Cristina Stoica Maniu

Voronoi based decentralized coverage problem: From optimal control to Model Predictive Control

This paper presents a novel decentralized framework for the Multi-Agent dynamical coverage problem subject to anti-collision constraints. The control objective is to authorize each agent operating strictly in its safety zone and then enhance the coverage. These zones are the result of a spatial Voronoi partition of the common working space of the Multi-Agent system based on the current positions of the agents. Each zone provides the local information to design the control policies that make each agent converging to a fixed point inside its Voronoi cell. The performance/effectiveness of the proposed techniques will be demonstrated via numerical examples.

Predictive Control for Path-Following. From Trajectory Generation to the Parametrization of the Discrete Tracking Sequences

This chapter discusses a series of developments on predictive control for path following via a priori generated trajectory for autonomous aerial vehicles. The strategy partitions itself into offline and runtime procedures with the assumed goal of moving the computationally expensive part into the offline phase and of leaving only tracking decisions to the runtime. First, it will be recalled that differential flatness represents a well-suited tool for generating feasible reference trajectory. Next, an optimization-based control problem which minimizes the tracking error for the nonholonomic system is formulated and further enhanced via path following mechanisms. Finally, possible changes of the selection of sampling times along the path and their impact on the predictive control formulation will be discussed in detail.

Discretized optimal control approach for dynamic multi-agent decentralized coverage

This paper presents a novel discrete-time decentralized control law for the Voronoi-based self-deployment of a Multi-Agent dynamical system. The basic control objective is to let the agents deploy into a bounded convex polyhedral region and maximize the coverage quality by computing locally the control action for each agent. The Voronoi tessellation algorithm is employed to partition dynamically the deployed region and to allocate each agent to a corresponding bounded functioning zone at each time instant. The control synthesis is then locally computed based on an optimal formulation framework related to the Lloyds algorithm but according to the discrete-time agents dynamics equation. The performance of the discretized optimal solution will be demonstrated via an illustrative example.

State estimation of an octorotor with unknown inputs. Application to radar imaging

This paper focuses on the design of a linear Kalman filter and an extended Kalman filter for the estimation of an octorotor unmanned aerial vehicles (UAV) state in the context of Synthetic Aperture Radar image reconstruction. A comparison to a linear interpolation method is also proposed. The Kalman filters are developed based on a complete nonlinear model of the UAV and its linearized form. A particularity of the considered platform is that the control signals are not measured and have to be estimated as well as the UAVs state. The proposed techniques are then tested on a UAV simulator and a radar imaging simulator.

Formation Reconfiguration Using Model Predictive Control Techniques for Multi-agent Dynamical Systems

The classical objective for multiple agents evolving in the same environment is the preservation of a predefined formation because it reinforces the safety of the global system and further lightens the supervision task. One of the major issues for this objective is the task assignment problem, which can be formulated in terms of an optimization problem by employing set-theoretic methods. In real time the agents will be steered into the defined formation via task (re)allocation and classical feedback mechanisms. The task assignment calculation is often performed in an offline design stage, without considering the possible variation of the number of agents in the global system. These changes (i.e., including/excluding an agent from a formation) can be regarded as a typical fault, due to some serious damages on the components or due to the operator decision. In this context, the present chapter proposes a new algorithm for the dynamical task assignment formulation of multi-agent systems in view of real-time optimization by including fault detection and isolation capabilities. This algorithm allows to detect whether there is a fault in the global multi-agent system, to isolate the faulty agent and to integrate a recovered/healthy agent. The proposed methods will be illustrated by means of a numerical example with connections to multi-vehicle systems.

Stability Analysis by means of Discrete Abstraction. Application to Voltage Stability of Distributed Generators

This paper proposes a stability analysis method for discrete-time piecewise affine systems based on the reachability study of the equivalent discrete abstraction. The finite discrete abstraction eases reachability analysis but does not allow to conclude on system instability. To be able to conclude, this abstraction is refined by bisimulation calculation until the system is found to be unstable or the discrete abstraction is found to be equivalent to the system. This method allows to conclude both on the stability and the instability of the system, avoiding the undecidable situations. This approach is illustrated on voltage stability of a distribution feeder hosting distributed generators equipped with piecewise affine control laws.