arXiv subjects

Ilya Kolmanovsky

Publications and source records attributed to Ilya Kolmanovsky.

At least 73 records · Page 4Linked to original sources

arXiv2021

Energy-Efficient Autonomous Driving Using Cognitive Driver Behavioral Models and Reinforcement Learning

Autonomous driving technologies are expected to not only improve mobility and road safety but also bring energy efficiency benefits. In the foreseeable future, autonomous vehicles (AVs) will operate on roads shared with human-driven vehicles. To maintain safety and liveness while simultaneously minimizing energy consumption, the AV planning and decision-making process should account for interactions between the autonomous ego vehicle and surrounding human-driven vehicles. In this chapter, we describe a framework for developing energy-efficient autonomous driving policies on shared roads by exploiting human-driver behavior modeling based on cognitive hierarchy theory and reinforcement learning.

cs.RO↗

arXiv2021

Command Governors with Inexact Optimization and without Invariance

Reference and command governors are add-on schemes that augment nominal closed-loop systems with the capability to enforce state and control constraints. They do this by monitoring and modifying, when necessary, the reference command. Existing command governors do this by solving at each sampling time a quadratic programming problem to find a modified reference closest to the original command such that the current state and the modified reference pair are constraint admissible. In this paper, we show that a simple modification of the basic command governor enables it to operate with inexact optimization and even without requiring invariance of the constraint admissible set. Thus this modification significantly extends the applicability of the reference and command governors to practical problems where finding invariant sets may be problematic and where exact optimization may not be feasible due to reliability of the optimizers or limited computing power. Numerical examples are reported which illustrate the approach.

math.OC↗

arXiv2021

A Reference Governor for linear systems with polynomial constraints

The paper considers the application of reference governors to linear discrete-time systems with constraints given by polynomial inequalities. We propose a novel algorithm to compute the maximal output admissible invariant set in the case of polynomial constraints. The reference governor solves a constrained nonlinear minimization problem at initialization and then uses a bisection algorithm at the subsequent time steps. The effectiveness of the method is demonstrated by two numerical examples.

eess.SY↗

arXiv2021

Safe Learning Reference Governor: Theory and Application to Fuel Truck Rollover Avoidance

This paper proposes a learning reference governor (LRG) approach to enforce state and control constraints in systems for which an accurate model is unavailable, and this approach enables the reference governor to gradually improve command tracking performance through learning while enforcing the constraints during learning and after learning is completed. The learning can be performed either on a black-box type model of the system or directly on the hardware. After introducing the LRG algorithm and outlining its theoretical properties, this paper investigates LRG application to fuel truck (tank truck) rollover avoidance. Through simulations based on a fuel truck model that accounts for liquid fuel sloshing effects, we show that the proposed LRG can effectively protect fuel trucks from rollover accidents under various operating conditions.

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arXiv2021

Reference Governor-Based Fault-Tolerant Constrained Control

This paper presents a fault-tolerant control scheme for constrained linear systems. First, a new variant of the Reference Governor (RG) called At Once Reference Governor (AORG) is introduced. The AORG is distinguished from the conventional RG by computing the Auxiliary Reference (AR) sequence so that to optimize performance over a prescribed time interval instead of only at the current time instant; this enables the integration of the AORG with fault detection schemes. In particular, it is shown that, when the AORG is combined with a Multi-Model Adaptive Estimator (MMAE), the AR sequence can be determined such that the tracking properties are guaranteed and constraints are satisfied at all times, while the detection performance is optimized, i.e., faults can be detected with a high probability of correctness. In addition a reconfiguration scheme is presented that ensures system viability despite the presence of faults based on recoverable sets. Simulations on a Boeing 747-100 aircraft model are carried out to evaluate the effectiveness of the AORG scheme in enforcing constraints and tracking the desired roll and side-slip angles. The effectiveness of the presented fault-tolerant control scheme in maintaining the airplane viability in the presence of damaged vertical stabilizer is also demonstrated.

math.OC↗

arXiv2021

Development, Implementation, and Experimental Outdoor Evaluation of Quadcopter Controllers for Computationally Limited Embedded Systems

Quadcopters are increasingly used for applications ranging from hobby to industrial products and services. This paper serves as a tutorial on the design, simulation, implementation, and experimental outdoor testing of digital quadcopter flight controllers, including Explicit Model Predictive Control, Linear Quadratic Regulator, and Proportional Integral Derivative. A quadcopter was flown in an outdoor testing facility and made to track an inclined, circular path at different tangential velocities under ambient wind conditions. Controller performance was evaluated via multiple metrics, such as position tracking error, velocity tracking error, and onboard computation time. Challenges related to the use of computationally limited embedded hardware and flight in an outdoor environment are addressed with proposed solutions.

eess.SY↗

arXiv2021

Safe Reinforcement Learning Using Robust Action Governor

Reinforcement Learning (RL) is essentially a trial-and-error learning procedure which may cause unsafe behavior during the exploration-and-exploitation process. This hinders the application of RL to real-world control problems, especially to those for safety-critical systems. In this paper, we introduce a framework for safe RL that is based on integration of a RL algorithm with an add-on safety supervision module, called the Robust Action Governor (RAG), which exploits set-theoretic techniques and online optimization to manage safety-related requirements during learning. We illustrate this proposed safe RL framework through an application to automotive adaptive cruise control.

