# Lucian BusoniuUniversitatea Tehnica Cluj-Napoca | UT Cluj · Robotics and Nonlinear Control Group, Department of Automation

Lucian Busoniu

PhD, MSc. Eng.

## About

123

Publications

47,672

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5,894

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Citations since 2017

Introduction

Please don't forget to check http://busoniu.net/publications.php for directly downloadable PDFs of the publications.

Additional affiliations

January 2012 - September 2013

October 2011 - September 2014

April 2011 - December 2011

## Publications

Publications (123)

We consider problems where a controller communicates with a general nonlinear plant via a network, and must optimize a performance index. The system is modeled in discrete time and may be affected by a class of stochastic uncertainties that can take finitely many values. Admissible inputs are constrained to belong to a finite set. Exploiting some o...

Unmanned aerial vehicles (UAVs) have gained significant attention in recent years. Low-cost platforms using inexpensive sensor payloads have been shown to provide satisfactory flight and navigation capabilities. In this report, we survey vision and control methods that can be applied to low-cost UAVs, and we list some popular inexpensive platforms...

An important problem in multiagent systems is consensus, which requires the agents to agree on certain controlled variables of interest. We focus on the challenge of dealing in a generic way with nonlinear agent dynamics, represented as a black box with unknown mathematical form. Our approach designs a reference behavior with a classical consensus...

From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control them. While Dynamic Programming (DP) has provided researchers with a way to optimally solve decision and control problems involving complex dynamic systems, its practical value was limited by a...

Multiagent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, and economics. The complexity of many tasks arising in these domains makes them difficult to solve with preprogrammed agent behaviors. The agents must, instead, discover a solution on their own, using learning. A...

We propose two new optimistic planning algorithms for nonlinear hybrid-input systems, in which the input has both a continuous and a discrete component, and the discrete component must respect a dwell-time constraint. Both algorithms select sets of input sequences for refinement at each step, along with a continuous or discrete step to refine (spli...

We propose two new optimistic planning algorithms for nonlinear hybrid-input systems, in which the input has both a continuous and a discrete component, and the discrete component must respect a dwell-time constraint. Both algorithms select sets of input sequences for refinement at each step, along with a continuous or discrete step to refine (spli...

We present stability conditions for deterministic time-varying nonlinear discrete-time systems whose inputs aim to minimize an infinite-horizon time-dependent cost. Global asymptotic and exponential stability properties for general attractors are established. This work covers and generalizes the related results on discounted optimal control problem...

Consider a robot with nonlinear dynamics that must quickly find a global optimum of an objective function defined over its operating area, e.g., a chemical concentration, physical measurement, quantity of material etc. The function is initially unknown and must be learned online from samples acquired in a single trajectory. Applying classical optim...

We present an approach for a mobile robot to seek the global maximum of an initially unknown function defined over its operating space. The method exploits a Lipschitz assumption to defne an upper bound on the function from previously seen samples, and optimistically moves towards the largest upper-bound point. This point is iteratively changed whe...

Underwater mapping with mobile robots has a wide range of applications, and good models are lacking for key parts of the problem, such as sensor behavior. The specific focus here is the huge environmental problem of underwater litter, in the context of the Horizon 2020 SeaClear project, where a team of robots is being developed to map and collect s...

We consider a scenario in which a UAV must locate an unknown number of targets at unknown locations in a 2D environment. A random finite set formulation with a particle filter is used to estimate the target locations from noisy measurements that may miss targets. A novel planning algorithm selects a next UAV state that maximizes an objective functi...

Rehabilitation promoting "assistance-as-needed" is considered a promising scheme of active rehabilitation, since it can promote neuroplasticity faster and thus reduce the time needed until restoration. To implement such schemes using robotic devices, it is crucial to be able to predict accurately and in real-time the intention of motion of the pati...

To investigate solutions of (near-)optimal control problems, we extend and exploit a notion of homogeneity recently proposed in the literature for discrete-time systems. Assuming the plant dynamics is homogeneous, we first derive a scaling property of its solutions along rays provided the sequence of inputs is suitably modified. We then consider ho...

