MPC OPTIMicro AUTOMOTIVE

汽车底盘控制 Automotive Chassis Control

以统一的实时优化框架，协同多执行器控制，在严苛约束下释放智能底盘的极致性能。 Coordinate multiple actuators within a unified real-time optimization framework to unlock the full performance of the intelligent chassis under demanding constraints.

本页内容On this page 主动悬架Active Suspension 01  实时01  Real-Time 02  内存效率02  Memory Efficiency 03  稳定可靠03  Reliable
Active Suspension

主动悬架 MPC 控制：从单车轮到整车，实现微秒/毫秒级底盘动载荷与舒适性优化 Active Suspension MPC: From a Single Wheel to the Full Vehicle, Optimizing Dynamic Loads and Comfort in Microseconds

任务场景The Task

主动悬架控制是实现智能底盘平顺性与操稳性突破的核心：基于悬架动力学模型与路面输入，实时计算主动执行器输出力，在随机路面冲击与动态工况下同时平衡乘坐舒适性、悬架行程极限与轮胎抓地力。

  • 硬实时：在百 Hz 的底盘控制周期内，毫秒级完成多状态量、长预测步长的整车全局优化。
  • 多目标协调：无需编写复杂的逻辑规则表，在单一优化框架下自动协调“车身加速度（舒适性）”与“轮胎动载荷/悬架动行程（安全性）”的竞争冲突。
  • 物理边界保障：显式处理悬架动行程极限（防打顶/拉底）、执行器饱和上限及变化率约束，严格限定在物理安全范围内。
  • 轻量化部署：纯 C 静态内存实现，无需昂贵工业 SoC，直接运行于量产车规级 MCU / DSP。

Active suspension control is the key to breakthroughs in ride comfort and handling: using a suspension dynamics model and road input, actuator forces are computed in real time to balance ride comfort, suspension travel limits, and tire grip under random road impacts and dynamic maneuvers.

  • Hard real-time: full-vehicle optimization with many states and a long horizon completes within milliseconds inside a hundred-hertz chassis control cycle.
  • Multi-objective coordination: one optimization framework automatically resolves the conflict between body acceleration (comfort) and tire dynamic load / suspension travel (safety), with no rule tables to maintain.
  • Physical limits guaranteed: suspension travel limits, actuator saturation, and rate-of-change constraints are handled explicitly, keeping the system strictly inside its physical safety envelope.
  • Lightweight deployment: pure C with static memory runs directly on production automotive MCUs and DSPs—no expensive industrial SoC required.

Predictive control model

整车主动悬架 MPCFull-Vehicle Suspension MPC

闭环控制 · 每毫秒更新 Closed loop · updated every ms 悬架状态 SUSPENSION STATES z 车身Body 位移position ẋ 车身Body 速度velocity F 动载荷Dynamic load a 车身Body 加速度acceleration Ff Fr MPC ROAD INPUT 路面输入 / 扰动 Road input / disturbance 车身垂向加速度 ↓ Body vertical acceleration ↓ 更舒适 · 更平稳 More comfortable · more stable 未控制 Uncontrolled 原始振动 Raw vibration 主动控制 Active control MPC 响应 MPC response 时间 → Time → MPC PREDICTION 预测未来 N 步 Predict N steps 状态量 State Variables 车身、悬架等状态量 Body and suspension states 控制量 Control Variables 前后主动执行器力 Front and rear actuator forces 车轮 / 扰动 Wheels / Disturbance 前后轮路面输入 Front and rear road inputs 预测步长 Prediction Horizon 预测未来 N 步 Predict the next N steps
01 Real-Time

毫秒级求解，直接嵌进底盘控制环 Millisecond Solve Times, Directly Inside the Chassis Control Loop

在 600 MHz Cortex-A9 处理器上，包含 14 个状态变量、4 个控制变量的整车悬架 MPC 问题，随控制步长（control horizon）增大的最坏求解耗时（worst case）如下：