cs.LG↗

arXiv2021

Safe Affine Transformation-Based Guidance of a Large-Scale Multi-Quadcopter System (MQS)

This paper studies the problem of affine transformation-based guidance of a multi-quadcopter system (MQS) in an obstacle-laden environment. Such MQSs can perform a variety of cooperative tasks including information collection, inspection mapping, disinfection, and firefighting. The MQS affine transformation is an approach to a decentralized leader-follower coordination guided by n +1 leaders, where leaders are located at vertices of an n-D simplex, called leading simplex, at any time t. The remaining agents are followers acquiring the desired affine transformation via local communication. Followers are contained in a rigid-size ball at any time t but they can be distributed either inside or outside the leading simplex. By eigen-decomposition of the affine transformation coordination, safety in a large-scale MQS coordination can be ensured by constraining eigenvalues of the affine transformation. Given the initial and final configurations of the MQS, A-star search is applied to optimally plan safe coordination of a large-scale MQS minimizing the travel distance between the the initial and final configuration. The paper also proposes a proximity-based communication topology for followers to assign communication weights with their in-neighbors and acquire the desired coordination with minimal computation cost.

cs.MA↗

arXiv2021

Feasibility Governor for Linear Model Predictive Control

This paper introduces the Feasibility Governor (FG): an add-on unit that enlarges the region of attraction of Model Predictive Control by manipulating the reference to ensure that the underlying optimal control problem remains feasible. The FG is developed for linear systems subject to polyhedral state and input constraints. Offline computations using polyhedral projection algorithms are used to construct the feasibility set. Online implementation relies on the solution of a convex quadratic program that guarantees recursive feasibility. The closed-loop system is shown to satisfy constraints, achieve asymptotic stability, and exhibit zero-offset tracking.

math.OC↗

arXiv2021

An Analysis of Closed-Loop Stability for Linear Model Predictive Control Based on Time-Distributed Optimization

Time-distributed Optimization (TDO) is an approach for reducing the computational burden of Model Predictive Control (MPC). When using TDO, optimization iterations are distributed over time by maintaining a running solution estimate and updating it at each sampling instant. In this paper, TDO applied to input constrained linear MPC is studied in detail, and analytic expressions for the system gains and a bound on the number of optimization iterations per sampling instant required to guarantee closed-loop stability is derived. Further, it is shown that the closed-loop stability of TDO-based MPC can be guaranteed using multiple mechanisms including increasing the number of solver iterations, preconditioning the optimal control problem, adjusting the MPC cost matrices, and reducing the length of the receding horizon. These results in a linear system setting also provide insights and guidelines that could be more broadly applicable, e.g., to nonlinear MPC.

math.OC↗

arXiv2021

Coordinated Receding-Horizon Control of Battery Electric Vehicle Speed and Gearshift Using Relaxed Mixed Integer Nonlinear Programming

In this paper, we propose an approach to coordinated receding-horizon control of vehicle speed and transmission gearshift for automated battery electric vehicles (BEVs) to achieve improved energy efficiency. The introduction of multi-speed transmissions in BEVs creates an opportunity to manipulate the operating point of electric motors under given vehicle speed and acceleration command, thus providing the potential to further improve the energy efficiency. However, co-optimization of vehicle speed and transmission gearshift leads to a mixed integer nonlinear program (MINLP), solving which can be computationally very challenging. In this paper, we propose a novel continuous relaxation technique to treat such MINLPs that makes it possible to compute solutions with conventional nonlinear programming solvers. After analyzing its theoretical properties, we use it to solve the optimization problem involved in coordinated receding-horizon control of BEV speed and gearshift. Through simulation studies, we show that co-optimizing vehicle speed and transmission gearshift can achieve considerably greater energy efficiency than optimizing them sequentially, and the proposed relaxation technique can reduce the online computational cost to a level that is comparable to the time available for real-time implementation.

eess.SY↗

arXiv2021

Beating humans in a penny-matching game by leveraging cognitive hierarchy theory and Bayesian learning

It is a long-standing goal of artificial intelligence (AI) to be superior to human beings in decision making. Games are suitable for testing AI capabilities of making good decisions in non-numerical tasks. In this paper, we develop a new AI algorithm to play the penny-matching game considered in Shannon's "mind-reading machine" (1953) against human players. In particular, we exploit cognitive hierarchy theory and Bayesian learning techniques to continually evolve a model for predicting human player decisions, and let the AI player make decisions according to the model predictions to pursue the best chance of winning. Experimental results show that our AI algorithm beats 27 out of 30 volunteer human players.

cs.AI↗

arXiv2021

Experimental Validation of Eco-Driving and Eco-Heating Strategies for Connected and Automated HEVs

