PidIpdtBatchRolloutScore【免费下载链接】mat-chem-sim-pred面向工业领域聚焦计算仿真、预测两大核心场景构建面向流程工业机理数据双轮驱动的领域计算层推动AI for Science在材料化学领域的深度应用。项目地址: https://gitcode.com/cann/mat-chem-sim-predOverviewPidIpdtBatchRolloutScoreis an independent IPDT batch closed-loop rollout operator for PID candidate scoring. It evaluates a batch of IPDT process models against a shared PID candidate set on NPU and returns the best candidate per loop.This operator is used in the tuning stage as the IPDT candidate simulation and selection kernel:input is process model parameters plus PID candidate arraysoutput is per-loop best score and best PID gainsimplementation is independent from the earlier model-fit operatorsThe plant dynamics are a pure integrator plus dead time:y[k1] y[k] b * u[k - delay]This is thea 1(no self-regulation) special case of the FOPDT rollout, so there is noacoefficient input. The PID control law, scoring, candidate-axis SIMD, delay ring and tiling are identical toPidFopdtBatchRolloutScore.工程语义IPDT rollout 与 FOPDT/SOPDT rollout 的候选评估框架相同输入已辨识出的模型参数和一批 PID 候选kernel 内部闭环递推累计IAE/ISE/overshoot/settling_time/control_energy计算固定 score 并选出每条回路的最优候选。区别只在被控对象动态。IPDT 是积分对象y[k1] y[k] b * u[k - delay]它没有 FOPDT 的自回归衰减项a*y[k]因此过程本身没有自稳能力输入作用会持续累积到输出上。对于液位、库存、积分型热量累积等对象这类模型比一阶自稳模型更贴近。PID 候选不由本算子生成本算子只接收kp[M]、ki[M]、kd[M]并评估。候选可以来自整定规则、规则附近扰动、人工网格或外部优化器。当前 batch rollout 已经融合了候选特征、候选评分和候选选优如果使用本算子直接输出best_result/best_idx通常不需要再接独立候选评分/选优算子。pid_step_response_features仅用于需要保留完整候选轨迹特征表的另一条模块化链路。Current status:correctness is validated against the in-process CPU reference (thebenchmarkprogram), max quality rel-err 1e-3the rollout is a serial time recurrence; the candidate axis is evaluated with a wide vector SIMD lane (kLane768) so the inner loop is throughput-bound rather than latency-boundSee benchmark report for the measured results.Inputs And OutputsTensorDtypeShapeMeaningbfloat32[B]per-step integration gain (y[k1] y[k] b*u[k-delay])delayint32[B]input delay, clamped to0..31in kernely0float32[B]initial outputspfloat32[B]setpointkpfloat32[M]PID candidate Kpkifloat32[M]PID candidate Kikdfloat32[M]PID candidate Kdbest_resultfloat32[B, 8]best candidate metrics per loopbest_idxint32[B]best candidate index per loopbest_resultlayout:best_score,best_kp,best_ki,best_kd,best_iae,best_ise,best_overshoot,best_settling_timeBuildcd prediction/ProcessControl/PIDModelFit/pid_ipdt_batch_rollout_score cmake -S . -B build -DCMAKE_BUILD_TYPERelease -DSOC_VERSIONAscend910B3 cmake --build build -j$(nproc)Note:this project now defaults toReleaseifCMAKE_BUILD_TYPEis not specifiedonnode202, runtime typically needs:export LD_LIBRARY_PATH/usr/local/Ascend/ascend-toolkit/latest/lib64:$PWD/build:$PWD/build/lib:${LD_LIBRARY_PATH}TestPython reference test:python tests/test_pid_ipdt_batch_rollout_score.pyNPU smoke:./build/test_aclnn_pid_ipdt_batch_rollout_score 0NPU / CPU benchmark:./build/benchmark_pid_ipdt_batch_rollout_score 0 64 1024 1024 0 2 0 64 # candidate_tile0 auto (min(C, kLane768))DocumentsAlgorithmAPI ReferenceBenchmark Report【免费下载链接】mat-chem-sim-pred面向工业领域聚焦计算仿真、预测两大核心场景构建面向流程工业机理数据双轮驱动的领域计算层推动AI for Science在材料化学领域的深度应用。项目地址: https://gitcode.com/cann/mat-chem-sim-pred创作声明：本文部分内容由AI辅助生成（AIGC），仅供参考