Abstract
The SPMs developed in Chaps. 4–6, can be applied to solve trajectory planning problems with different features. They are actually open-loop control techniques, where control commands are computed based on the given model under ideal conditions. Model predictive control (MPC) owns the capability to handle constraints. At each sampling instant, an optimization problem is required to be solved. It could be extremely time-consuming when the optimization problem is not well formulated or subjected to too many complex constraints. Hence, the bottleneck that limits the application of MPC in practice is its online computational efficiency. In this chapter, we will take the SPM developed in Chap. 5 as the core solver to construct the symplectic pseudospectral MPC.
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Wang, X., Liu, J., Peng, H. (2021). Model Predictive Control: From Open-Loop to Closed-Loop. In: Symplectic Pseudospectral Methods for Optimal Control. Intelligent Systems, Control and Automation: Science and Engineering, vol 97. Springer, Singapore. https://doi.org/10.1007/978-981-15-3438-6_7
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DOI: https://doi.org/10.1007/978-981-15-3438-6_7
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