This paper presents experimental results that validate eco-driving and eco-heating strategies developed for connected and automated vehicles (CAVs). By exploiting vehicle-to-infrastructure (V2I) communications, traffic signal timing, and queue length estimations, optimized and smoothed speed profiles for the ego-vehicle are generated to reduce energy consumption. Next, the planned eco-trajectories are incorporated into a real-time predictive optimization framework that coordinates the cabin thermal load (in cold weather) with the speed preview, i.e., eco-heating. To enable eco-heating, the engine coolant (as the only heat source for cabin heating) and the cabin air are leveraged as two thermal energy storages. Our eco-heating strategy stores thermal energy in the engine coolant and cabin air while the vehicle is driving at high speeds, and releases the stored energy slowly during the vehicle stops for cabin heating without forcing the engine to idle to provide the heating source. To test and validate these solutions, a power-split hybrid electric vehicle (HEV) has been instrumented for cabin thermal management, allowing to regulate heating, ventilation, and air conditioning (HVAC) system inputs (cabin temperature setpoint and blower flow rate) in real-time. Experiments were conducted to demonstrate the energy-saving benefits of eco-driving and eco-heating strategies over real-world city driving cycles at different cold ambient temperatures. The data confirmed average fuel savings of 14.5% and 4.7% achieved by eco-driving and eco-heating, respectively, offering a combined energy saving of more than 19% when comparing to the baseline vehicle driven by a human driver with a constant-heating strategy.

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arXiv2020

A Feasibility Governor for Enlarging the Region of Attraction of Linear Model Predictive Controllers

This paper proposes a method for enlarging the region of attraction of Linear Model Predictive Controllers (MPC) when tracking piecewise-constant references in the presence of pointwise-in-time constraints. It consists of an add-on unit, the Feasibility Governor (FG), that manipulates the reference command so as to ensure that the optimal control problem that underlies the MPC feedback law remains feasible. Offline polyhedral projection algorithms based on multi-objective linear programming are employed to compute the set of feasible states and reference commands. Online, the action of the FG is computed by solving a convex quadratic program. The closed-loop system is shown to satisfy constraints, be asymptotically stable, exhibit zero-offset tracking, and display finite-time convergence of the reference.

math.OC↗

arXiv2020

Chance-Constrained Controller State and Reference Governor

The controller state and reference governor (CSRG) is an add-on scheme for nominal closed-loop systems with dynamic controllers which supervises the controller internal state and the reference input to the closed-loop system to enforce pointwise-in-time constraints. By admitting both controller state and reference modifications, the CSRG can achieve an enlarged constrained domain of attraction compared to conventional reference governor schemes where only reference modification is permitted. This paper studies the CSRG for systems subject to stochastic disturbances and chance constraints. We describe the CSRG algorithm in such a stochastic setting and analyze its theoretical properties, including chance-constraint enforcement, finite-time reference convergence, and closed-loop stability. We also present examples illustrating the application of CSRG to constrained aircraft flight control.

eess.SY↗

arXiv2020

Suboptimal Nonlinear Model Predictive Control Strategies for Tracking Near Rectilinear Halo Orbits

Near Rectilinear Halo Orbits (NRHOs), a subclass of halo orbits around the L1 and L2 Lagrange points, are promising candidates for future lunar gateways in cis-lunar space and as staging orbits for lunar missions. Closed-loop control is beneficial to compensate orbital perturbations and potential instabilities while maintaining spacecraft on NRHOs and performing relative motion maneuvers. This paper investigates the use of nonlinear model predictive control (NMPC) coupled with low-thrust actuators for station-keeping on NRHOs. It is demonstrated through numerical simulations that NMPC is able to stabilize a spacecraft to a reference orbit and handle control constraints. Further, it is shown that the computational burden of NMPC can be managed using specialized optimization routines and suboptimal approaches without jeopardizing closed-loop performance.

eess.SY↗

arXiv2020

A constraint-separation principle in model predictive control

In this brief, we consider the constrained optimization problem underpinning model predictive control (MPC). We show that this problem can be decomposed into an unconstrained optimization problem with the same cost function as the original problem and a constrained optimization problem with a modified cost function and dynamics that have been precompensated according to the solution of the unconstrained problem. In the case of linear systems subject to a quadratic cost, the unconstrained problem has the familiar LQR solution and the constrained problem reduces to a minimum-norm projection. This implies that solving linear MPC problems is equivalent to precompensating a system using LQR and applying MPC to penalize only the control input. We propose to call this a constraint-separation principle and discuss the utility of both constraint separation and general decomposition in the design of MPC schemes and the development of numerical solvers for MPC problems.

math.OC↗

arXiv2020

A Sum-of-Squares-Based Procedure to Approximate the Pontryagin Difference of Semialgebraic Sets

The P-difference between two sets $\mathcal{A}$ and $\mathcal{B}$ is the set of all points, $\mathcal{C}$, such that the addition of $\mathcal{B}$ to any of the points in $\mathcal{C}$ is contained in $\mathcal{A}$. Such a set difference plays an important role in robust model predictive control and in set-theoretic control. In the paper we demonstrate that an inner approximation of the P-difference between two semialgebraic sets can be computed using the Sums of Squares Programming, and we illustrate the procedure using several computational examples.

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