Model Predictive Control with Reach-avoid Analysis
Authors: Dejin Ren, Wanli Lu, Jidong Lv, Lijun Zhang, Bai Xue
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we evaluate the proposed method and make comparisons with state-of-the-art ones based on several examples. All the experiments were run on MATLAB 2022b with CPU 12th Gen Intel(R) Core(TM) i9-12900K and RAM 64 GB. |
| Researcher Affiliation | Academia | 1State Key Lab. Computer Science, Institute of Software, CAS, Beijing, China 2University of Chinese Academy of Sciences, Beijing, China 3National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing, China |
| Pseudocode | Yes | Algorithm 1 The framework for solving optimization (4). and Algorithm 2 The RAMPC algorithm for solving (4). |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating the public availability of its source code. |
| Open Datasets | No | The paper evaluates its method on several synthetic examples, such as a drone system and a Van der Pol oscillator, which are defined within the paper by their system dynamics. These are not publicly available datasets for which access information (link, DOI, citation) is provided. |
| Dataset Splits | No | The paper uses synthetic dynamic systems for evaluation and does not involve traditional datasets with predefined training, validation, and test splits. Therefore, no specific dataset split information is provided. |
| Hardware Specification | Yes | All the experiments were run on MATLAB 2022b with CPU 12th Gen Intel(R) Core(TM) i9-12900K and RAM 64 GB. |
| Software Dependencies | No | All the experiments were run on MATLAB 2022b with CPU 12th Gen Intel(R) Core(TM) i9-12900K and RAM 64 GB. Constraint (5) is solved by encoding it into sum-of-squares constraints which is treated by the semi-definite programming solver MOSEK; the nonlinear programming (7) and the mixedinteger nonlinear programming in the LMPC algorithm in [Rosolia and Borrelli, 2017] are solved using YALMIP [Lofberg, 2004]. The paper lists MATLAB with a version, but does not specify versions for MOSEK or YALMIP. |
| Experiment Setup | Yes | The configuration parameters in Alg. 2 for all examples are shown in Table 1. Table 1 lists specific values for parameters such as λ, M, N, K, ξ, δ, ϵ for each example. |