Gaussian Process improves tracking control for unknown systems.
problem Challenges in perfect tracking control for real-world Euler-Lagrange systems.
method Employing Gaussian Process regression for data-driven model of unknown dynamics and adaptive feedback gains.
result Guaranteed globally bounded tracking error with specific probability.
RL approach for target tracking with unknown dynamics and sensor control.
problem Tracking an unknown target with sensor control.
method Track-MDP formulation for RL, compared with POMDP.
result Optimal RL policy tracks all target paths with certainty.
Paper presents neural network controllers for offset-free setpoint tracking.
problem Offset-free setpoint tracking using neural network controllers.
method Exploiting slope-restricted activation functions, linear matrix inequalities are used to verify stability.
result Global and local stability conditions for neural network controllers are derived.
Paper solves tracking control for (x,u)-flat systems using classical states.
problem Tracking control for (x,u)-flat systems. method Quasi-static feedback of classical states.
result Achieves linear, decoupled and asymptotically stable tracking error dynamics.
Improved FDR control for sparse financial index tracking.
problem Maintaining FDR control in high-dimensional financial data with strong variable dependencies.
method Expanding T-Rex framework to handle overlapping groups of correlated variables with nearest neighbors penalization.
result Accurately tracks the S&P 500 index using only a small number of stocks.
In this paper, we propose new conditions guaranteeing that the trajectories of a mechanical control system can track any curve on the configuration manifold. We focus on systems that can be represented as forced affine connection control systems and we generalize the sufficient conditions for tracking known in the lite…
The paper solves a control problem using reflections to track a benchmark process.
problem Optimal consumption with a benchmark process that grows over time.
method Introduced two auxiliary state processes with reflections to transform the problem into a more tractable form.
result Established the existence of a unique classical solution to the dual PDE.
New method uses Hessian to track gradients, improving variance reducing stochastic methods.
problem Improving variance reducing stochastic methods for faster convergence.
method Proposes a modified SVRG method using the Hessian for better control variates and accurate approximations.
result Demonstrates faster theoretical convergence and effectiveness on various problems.
Paper shows how to linearize flat systems with two inputs.
problem Linearizing flat nonlinear control systems with two inputs.
method Using prolongations of a control, the system can be made static feedback linearizable.
result A tracking control can be designed without requiring measurements of a generalized Brunovsky state.
The paper extends Merton's problem by adding benchmark tracking, finding optimal strategies.
problem Maximizing consumption utility with a trade-off against benchmark performance.
method Developed a convex duality theorem and derived optimal strategies for specific cases.
result Found optimal portfolio and consumption strategies for CRRA utility and geometric Brownian motion benchmarks.
Deep learning models control vehicle dynamics on a track.
problem Coupled longitudinal and lateral control of a vehicle.
method Trained two neural networks (MLP and CNN) to compute controls based on high-fidelity simulations.
result Deep learning models outperform conventional controllers on a challenging track.
New approach uses Gaussian processes to learn and track complex systems with guaranteed accuracy.
problem Inaccurate first principle models for complex systems due to data complexity.
method Bayesian prediction error bound for Gaussian process regression, derived from kernel-based data density.
result Achieves vanishing tracking error with increasing data density, providing time-varying accuracy guarantees.
Enhanced tracking control for AUVs with improved policy gradient method.
problem Trajectory tracking problem for underactuated AUVs with unknown dynamics and constrained inputs.
method Hybrid actors-critics architecture with multiple actors and critics, Pseudo Q-learning, and deterministic policy gradient.
result High-level tracking control accuracy and stable learning of AUVs.
Optimal tracking of nonholonomic systems using geometric methods.
problem Tracking a trajectory for nonholonomic mechanical systems.
method Geometric optimal control, Pontryagin Maximum Principle, variational approach.
result Optimal control solutions for nonholonomic systems validated by examples and simulations.
Study optimal consumption with relaxed benchmarks and drawdown constraints.
problem Optimal consumption under relaxed benchmark tracking and consumption drawdown constraint.
method Transformed stochastic control problem into regular control problem with state-control constraints, then solved using dual transform and optimal consumption behavior.
result Closed-form solution for optimal investment and consumption in feedback form.
New solar tracking system uses computer vision and low-cost hardware.
problem Current solar tracking systems are expensive and have operational issues.
method Low-cost hardware, computer vision, and deep learning.
result The new system shows great potential and can improve solar tracking performance.
