Admissibility-Preserving Control for Strict-Feedback Nonlinear Systems with Asymmetric Actuator Constraints
- URL: http://arxiv.org/abs/2608.15375v1
- Date: Sat, 15 Aug 2026 19:09:47 GMT
- Title: Admissibility-Preserving Control for Strict-Feedback Nonlinear Systems with Asymmetric Actuator Constraints
- Abstract summary: Admissibility-Preserving Control (APC) is a realization-centered safety-critical control framework for strict-feedback systems.<n>The APIR dynamically generates the physical plant input while rendering its prescribed asymmetric actuator set forward invariant.<n>The framework is extended to time-varying output-safe tracking through a smooth asymmetric logarithmic barrier coordinate.
- Score: 1.4477778415264126
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This paper develops Admissibility-Preserving Control (APC), a realization-centered safety-critical control framework for strict-feedback systems subject to asymmetric actuator limits, time-varying output constraints, and actuator-rate limitations. APC denotes the overall control architecture, whereas an Admissibility-Preserving Input Realization (APIR) denotes its constraint-realization module. Therein, the APIR dynamically generates the physical plant input while rendering its prescribed asymmetric actuator set forward invariant. In contrast to algebraic clipping and post-design saturation compensation, the actuator limits are embedded directly in a continuously differentiable dynamic realization with user-selectable regularity and interpretable tuning parameters. The APIR is integrated with recursive backstepping by treating the realized plant input as an additional state. The resulting design does not require an input-to-state stability assumption on the uncontrolled plant. Instead, the nonlinear drift terms are compensated recursively, subject to an explicit compatibility condition between the desired motion, the available control authority, and the APIR interior gain. The framework is further extended to time-varying output-safe tracking through a smooth asymmetric logarithmic barrier coordinate and its associated Lyapunov function and to simultaneous actuator-magnitude and rate constraints through a cascaded APIR. Rigorous Lyapunov and invariance analyses establish regional asymptotic tracking, forward invariance of the compatible admissible sets, and boundedness of all closed-loop signals. Numerical studies illustrate asymmetric actuator utilization, output-safety preservation, and magnitude-rate constraint enforcement.
Related papers
- Networked Admissibility-Preserving Control for Directed Safe Coordination [0.0]
This paper addresses safety-critical coordination for scalar agents.<n>Agents communicate over a fixed weighted digraph with a directed spanning tree.<n>The synthesis yields an exact cascade in which exponentially decaying realization errors drive nonsymmetric consensus dynamics.
arXiv Detail & Related papers (2026-09-08T19:38:20Z) - MoASE++: Mixture of Activation Sparsity Experts with Domain-Adaptive On-policy Distillation for Continual Test Time Adaptation [65.068801413044]
Continual test-time adaptation adapts a source-pretrained model to non-stationary, unlabeled target streams.<n>We introduce MoASE, a mixture-of-experts that disentangles domain-agnostic structure from domain-specific texture.
arXiv Detail & Related papers (2026-05-18T01:52:12Z) - Response-Aware Risk-Constrained Control Barrier Function With Application to Vehicles [0.0]
This paper proposes a unified control framework based on Response-Aware Risk-Constrained Control Barrier Function for dynamic safety boundary control of vehicles.<n>The framework fuses nominal dynamics priors with direct vehicle body responses to provide a baseline direction for control gradients.<n>It also reformulates traditional deterministic safety constraints into probabilistic constraints on the tail risk of barrier function derivatives.
arXiv Detail & Related papers (2026-03-13T02:38:17Z) - Trajectory Tracking Control Design for Autonomous Helicopters with Guaranteed Error Bounds [0.0]
This paper presents a framework for computing formally guaranteed trajectory tracking error bounds for autonomous helicopters based on Robust Invariant (RPI) sets.<n>Ellipsoidal RPI sets are computed, yielding explicit position error bounds suitable as certified buffer zones in upper-level trajectory planning.
