and where high-fidelity models are required to avoid obstacles or make tighter curves (e.g. at large steering angles). With this metric, we design a hybrid controller that switches at runtime between predictive controllers in which respective models are deployed.
AB - The use of nonlinear model-predictive methods for path planning and following has the advantage of concurrently solving problems of obstacle avoidance, feasible trajectory selection, and trajectory following, while obeying constraints 011 control inputs and state values. However, such approaches are computationally intensive, and may not be guaranteed to return a result in bounded time when performing a non- convex optimization. This problem is an interesting application to cyber-physical systems due to their reliance 011 computation to carry out complex control. The computa-tional burden can be addressed through model reduction, at a cost of potential (bounded) model error over the prediction horizon. In this paper we introduce a metric called uncontrollable divergence, and discuss how the selection of the model to use for the predictive controller can be addressed by evaluating this metric, which reveals the divergence between predicted and true states caused by return time and model mismatch. A map of uncontrollable divergence plotted over the state space gives the criterion to judge where reduced models can be tolerated when high update rate is preferred (e.g. at high speed and small steering angles), and where high-fidelity models are required to avoid obstacles or make tighter curves (e.g. at large steering angles). With this metric, we design a hybrid controller that switches at runtime between predictive controllers in which respective models are deployed.
KW - Hybrid control
KW - MPC
KW - Model error evaluation
UR - http://www.scopus.com/inward/record.url?scp=84944514646&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84944514646&partnerID=8YFLogxK
U2 - 10.1145/2735960.2735966
DO - 10.1145/2735960.2735966
M3 - Conference contribution
AN - SCOPUS:84944514646
T3 - ACM/IEEE 6th International Conference on Cyber-Physical Systems, ICCPS 2015
SP - 139
EP - 148
BT - ACM/IEEE 6th International Conference on Cyber-Physical Systems, ICCPS 2015
PB - Association for Computing Machinery, Inc
T2 - 6th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2015
Y2 - 14 April 2015 through 16 April 2015
ER -