Bayesian approach for constructing and rebalancing sparse index-tracking portfolios.
problem Sparse tracking of a reference index with uncertainty quantification.
method Sparse linear regression with Laplace prior, empirical-Bayes calibration, Langevin-type MCMC, threshold-based rules.
result Posterior uncertainty on tracking error, portfolio composition, and rebalancing moves.
Bayesian optimisation finds optimal race track driving policies.
problem Optimizing a robot's race track performance with limited interactions.
method Sequential coordinate descent Bayesian optimisation in RKHS.
result Algorithm finds optimal policies with minimal interactions.
Combining causality, control, and reinforcement learning for system control.
problem Learning to control dynamical systems using causal, control, and reinforcement learning approaches.
method Combining causal identification, control strategies, and reinforcement learning to control dynamical systems.
result Combining different learning paradigms for effective system control.
We develop a methodology for index tracking and risk exposure control using financial derivatives. Under a continuous-time diffusion framework for price evolution, we present a pathwise approach to construct dynamic portfolios of derivatives in order to gain exposure to an index and/or market factors that may be not di…
Unified framework for active and passive portfolio management combining outperformance and tracking.
problem Combining active and passive portfolio management objectives.
method Dynamic asset allocation using stochastic control techniques.
result Explicit closed-form expressions for optimal asset allocation.
This paper optimizes portfolio selection by penalizing tracking error, improving Sharpe ratio.
problem Optimizing portfolio allocation with a penalty for deviation from a reference portfolio.
method Formulated as a McKean-Vlasov control problem, provides explicit solutions and asymptotic expansions.
result The penalized portfolio strategy outperforms standard mean-variance and reference portfolios in most cases.
Optimal portfolio tracking with dynamic capital injection into a ratcheting benchmark.
problem Optimizing a portfolio's performance by dynamically adding capital to a non-decreasing benchmark.
method Formulated as an unconstrained control problem with a running maximum cost, transformed into an auxiliary problem with a nonlinear HJB equation, solved using probabilistic representation and stochastic flow analysis.
result Established the existence of a unique classical solution to the HJB equation, providing feedback optimal portfolio strategies.
Method improves volatility targeting for index construction.
problem High turnover, leverage spikes, and sensitivity to estimation error in existing volatility-targeting strategies.
method Proportional-control approach for setting index weights that corrects tracking error through feedback.
result The proportional-control approach achieves the target volatility more effectively than open-loop alternatives.
We consider the problem of tracking a target whose dynamics is modeled by a continuous Itō semi-martingale. The aim is to minimize both deviation from the target and tracking efforts. We establish the existence of asymptotic lower bounds for this problem, depending on the cost structure. These lower bounds can be relat…
This paper optimizes tracking portfolios in incomplete markets using reinforcement learning.
problem Optimizing tracking portfolios in incomplete markets with capital injection.
method Reinforcement learning approach for optimal control in reflected diffusion processes.
result Satisfactory performance of the q-learning algorithm in numerical examples.
We propose a long term portfolio management method which takes into account a liability. Our approach is based on the LQG (Linear, Quadratic cost, Gaussian) control problem framework and then the optimal portfolio strategy hedges the liability by directly tracking a benchmark process which represents the liability. Two…
Deep learning tracks body parts without markers, improving efficiency in neuroscience.
problem Efficiently tracking specific behaviors in animals without intrusive markers.
method Transfer learning with deep neural networks for markerless tracking.
result Deep learning achieves excellent tracking performance with minimal labeled data.
Paper proposes adaptive control for unknown systems using reinforcement learning.
problem Adaptive control for unknown, linearizable systems.
method On-policy reinforcement learning for discrete-time, stochastic systems.
result Stability and tracking errors concentrate near zero with high probability.
Optimizes heating setpoints with uncertain loads using various feedback types.
problem Optimizing heating setpoints with uncertain, flexible loads.
method Online convex optimization (OCO) with different types of feedback.
result Sublinear regret bounds achieved in all feedback types.
Deep RL improves power control and scheduling for wireless multicast systems.
problem Scalable power control and scheduling for wireless multicast networks.
method Deep reinforcement learning with function approximation using a deep neural network.
result Deep RL can learn optimal power control policies for large systems.
Hybrid method uses GP models to control robot trajectories.
problem Minimizing unmodeled dynamics in robot motion.
method Embeds non-parametric statistical models (GPs) for feedback control.
result Proposed method avoids complex analysis for wide trajectory classes.
