Formation control of road vehicles based on dynamic inversion and passivity

This paper proposes a hierarchical formation stabilization method for vehicles having nonlinear dynamics. Supposing that the formation control problem is already solved for the case of linear vehicle dynamics, the method proposes a dynamic inversion based low-level control, which linearizes, at least approximately, the original vehicle dynamics so that the formation control can be applied. In this way a hierarchical control system is obtained, which is then completed with a passivity based external stabilization procedure for the stability of the entire system can be guaranteed. The proposed algorithm is tested by simulation on a formation control problem of road vehicles

Robustness properties of the hierarchical passivity based formation control

In this paper the robustness analysis of the hierarchical formation stabilization control proposed by [7] is performed. The analysis is based on the nonlinear small gain theorem and exploits the strict passivity of the components in the closed loop dynamics. The computations are tested via a formation control problem of road vehicles

Model predictive control for the hybrid primary circuit dynamics of a pressurized water nuclear power plant^1

In this paper, a model predictive controller is developed for controlling the main primary circuit dynamics of pressurized water nuclear power plants during load-change transients. The hybrid model of the plant is successfully embedded into a non-hybrid discrete time LPV form. The designed controller is able to handle the hard constraints for the state and input variables while keeping the plant stable and producing satisfactory time-domain behavior

Network-level optimal control for public bus operation

The paper presents modeling, control and analysis of an urban public transport network. First, a centralized system description is given, built up from the dynamics of individual buses and bus stops. Aiming to minimize three conflicting goals (equidistant headways, timetable adherence, and minimizing passenger waiting times), a reference tracking model predictive controller formulated based on the piecewise-affine system model. The closed-loop system is analyzed with three methods. Numerical simulations on a simple experimental network showed that the temporal evolution of headways and passenger numbers could maintain their periodicity with the help of velocity control. With the help of randomized simulation scenarios, sensitivity of the system is analyzed. Finally, infeasible regions for the bus network control was sought using by formulating an explicit model predictive controller

Backflipping with Miniature Quadcopters by Gaussian Process Based Control and Planning

The paper proposes two control methods for performing a backflip maneuver with miniature quadcopters. First, an existing feedforward control approach is improved by finding the optimal sequence of motion primitives via Bayesian optimization, using a surrogate Gaussian Process model. To evaluate the cost function, the flip maneuver is performed repeatedly in a simulation environment. The second method is based on closed-loop control and it consists of two main steps: first a novel robust, adaptive controller is designed to provide reliable reference tracking even in case of model uncertainties. The controller is constructed by augmenting the nominal model of the drone with a Gaussian Process that is trained by using measurement data. Second, an efficient trajectory planning algorithm is proposed, which designs feasible trajectories for the flip maneuver by using only quadratic programming. The two approaches are analyzed in simulations and in real experiments using Bitcraze Crazyflie 2.1 quadcopters.Comment: Submitted to IEEE Transactions on Control Systems Technology (2022

Nonlinear analysis of vehicle control actuations based on controlled invariant sets

In the paper, an analysis method is applied to the lateral stabilization problem of vehicle systems. The aim is to find the largest state-space region in which the lateral stability of the vehicle can be guaranteed by the peak-bounded control input. In the analysis, the nonlinear polynomial sum-of-squares programming method is applied. A practical computation technique is developed to calculate the maximum controlled invariant set of the system. The method calculates the maximum controlled invariant sets of the steering and braking control systems at various velocities and road conditions. Illustration examples show that, depending on the environments, different vehicle dynamic regions can be reached and stabilized by these controllers. The results can be applied to the theoretical basis of their interventions into the vehicle control system