Quantized Control Strategies for Nonlinear Systems
Summary
Quantized control strategies address the challenge of implementing feedback laws when signals must be represented with a finite number of bits or discrete levels. In nonlinear systems, quantization arises in digital networks, embedded controllers and sensor limitations, and it can introduce significant distortions or limit cycles if not properly managed. Contemporary approaches combine uniform or dynamic quantizers with adaptive mechanisms, event-triggering schemes and barrier or Lyapunov-based methods to ensure stability and performance despite coarse signal resolution. Finite-time convergence, robustness to disturbances and actuator faults, and handling of input saturation are central objectives. Neural approximators, fuzzy logic systems and backstepping designs are often employed to compensate for unknown dynamics and unmodelled nonlinearities. Applications range from aerial vehicles and robotic manipulators to power electronics and large-scale networked systems, where reduced communication load and guaranteed safety margins are critical. The global significance of quantized control lies in enabling high-performance, resource-constrained automation across industrial, aerospace and energy domains.
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Quantized Control Strategies for Nonlinear Systems publication trend
The graph below shows the total number of articles in quantized control strategies for nonlinear systems across all publications each year (not limited to Nature Index journals).
Technical terms
Quantization: The mapping of continuous signals into a finite set of discrete values, often leading to information loss.
Nonlinear system: A dynamic system whose response cannot be described by a linear superposition of inputs.
Lyapunov stability: A criterion ensuring that small perturbations in state lead to bounded deviations from equilibrium.
Finite-time stability: The property that system states converge to a target set within a predetermined finite interval.
Event-triggered control: A strategy that updates control actions only when a specified state-dependent condition is met, reducing communication and computation load.
References
- Finite‐Time Adaptive Fuzzy Control for Stochastic Nonlinear Systems with Input Quantization and Actuator Faults. Journal of Mathematics (2024).
- Nearest neighbor control for practical stabilization of passive nonlinear systems. Automatica (2022).
- Finite-Time Adaptive Quantized Control for Quadrotor Aerial Vehicle with Full States Constraints and Validation on QDrone Experimental Platform. Drones (2024).
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