This paper uses deep learning to estimate flow fields from OCT images for laser ablation control.
problem Automatic control of laser bone ablation using 4D OCT images.
method Semi-supervised convolutional neural network for 2.5D scene flow estimation.
result Scene flow estimation enables markerless tracking and automated laser ablation control.
This work provides safety guarantees for iterative GP predictions.
problem Analytical intractability of uncertainty tracking in iterative GP predictions.
method Deriving formal probability error bounds for iterative GP predictions.
result Formal bounds ensure that GP trajectories lie within specified regions with high probability.
New methods optimize machine learning models without sharing data.
problem Training machine learning models with distributed data.
method Decentralized stochastic optimization with gradient tracking and variance reduction.
result Improved algorithms for training machine learning models without data sharing.
Proposes an efficient method for sparse index tracking with ℓ0-norm constraints.
problem Constructing a sparse portfolio to track a financial index.
method Formulates a new problem using ℓ0-norm constraints, develops an efficient algorithm based on primal-dual splitting. result Demonstrates effectiveness through experiments on S&P500 and Russell3000 datasets.
The paper provides guarantees for feedback control with sensor errors.
problem Certifying performance and safety in feedback control systems with sensor errors.
method Solving a supervised learning problem to characterize sensor errors and providing uniform error bounds.
result Finite-time convergence rate on sub-optimality of using a regressor in closed-loop for waypoint tracking.
The paper uses deep reinforcement learning to control autonomous lane changes safely.
problem Safe and efficient autonomous lane changes in vehicles.
method Deep Q-networks and quadratic approximators for decision-making and control.
result Demonstrated effectiveness in simulations for decision-making and control.
Deep RL optimizes power control for wireless multicast systems.
problem Optimal power control is intractable due to a large state space.
method Deep reinforcement learning with function approximation via neural networks.
result Optimal power control can be learned for large systems.
Percolation on complex networks has been used to study computer viruses, epidemics, and other casual processes. Here, we present conditions for the existence of a network specific, observation dependent, phase transition in the updated posterior of node states resulting from actively monitoring the network. Since tradi…
Reinforcement learning optimizes robot trajectories for unknown dynamics.
problem Optimizing robot trajectories for systems with unknown dynamics.
method Curriculum learning with reinforcement learning to generate smooth trajectories.
result Reinforcement learning agent outperforms PID controllers in trajectory tracking.
Deep learning predicts market sensitivities for cost-effective index tracking.
problem Costly and impractical replication of index funds.
method Learning to predict market sensitivities using deep learning models.
result Significant reduction in prediction errors compared to historical methods.
Eye tracking measures ADHD-related working memory deficits.
problem Diagnosing ADHD in adults requires reliable measures of working memory capacity.
method Eye tracking technology and machine learning applied to a working memory task.
result Machine learning generated features unique to ADHD.
Paper studies optimal tracking portfolio in mean field game of large fund competition.
problem Optimal tracking portfolio in large fund competition with relative performance benchmark.
method Formulated mean field game problem, established existence of mean field equilibrium using PDE approach, constructed approximate Nash equilibrium.
result Existence of mean field equilibrium and consistency condition verified.
This paper learns motion primitives from driving data to improve vehicle path-tracking.
problem Improving vehicle path-tracking accuracy through learned motion primitives.
method Two-level structure with path segmentation and clustering; Gaussian Mixture Model (GMM) and Gaussian Mixture Regression (GMR).
result The model predicts future lateral control commands with high accuracy.
We consider the optimal control problem for a linear conditional McKean-Vlasov equation with quadratic cost functional. The coefficients of the system and the weigh-ting matrices in the cost functional are allowed to be adapted processes with respect to the common noise filtration. Semi closed-loop strategies are intro…
Deep RL estimates muscle excitations in biomechanical simulations.
problem Estimating muscle excitations from biomechanical systems.
method NAF reinforcement learning with custom reward function, episode-based hard update, and dual buffer experience replay.
result Models learned muscle excitations for given motions after 100,000 steps with <1% error.
Active learning method estimates nonlinear systems efficiently.
problem Identifying nonlinear dynamical systems with continuous states and actions.
method Repeating three steps: trajectory planning, tracking, and re-estimation.
result Estimates nonlinear dynamical systems at a parametric rate.