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Found 13 papers, 1 papers with code
Predictive stability filters for nonlinear dynamical systems affected by disturbances
Predictive safety filters provide a way of projecting potentially unsafe inputs, proposed, e. g. by a human or learning-based controller, onto the set of inputs that guarantee recursive state and input constraint satisfaction by leveraging model predictive control techniques.
Learning Soft Constrained MPC Value Functions: Efficient MPC Design and Implementation providing Stability and Safety Guarantees
This combination enables us to obtain a corresponding optimal control law, which can be implemented efficiently on embedded platforms.
LQG for Constrained Linear Systems: Indirect Feedback Stochastic MPC with Kalman Filtering
We present an output feedback stochastic model predictive control (SMPC) approach for linear systems subject to Gaussian disturbances and measurement noise and probabilistic constraints on system states and inputs.
Approximate Predictive Control Barrier Functions using Neural Networks: A Computationally Cheap and Permissive Safety Filter
A predictive control barrier function (PCBF) based safety filter is a modular framework to verify safety of a control input by predicting a future trajectory.
Adaptive Model Predictive Safety Certification for Learning-based Control -- Extended Version
By continuously connecting the current system state with a safe terminal set using a robust tube, safety can be ensured.
Learning-based Moving Horizon Estimation through Differentiable Convex Optimization Layers
In a numerical example of estimating temperatures of a group of manufacturing machines, we show the performance of tuning the unknown system parameters and the benefits of integrating physical state constraints in the MHE formulation.
Predictive control barrier functions: Enhanced safety mechanisms for learning-based control
While learning-based control techniques often outperform classical controller designs, safety requirements limit the acceptance of such methods in many applications.
Cautious Bayesian MPC: Regret Analysis and Bounds on the Number of Unsafe Learning Episodes
Furthermore, it is shown that the proposed constraint tightening implies a bound on the expected number of unsafe learning episodes in the linear and nonlinear case using a soft-constrained MPC formulation.
Data-Driven Distributed Stochastic Model Predictive Control with Closed-Loop Chance Constraint Satisfaction
Distributed model predictive control methods for uncertain systems often suffer from considerable conservatism and can tolerate only small uncertainties due to the use of robust formulations that are amenable to distributed design and optimization methods.
Distributed Model Predictive Safety Certification for Learning-based Control
While distributed algorithms provide advantages for the control of complex large-scale systems by requiring a lower local computational load and less local memory, it is a challenging task to design high-performance distributed control policies.
A predictive safety filter for learning-based control of constrained nonlinear dynamical systems
In this paper, we address this problem for nonlinear systems with continuous state and input spaces by introducing a predictive safety filter, which is able to turn a constrained dynamical system into an unconstrained safe system and to which any RL algorithm can be applied `out-of-the-box'.
Linear model predictive safety certification for learning-based control
The MPSC scheme can be used in order to expand any potentially conservative set of safe states for learning and we prove an iterative technique for enlarging the safe set.
Scalable synthesis of safety certificates from data with application to learning-based control
The control of complex systems faces a trade-off between high performance and safety guarantees, which in particular restricts the application of learning-based methods to safety-critical systems.
