arXiv subjects

Peter A. Fisher

Publications and source records attributed to Peter A. Fisher.

9 recordsLinked to original sources

arXiv2026

Closed-Loop Model-Based Control Barrier Functions with Application to Robust Flight Envelope Protection

Ensuring operation of aerospace systems within prescribed flight envelope limits is a fundamental requirement for modern flight control architectures. Flight envelope protection aims to prevent violations of aerodynamic and structural constraints, thereby mitigating risks such as stall and excessive load factors. Control barrier functions (CBFs) have emerged as a principled tool for enforcing safety by ensuring that the system state remains within a prescribed safe set. In most existing approaches, safety constraints are imposed at the control input-level based on an open-loop model of the system. While this open-loop model-based CBF formulation enables modular design, it may alter the closed-loop system dynamics, potentially compromising robustness guarantees and complicating integration into existing flight control architectures. This paper proposes a closed-loop model-based control barrier function (CLM-CBF) framework for flight envelope protection. The key idea is to enforce safety at the reference-level using an explicit model of the closed-loop system, thereby preserving the stability and robustness properties of the underlying controller. This formulation enables safety filtering without modifying the control law, facilitating modular integration and retrofitting into existing systems.

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arXiv2026

Flight Envelope Protection for a Hypersonic Glide Vehicle Using Adaptive Safety-Critical Control

This paper presents an adaptive safety-critical control framework for flight envelope protection (FEP) of hypersonic glide vehicles (HGVs) under model uncertainty. The proposed architecture treats input and state constraints through two complementary but distinct mechanisms. At the input end, an adaptive controller with a calibrated closed-loop reference model (CCRM) is designed to accommodate magnitude-limited control inputs and the effects of actuator saturation. Building on this input-constrained adaptive control architecture, flight envelope state constraints are enforced at the state end by an error-based safety filter (EBSF) based on control barrier functions (CBFs). The EBSF modifies the reference command and adapts the admissible safe set online using the measured mismatch between the reference model and the uncertain plant, thereby preserving forward invariance during transient adaptation. Simulation results for the DLR generic hypersonic glide vehicle 2 (GHGV-2) demonstrate stable, high-performance tracking under magnitude-limited control inputs while maintaining flight envelope constraints in the presence of model uncertainty.

eess.SY↗

arXiv2026

Robust Safety Filters for Lipschitz-Bounded Adaptive Closed-Loop Systems with Structured Uncertainties

Adaptive control provides closed-loop stability and reference tracking for uncertain dynamical systems through online parameter adaptation. These properties alone, however, do not ensure safety in the sense of forward invariance of state constraints, particularly during transient phases of adaptation. Control barrier function (CBF)-based safety filters have been proposed to address this limitation, but existing approaches often rely on conservative constraint tightening or static safety margins within quadratic program formulations. This paper proposes a reference-based adaptive safety framework for systems with structured parametric uncertainty that explicitly accounts for transient plant-reference mismatch. Safety is enforced at the reference level using a barrier-function-based filter, while adaptive control drives the plant to track the safety-certified reference. By exploiting Lipschitz bounds on the closed-loop tracking error dynamics, a tracking-error-dependent robust CBF condition is derived and equivalently reformulated as a convex second-order cone program (SOCP). The proposed safety-filter formulation reduces conservatism relative to fixed-margin CBF formulations by rendering the resulting safety constraints progressively less restrictive as the plant-reference tracking error decreases, while preserving formal guarantees of forward invariance and closed-loop stability.

eess.SY↗

arXiv2026

Adapt and Stabilize, Then Learn and Optimize: A New Approach to Adaptive LQR

This paper focuses on adaptive control of the discrete-time linear quadratic regulator (adaptive LQR). Recent literature has made significant contributions in proving non-asymptotic convergence rates, but existing approaches have a few drawbacks that pose barriers for practical implementation. These drawbacks include (i) a requirement of an initial stabilizing controller, (ii) a reliance on exploration for closed-loop stability, and/or (iii) computationally intensive algorithms. This paper proposes a new algorithm that overcomes these drawbacks for a particular class of discrete-time systems. This algorithm leverages direct model-reference adaptive control (direct MRAC) and combines it with an epoch-based approach in order to address the drawbacks (i)-(iii) with a provable high-probability regret bound comparable to existing literature. Simulations demonstrate that the proposed approach yields regrets that are comparable to those from existing methods when the conditions (i) and (ii) are met, and yields regrets that are significantly smaller when either of these two conditions is not met.