arXiv Detail & Related papers (2026-03-09T07:35:09Z) - Diffusion Controller: Framework, Algorithms and Parameterization [54.82539154511621]
We present a unified control-theoretic view that casts reverse diffusion sampling as state-only control within (generalized) linearly-solvable Markov Decision Processes.<n>Under this framework, control acts by reweighting the pretrained reverse-time transition kernels, balancing terminal objectives against an $f$-divergence cost.<n>Experiments on Stable Diffusion v1.4 show consistent gains in preference-alignment win rates and improved quality-efficiency trade-offs.
arXiv Detail & Related papers (2026-03-07T01:49:59Z) - QP-Based Control of an Underactuated Aerial Manipulator under Constraints [1.5984927623688912]
This paper presents a constraint-aware control framework for underactuated aerial manipulators.<n>It enables accurate end-effector trajectory tracking while explicitly accounting for safety and feasibility constraints.
arXiv Detail & Related papers (2026-01-13T13:05:36Z) - Model-Less Feedback Control of Space-based Continuum Manipulators using Backbone Tension Optimization [0.0]
Continuum manipulators offer dexterity and safe compliance for navigation within confined and obstacle-rich environments.<n>Their infinite-dimensional backbone deformation, unmodeled internal friction, and configuration-dependent stiffness limit the reliability of model-based kinematic formulations.<n>This work presents a complete model-less control framework that bypasses kinematic modeling by using an empirically intrinsic Jacobian refined online through differential convex updates.
arXiv Detail & Related papers (2025-12-07T09:34:28Z) - Deep Reinforcement Learning Optimization for Uncertain Nonlinear Systems via Event-Triggered Robust Adaptive Dynamic Programming [0.3848364262836075]
This work proposes a unified control architecture that couples a Reinforcement Learning (RL)-driven controller with a disturbance-rejection Extended State Observer (ESO)<n>The ESO is utilized to estimate the system states and the lumped disturbance in real time, forming the foundation for effective disturbance compensation.
arXiv Detail & Related papers (2025-12-05T22:52:22Z) - Verifying Closed-Loop Contractivity of Learning-Based Controllers via Partitioning [52.23804865017831]
We address the problem of verifying closed-loop contraction in nonlinear control systems whose controller and contraction metric are both parameterized by neural networks.<n>We derive a tractable and scalable sufficient condition for closed-loop contractivity that reduces to checking that the dominant eigenvalue of a symmetric Metzler matrix is nonpositive.
arXiv Detail & Related papers (2025-12-01T23:06:56Z) - On the Joint Minimization of Regularization Loss Functions in Deep Variational Bayesian Methods for Attribute-Controlled Symbolic Music Generation [47.38557855930304]
Explicit latent variable models provide a flexible yet powerful framework for data synthesis.<n>We show that existing approaches struggle to jointly minimize both regularization objectives.<n>We show that suitable attribute transformations can help achieve both controllability and regularization of the target latent dimensions.
arXiv Detail & Related papers (2025-11-10T14:09:25Z) - Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty [49.19257648205146]
We propose an unsupervised conformal inference framework for generation.<n>Our gates achieve close-to-nominal coverage and provide tighter, more stable thresholds than split UCP.<n>The result is a label-free, API-compatible gate for test-time filtering.
arXiv Detail & Related papers (2025-09-26T23:40:47Z) - Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic
Dynamical Models with Epistemic Uncertainty [68.00748155945047]
Capturing uncertainty in models of complex dynamical systems is crucial to designing safe controllers.
Several approaches use formal abstractions to synthesize policies that satisfy temporal specifications related to safety and reachability.
Our contribution is a novel abstraction-based controller method for continuous-state models with noise, uncertain parameters, and external disturbances.
arXiv Detail & Related papers (2022-10-12T07:57:03Z) - Gaussian Process-based Min-norm Stabilizing Controller for
Control-Affine Systems with Uncertain Input Effects and Dynamics [90.81186513537777]
We propose a novel compound kernel that captures the control-affine nature of the problem.
We show that this resulting optimization problem is convex, and we call it Gaussian Process-based Control Lyapunov Function Second-Order Cone Program (GP-CLF-SOCP)
arXiv Detail & Related papers (2020-11-14T01:27:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.