1.4 Concurrent Engineering and the Origins of CCD
From handoffs to concurrent decisions¶
Concurrent engineering keeps interacting disciplines engaged early enough to influence one another. CCD specializes that idea for dynamic systems: subsystem dynamics, control objectives, sensing, actuation, and implementation constraints are considered from the beginning rather than appended after the physical design is mature.
Integrated structure-control design in aerospace, combined passive-active suspension design, mechatronic optimization, optimal actuator placement, and multidisciplinary dynamic-system optimization all contributed to modern CCD. The terminology varies across fields, but the recurring goal is the same: preserve system-level design freedom where dynamics and feedback interact.
CCD is broader than one optimization problem¶
Garcia-Sanz organizes CCD into three complementary perspectives.
1. Control-inspired engineering design¶
Control knowledge guides physical invention and architecture before a formal optimizer is built. Engineers may reshape a structure to improve controllability, introduce passive load paths that cooperate with feedback, relocate sensors, separate time scales, or create a mechanism whose natural dynamics reduce control effort.
This perspective preserves physical intuition and can produce architecture changes that continuous parameter optimization would never discover.
2. Formal mathematical co-optimization¶
Plant variables, controller parameters or trajectories, states, and constraints are placed in an integrated mathematical program. The formulation may be nested, simultaneous, continuous, mixed-integer, deterministic, or uncertain. This perspective makes tradeoffs explicit and provides repeatable numerical evidence.
3. Model- and data-based co-simulation¶
Multiphysics, multiscale, or mixed-fidelity simulations evaluate candidate system designs. Experimental data, hardware tests, or learned models may augment the physics. Co-simulation is especially valuable when a compact analytic model cannot preserve the interactions that determine the design.
| Perspective | Primary strength | Typical limitation |
|---|---|---|
| Control-inspired design | Creativity and physical interpretation | May not quantify global tradeoffs |
| Co-optimization | Formal, repeatable system-level tradeoffs | Sensitive to formulation and numerical quality |
| Co-simulation | Captures complex interactions and data | Can be computationally expensive and opaque |
A concise historical arc¶
Classical control era: controllers are designed for largely fixed plants.
Integrated structure-control work: structural parameters, actuator placement, and feedback begin to be coordinated.
Mechatronic and multidisciplinary optimization: physical and control variables enter shared system studies.
Dynamic optimization and direct transcription: time histories become optimization variables in sparse nonlinear programs.
Modern CCD: architecture, uncertainty, information availability, high-fidelity simulation, and data are integrated.
This history cautions against defining CCD as merely “put plant parameters and gains in the same vector.” CCD is a concurrent engineering discipline supported—but not exhausted—by optimization.
Sources and further reading¶
M. Garcia-Sanz, “Control Co-Design: An Engineering Game Changer,” Advanced Control for Applications, 2019.
J. T. Allison and D. R. Herber, “Multidisciplinary Design Optimization of Dynamic Engineering Systems,” AIAA Journal, 2014.