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arXiv2026

An Error-Based Safety Buffer for Safe Adaptive Control (Extended Version)

We consider the problem of adaptive control of a class of feedback linearizable plants with matched parametric uncertainties whose states are accessible, subject to state constraints, which often arise due to safety considerations. In this paper, we combine adaptation and control barrier functions into a real-time control architecture that guarantees stability, ensures control performance, and remains safe even with the parametric uncertainties. Two problems are considered, differing in the nature of the parametric uncertainties. In both cases, the control barrier function is assumed to have an arbitrary relative degree. In addition to guaranteeing stability, it is proved that both the control objective and safety objective are met with near-zero conservatism. No excitation conditions are imposed on the command signal. Simulation results demonstrate the non-conservatism of all of the theoretical developments.

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arXiv2025

Safe and Stable Formation Control with Autonomous Multi-Agents Using Adaptive Control (Extended Version)

This manuscript considers the problem of ensuring stability and safety during formation control with distributed multi-agent systems in the presence of parametric uncertainty in the dynamics and limited communication. We propose an integrative approach that combines Adaptive Control, Control Barrier Functions (CBFs), and connected graphs. The main elements employed in the integrative approach are an adaptive control design that ensures stability, a CBF-based safety filter that generates safe commands based on a reference model dynamics, and a reference model that ensures formation control with multi-agent systems when no uncertainties are present. The overall control design is shown to lead to a closed-loop adaptive system that is stable, avoids unsafe regions, and converges to a desired formation of the multi-agents. Numerical examples are provided to support the theoretical derivations.

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arXiv2024

Analytical Construction of CBF-Based Safety Filters for Simultaneous State and Input Constraints (Extended Version)

We revisit the problem explored in [1] of guaranteeing satisfaction of multiple simultaneous state constraints applied to a single-input, single-output plant consisting of a chain of n integrators subject to input limitations. For this problem setting, we derive an analytic, easy-to-implement safety filter which respects input limitations and ensures forward-invariance of all state constraints simultaneously. Additionally, we provide a straightforward extension to the multi-input, multi-output chained integrator setting, and provide an analytic safety filter guaranteeing satisfaction of arbitrarily many simultaneous hyperplane constraints on the output vector. Whereas the approach in [1] obtains maximal invariant sets, our approach trades off some degree of conservatism in exchange for a recursive safety filter which is analytic for any arbitrary n >= 1.

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arXiv2023

Online Algorithms and Policies Using Adaptive and Machine Learning Approaches

This paper considers the problem of real-time control and learning in dynamic systems subjected to parametric uncertainties. We propose a combination of a Reinforcement Learning (RL) based policy in the outer loop suitably chosen to ensure stability and optimality for the nominal dynamics, together with Adaptive Control (AC) in the inner loop so that in real-time AC contracts the closed-loop dynamics towards a stable trajectory traced out by RL. Two classes of nonlinear dynamic systems are considered, both of which are control-affine. The first class of dynamic systems utilizes equilibrium points %with expansion forms around these points and a Lyapunov approach while second class of nonlinear systems uses contraction theory. AC-RL controllers are proposed for both classes of systems and shown to lead to online policies that guarantee stability using a high-order tuner and accommodate parametric uncertainties and magnitude limits on the input. In addition to establishing a stability guarantee with real-time control, the AC-RL controller is also shown to lead to parameter learning with persistent excitation for the first class of systems. Numerical validations of all algorithms are carried out using a quadrotor landing task on a moving platform.

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arXiv2023

Discrete-Time Adaptive Control of a Class of Nonlinear Systems Using High-Order Tuners

This paper concerns the adaptive control of a class of discrete-time nonlinear systems with all states accessible. Recently, a high-order tuner algorithm was developed for the minimization of convex loss functions with time-varying regressors in the context of an identification problem. Based on Nesterov's algorithm, the high-order tuner was shown to guarantee bounded parameter estimation when regressors vary with time, and to lead to accelerated convergence of the tracking error when regressors are constant. In this paper, we apply the high-order tuner to the adaptive control of a particular class of discrete-time nonlinear dynamical systems. First, we show that for plants of this class, the underlying dynamical error model can be causally converted to an algebraic error model. Second, we show that using this algebraic error model, the high-order tuner can be applied to provably stabilize the class of dynamical systems around a reference trajectory.

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