  • 控制步长 5：最坏求解耗时 0.4 ms
  • 控制步长 10：最坏求解耗时 1.7 ms
  • 控制步长 15：最坏求解耗时 3.3 ms
  • 控制步长 20：最坏求解耗时 6.1 ms

毫秒级解算能力让悬架 MPC 能够直接接入底盘高频控制主环，实现对高频路面冲击的毫秒级主动抑制。

On a 600 MHz Cortex-A9 processor, a full-vehicle suspension MPC problem with 14 states and 4 controls shows the following worst-case solve times as the control horizon grows:

  • control horizon 5: worst case 0.4 ms
  • control horizon 10: worst case 1.7 ms
  • control horizon 15: worst case 3.3 ms
  • control horizon 20: worst case 6.1 ms

Millisecond solve times let suspension MPC run inside the high-frequency chassis control loop and actively suppress road impacts in real time.

最坏求解耗时 vs 控制步长 Worst-case solve time vs. control horizon
86420 0.4 ms1.7 ms3.3 ms6.1 ms 5101520 控制步长 (control horizon) Control horizon ms
02 Memory Efficiency

全静态内存分配，RAM 需求完全确定 Fully Static Memory Allocation with Entirely Deterministic RAM Requirements

求解器完全剔除动态内存分配（Malloc-Free），所有工作区在编译期静态分配：无内存碎片、无分配失败风险，RAM 需求在部署前即可完全确定，便于车规级 MCU 的资源预算与安全认证。

不同控制步长（control horizon）配置下的静态 RAM 需求：

  • 控制步长 5：6 KB
  • 控制步长 10：18 KB
  • 控制步长 15：32 KB
  • 控制步长 20：55 KB

The solver removes dynamic memory allocation entirely (malloc-free); every workspace is statically allocated at compile time. There is no fragmentation and no allocation failure, and the RAM requirement is fully known before deployment, simplifying resource budgeting and safety certification on automotive MCUs.

Static RAM requirement for each control-horizon configuration:

  • control horizon 5: 6 KB
  • control horizon 10: 18 KB
  • control horizon 15: 32 KB
  • control horizon 20: 55 KB
静态 RAM 需求 vs 控制步长 Static RAM requirement vs. control horizon
644832160 6 KB18 KB32 KB55 KB 5101520 控制步长 (control horizon) Control horizon KB
03 Reliability & Infeasibility

数值稳健、约束可松弛、执行时间可预期 Numerically Robust, Relaxable Constraints, Predictable Execution Time

  • 极佳数值稳健性：即使全程使用单精度（float）运算，在低算力 32 位 MCU 上依然保持稳定收敛与足够的解算精度，无需双精度硬件支持。
  • 软约束松弛机制：悬架动行程与轮胎动变形以带惩罚项的软约束表达。遭遇超越物理极限的剧烈路面冲击时，求解器不会因无可行解（Infeasible）而报错或不收敛，而是自动给出离安全边界最近的可行折中解。
  • 确定性最坏执行时间：无论路面冲击与初始状态如何变化，最坏执行时间（WCET）在上线前即已知且可复现，算法必在固定时钟周期内给出输出，零超时风险。
  • Excellent numerical robustness: even with single-precision (float) arithmetic throughout, the solver keeps stable convergence and sufficient solution accuracy on low-compute 32-bit MCUs—no double-precision hardware required.
  • Soft-constraint relaxation: suspension travel and tire deformation are expressed as soft constraints with penalty terms. Under road impacts beyond physical limits the solver never reports infeasibility or stalls; it returns the closest feasible compromise to the safety envelope.
  • Deterministic worst-case execution time: regardless of road disturbance or initial state, the worst-case execution time (WCET) is known and reproducible before deployment, guaranteeing output within a fixed clock cycle with zero risk of timeout.

让复杂控制在车规芯片上实时运行 Run Advanced Control in Real Time on Automotive MCUs

了解 OPTIMicro 如何帮助您的智能底盘控制器跨越实时计算与量产部署的鸿沟。 See how OPTIMicro bridges the gap between real-time computation and production deployment for intelligent chassis controllers.

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