Dge plus the parameter tuning time. The practical weighting matrices and
Dge and the parameter tuning time. The practical weighting matrices and have been additional revised pre-trained datum value from the weighting matrix, it might matrices applied in non-RLMPC for RLMPC, as indicated in Equation (58). The weighting drastically minimize the parameter tuning time. The the operator had been matrices as and Rn were additional revised for Equathat have been tuned bypractical weighting the exact same Qn the simulation case indicated in RLMPC, as indicated in Equation (58). The weighting matrices applied in non-RLMPC that have been tion (53). tuned by the operator have been thethe path tracking resultscase indicated in Equation (53). For situation 1 experiments, very same because the simulation of MPC and RLMPC are shown For scenario 1 tracking errors path tracking final results are indicated in Figure 11. The in Figure ten, and theexperiments, theof MPC and RLMPC of MPC and RLMPC are shown in Figure 10, and theresults have been quiteMPC and RLMPC are indicated in Figure 11. benefits line path tracking tracking errors of equivalent to the aforementioned simulation The line path in Figures five and 6. The human-tuned MPC represented simulation GS-626510 manufacturer outcomes shown shown tracking benefits have been quite related for the aforementioned some oscillation when thein Figures 5 the 6. The human-tuned MPC represented some oscillation error just after the 70th EV reachedand line path. Nonetheless, the RLMPC exhibited a smallerwhen the EV reached the line sample. path. Nonetheless, the RLMPC exhibited a smaller sized error following the 70th sample.Figure 10. Trajectory comparison MPC and RLMPC in situation 1. Figure 10. Trajectory comparison ofof MPC and RLMPC in scenario 1.For the scenario two experiments, the path tracking benefits of MPC and RLMPC are shown in Figure 12, and the tracking errors of MPC and RLMPC are indicated in Figure 13. It was apparent that the RLMPC outperformed the tracking error when compared with the humantuned MPC. To supply a confident and quantitative error evaluation, each of the experiments were performed three occasions for the performance comparison, as indicated in Table 4. Table 4 shows the relative statistical data of averaging the values on the 3 trials. Each in the average RMSEs had been less than 0.three m, and also the Compound 48/80 medchemexpress maximum errors had been less than 0.7 m.Electronics 2021, 10,18 ofThe all round benefits showed that the RLMPC and human-tuned MPC followed precisely the same ronics 2021, 10, x FOR PEER Review trajectory well. However, with well-converged parameters, RLMPC had much better functionality than MPC tuned by humans with regards to maximum error, typical error, normal deviation, and RMSE.Figure 11. Tracking error comparison of MPC and RLMPC in Scenario 1.Figure Tracking error comparison of MPC and Situation in Figure 11.11. Tracking error comparison of MPC and RLMPC inRLMPC1. Scenario 1.For the scenario 2 experiments, the path tracking results of MPC and shown in Figure 12, as well as the tracking errors of MPC and RLMPC are indica 13. It was apparent that the RLMPC outperformed the tracking error com human-tuned MPC. To provide a confident and quantitative error evalu experiments had been performed three times for the performance comparison, a Table 4. Table 4 shows the relative statistical data of averaging the value trials. Each from the average RMSEs have been significantly less than 0.3 m, along with the maximum er than 0.7 m. The all round results showed that the RLMPC and human-tuned M the exact same trajectory well. Having said that, with well-converged parameters, RLM functionality than MPC tuned by humans when it comes to maximum error, a regular deviation, and RMSE.For t.