A key component in autonomous driving is the ability of the self-driving car to understand, track and predict the dynamics of the surrounding environment. Although there is significant work in the area of object detection, tracking and observations prediction, there is no prior work demonstrating that raw observations prediction can be used for mot...

Most of the recent deep reinforcement learning advances take an RL-centric perspective and focus on refinements of the training objective. We diverge from this view and show we can recover the performance of these developments not by changing the objective, but by regularising the value-function estimator. Constraining the Lipschitz constant of a s...

Originating in the artificial intelligence literature, optimistic planning(OP) is an algorithm that generates near-optimal control inputs for generic nonlinear discrete-time systems whose input set is finite. This technique is therefore relevant for the near-optimal control of nonlinear switched systems for which the switching signal is the control...

Power-assisted wheelchairs (PWA) is an important growing market. The goal is to provide electrical assistive kits that are able to cope with a large family of disabled people and to equip a large variety of wheelchairs. This work is made in collaboration with Autonomad Mobility, a company that designs the hardware and sells Power-Assistance kits fo...

Model predictive control (MPC) is based on the systematic resolution of an online optimization problem at each time step. In practice, the computation cost is often very high, especially for the non-linear case under constraints, thus complicating the application of MPC to real-time systems. This paper proposes to improve the non-linear quadratic d...

This paper presents an observer-based control design approach for a class of nonlinear discrete-time systems. The model nonlinearities are handled in two ways: 1) a Takagi-Sugeno fuzzy representation is used for nonlinearities that depend on measured states, and 2) nonlinearities that depend on unmeasured states are kept in their original form and...

Value iteration (VI) is a ubiquitous algorithm for optimal control, planning, and reinforcement learning schemes. Under the right assumptions, VI is a vital tool to generate inputs with desirable properties for the controlled system, like optimality and Lyapunov stability. As VI usually requires an infinite number of iterations to solve general non...

In tasks such as surveying or monitoring remote regions, an autonomous robot must move while transmitting data over a wireless network with unknown, position-dependent transmission rates. For such a robot, this paper considers the problem of transmitting a data buffer in minimum time, while possibly also navigating towards a goal position. Two appr...

We address formally the problem of opinion dynamics when the agents of a social network (e.g., consumers) are not only influenced by their neighbors but also by an external influential entity referred to as a marketer. The influential entity tries to sway the overall opinion as close as possible to a desired opinion by using a specific influence bu...

We address formally the problem of opinion dynamics when the agents of a social network (e.g., consumers) are not only influenced by their neighbors but also by an external influential entity referred to as a marketer. The influential entity tries to sway the overall opinion as close as possible to a desired opinion by using a specific influence bu...

In tasks such as surveying or monitoring remote regions, an autonomous robot must move while transmitting data over a wireless network with unknown, position-dependent transmission rates. For such a robot, this paper considers the problem of transmitting a data buffer in minimum time, while possibly also navigating towards a goal position. Two appr...

This paper presents a discrete-time Takagi-Sugeno fuzzy observer design approach for a class of nonlinear systems. Instead of including all the nonlinear terms in the membership functions, some of them are kept as nonlinear consequents, and they need to fulfill a global Lipschitz condition. The form considered permits nonlinear consequents that dep...

We consider a robot that must sort objects transported by a conveyor belt into different classes. Multiple observations must be performed before taking a decision on the class of each object, because the imperfect sensing sometimes detects the incorrect object class. The objective is to sort the sequence of objects in a minimal number of observatio...

Motivated by (approximate) dynamic programming and model predictive control problems, we analyse the stability of deterministic nonlinear discrete-time systems whose inputs minimize a discounted finite-horizon cost. We assume that the system satisfies stabilizability and detectability properties with respect to the stage cost. Then, a Lyapunov func...

We consider adversarial problems in which two agents control two switching signals, the first agent aiming to maximize a discounted sum of rewards, and the second aiming to minimize it. Both signals may be subject to constraints on the dwell time after a switch. We search the tree of possible mode sequences with an algorithm called optimistic minim...

We describe a novel student contest concept in which an unmanned aerial vehicle (UAV or drone) must autonomously navigate a straight corridor using feedback from camera images. The objective of the contest is to promote engineering skills (related to sensing and control in particular) among students and young professionals, by means of an attractiv...

