Prescribed Performance Control of Nonlinear Dynamical Systems
Summary
Prescribed Performance Control (PPC) addresses the challenge of enforcing explicit transient and steady-state specifications on the tracking error of nonlinear dynamical systems. By embedding performance envelopes—defined by time-varying bounds—into the control design, PPC ensures that tracking errors remain within predetermined limits, guaranteeing constraints on overshoot, convergence rate and steady-state accuracy. This paradigm integrates methodologies such as backstepping, sliding-mode control and neural–fuzzy approximation to tackle unknown dynamics, input saturations and external disturbances. Recent advances have focused on enhancing robustness, alleviating fragility under actuator constraints and reducing computational complexity through simplified adaptation schemes and command filters. The global significance of PPC spans aerospace applications—from waverider vehicles and spacecraft attitude control to marine systems and robotic manipulators—where stringent performance requirements are paramount. Interdisciplinary research continues to refine error transformation techniques, disturbance observers and finite-time convergence guarantees, cementing PPC as a unifying framework for high-precision control in safety-critical settings.
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Prescribed Performance Control of Nonlinear Dynamical Systems publication trend
The graph below shows the total number of articles in prescribed performance control of nonlinear dynamical systems across all publications each year (not limited to Nature Index journals).
Technical terms
Prescribed performance function: A time-varying bound that shapes permissible tracking error evolution in terms of overshoot, convergence rate and steady-state accuracy.
Funnel control: A control strategy that constrains the tracking error within a predefined funnel-shaped region over time.
Sliding-mode control: A robust control technique that drives system trajectories onto a sliding surface to maintain desired behaviour despite disturbances.
Backstepping: A recursive design methodology for strict-feedback nonlinear systems that stabilises each subsystem step by step.
Fuzzy neural network: A hybrid estimator combining fuzzy logic and neural networks to approximate unknown nonlinear functions within control laws.
References
- Nonfragile Quantitative Prescribed Performance Control of Waverider Vehicles With Actuator Saturation. IEEE Transactions on Aerospace and Electronic Systems (2022).
- Funnel control of nonlinear systems. Mathematics of Control, Signals, and Systems (2021).
- Robust Finite-Time Control of a Multi-AUV Formation Based on Prescribed Performance. Journal of Marine Science and Engineering (2023).
- Command Filter-Based Control for Spacecraft Attitude Tracking With Pre-Defined Maximum Settling Time Guaranteed. IEEE Access (2021).
- Non-Fragile Prescribed Performance Control of Robotic System without Function Approximation. Electronics (2024).
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