We propose an algorithm to search for parametrized policies in continuous state and action Markov Decision Processes (MDPs). The policies are represented via a number of basis functions, and the main novelty is that each basis function corresponds to a small, discrete modification of the continuous action. In each state, the policy chooses a discre...

This paper presents a sliding mode control design for a ball-balancing robot (ballbot), with associated real-time results. The sliding mode control is designed based on the linearized plant model, and is robust to matched uncertainties. The design is considerably simpler than other nonlinear control strategies presented in the literature, and the e...

Originating in the artificial intelligence literature, optimistic planning (OP) is an algorithm that generates near-optimal control inputs for generic nonlinear discrete-time systems whose input set is finite. This technique is therefore relevant for the near-optimal control of nonlinear switched systems, for which the switching signal is the contr...

The objective of this paper is to develop a method for assisting users to push Power-Assisted Wheelchairs (PAW) in such a way that the electrical energy consumption over a predefined distance-to-go is optimal, while at the same time bringing users to a desired fatigue level. This assistive task is formulated as an optimal control problem and solved...

Power-assisted wheelchairs (PAW) are efficient means of transportation for disabled persons. The resulting human-machine system includes several unknown parameters such as the mass of the user or ground adhesion. Moreover, the torque signals produced by the human are required to design a robust assistive strategy, but measuring them with torque sen...

We consider dual switched systems, in which two switching signals act simultaneously to select the dynamical mode. The first signal is controlled and the second is random, with probabilities that evolve either periodically or as a function of the dwell time. We formalize both cases as Markov decision processes, which allows them to be solved with a...

Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain. This review mainly covers artificial-intelligence approaches to RL, from the viewpoint of the control engineer. We explain how approximate representations of the solut...

We consider discrete-time, infinite-horizon optimal control problems with discounted rewards. The value function must be Lipschitz continuous over action (input) sequences, the actions are in a scalar interval, while the dynamics and rewards can be nonlinear/nonquadratic. Exploiting ideas from artificial intelligence, we propose two optimistic plan...

Compared to manual wheelchairs and fully electric powered wheelchairs, power-assisted wheelchairs (PAWs) provide a special structure where the human can use her/his propulsion to interact with the assistive system. In this context, different studies have focused on the assistive control of PAWs in recent years. This paper presents an observed-based...

We describe a demonstrator application that uses a UAV to monitor and detect falls of an at-risk person. The position and state (upright or fallen) of the person are determined with deep-learning-based computer vision, where existing network weights are used for position detection, while for fall detection the last layer is fine-tuned in additional...

We address formally the problem of opinion dynamics when the agents of a social network (e.g., consumers) are not only influenced by their neighbors but also by an external influential entity referred to as a marketer. The influential entity tries to sway the overall opinion to its own side by using a specific influence budget during discrete-time...

We consider infinite-horizon optimal control of nonlinear systems where the control actions are discrete, and focus on optimistic planning algorithms from artificial intelligence, which can handle general nonlinear systems with nonquadratic costs. With the main goal of reducing computations, we introduce two such algorithms that only search for con...

We consider the infinite-horizon optimal control of discrete-time, Lipschitz continuous piecewise affine systems with a single input. Stage costs are discounted, bounded, and use a 1 or ∞-norm. Rather than using the usual fixed-horizon approach from model-predictive control, we tailor an adaptive-horizon method called optimistic planning for contin...

We consider three problems for discrete-time switched systems with autonomous, general nonlinear modes. The first is optimal control of the switching rule so as to optimize the infinite-horizon discounted cost. The second and third problems occur when the switching rule is uncontrolled, and we seek either the worst-case cost when the rule is unknow...

We analyse the stability of general nonlinear discrete-time systems controlled by an optimal sequence of inputs that minimizes an infinite-horizon discounted cost. First, assumptions related to the controllability of the system and its detectability with respect to the stage cost are made. Uniform semiglobal and practical stability of the closed-lo...

Markov decision processes are a powerful framework for nonlinear, possibly stochastic optimal control. We consider two existing optimistic planning algorithms to solve them, which originate in artificial intelligence. These algorithms have provable near-optimal performance when the actions and possible stochastic next-states are discrete, but they...

Unmanned aerial vehicles (UAVs) have gained special attention in recent years, among others in monitoring and inspection applications. In this paper, a less explored application field is proposed, railway inspection, where UAVs can be used to perform visual inspection tasks such as semaphore, catenary, or track inspection. We focus on lightweight U...

This paper addresses the infinite-horizon optimal control problem for max-plus linear systems where the considered objective function is a sum of discounted stage costs over an infinite horizon. The minimization problem of the cost function is equivalently transformed into a maximization problem of a reward function. The resulting optimal control p...

This book focuses on two challenges posed in robot control by the increasing adoption of robots in the everyday human environment: uncertainty and networked communication. Part I of the book describes learning control to address environmental uncertainty. Part II discusses state estimation, active sensing, and complex scenario perception to tackle...

As the guest editors of the special issue, we would like to thank all the authors and reviewers for their contributions to our special issue.

Optimistic planning (OP) is a promising approach for receding-horizon optimal control of general nonlinear systems. This generality comes however at large computational costs, which so far have prevented the application of OP to the control of nonlinear physical systems in real-time. We therefore introduce an extension of OP to real-time control, w...

We consider the generalized flocking problem in multiagent systems, where the agents must drive a subset of their state variables to common values, while communication is constrained by a proximity relationship in terms of another subset of variables. We build a flocking method for general nonlinear agent dynamics, by using at each agent a near-opt...

Quadcopters are small-sized aerial vehicles with four fixed-pitch propellers. These robots have great potential since they are inexpensive with affordable hardware, and with appropriate software solutions they can accomplish assignments autonomously. They could perform daily tasks in the future, such as package deliveries, inspections, and rescue m...

In this work we consider a fleet of non-holonomic robots that has to realize a formation in a decentralized and collaborative manner. The fleet is clustered due to communication or energy-saving constraints. We assume that each robot continuously measures its relative distance to other robots belonging to the same cluster. Due to this, the robots c...

We investigate the stability of general nonlinear discrete-time systems controlled by an optimal sequence of inputs that minimizes an infinite-horizon discounted cost. We first provide conditions under which a global asymptotic stability property is ensured for the corresponding undiscounted problem. We then show that this property is semiglobally...

We consider infinite-horizon optimal control of nonlinear systems where the actions (inputs) are discrete. With the goal of limiting computations, we introduce a search algorithm for action sequences constrained to switch at most a given number of times between different actions. The new algorithm belongs to the optimistic planning class originatin...

Optimistic planning is an optimal control approach from artificial intelligence, which can be applied in receding horizon. It works for very general nonlinear dynamics and cost functions, and its analysis establishes a tight relationship between computation invested and near-optimality. However, there is no optimistic planning algorithm that search...

Unmanned aerial vehicles are increasingly being used and showing their advantages in many domains. However, their application to railway systems is very little studied. In this paper, we focus on controlling an AR.Drone UAV in order to follow the railway track. The method developed relies on vision-based detection and tracking of the vanishing poin...

Optimistic planning for deterministic systems (OPD) is an algorithm able to find near-optimal control for very general, nonlinear systems. OPD iteratively builds near-optimal sequences of actions by always refining the most promising sequence; this is done by adding all possible one-step actions. However, OPD has large computational costs, which mi...

An important challenge in multiagent systems is consensus, in which the agents must agree on certain controlled variables of interest. So far, most consensus algorithms for agents with nonlinear dynamics exploit the specific form of the nonlinearity. Here, we propose an approach that only requires a black-box simulation model of the dynamics, and i...

Reinforcement learning (RL) comprises an array of techniques that learn a control policy so as to maximize a reward signal. When applied to the control of elevator systems, RL has the potential of finding better control policies than classical heuristic, suboptimal policies. On the other hand, elevator systems offer an interesting benchmark applica...

Reinforcement learning (RL) allows agents to learn how to optimally interact with complex environments. Fueled by recent advances in approximation-based algorithms, RL has obtained impressive successes in robotics, artificial intelligence, control, operations research, etc. However, the scarcity of survey papers about approximate RL makes it diffic...