Giovanni Palmieri

LTV-MPC for Yaw Rate Control and Side Slip Control with Dynamically Constrained Differential Braking

In this paper a novel vehicle lateral dynamic control approach is presented. A differential braking control law based on vehicle planar motion has been designed using a two-degrees-of-freedom vehicle model. On the basis of the estimate of tire longitudinal forces we estimate the range of lateral forces which the tire can exert. Using this constraints a model predictive control (MPC) based on a two-track model is designed in order to stabilize the vehicle. The performances are estimated comparing the results with standard manoeuvers. Simulation results show the benefits of the control methodology used: in particular we show how very effective distribution of braking torque are obtained as a result of this feedback policy.

A preliminary study on the effects of roll dynamics in predictive vehicle stability control

A model predictive control (MPC) -based approach is presented for autonomous path following via active front steering (AFS). We start from the nonlinear MPC (NMPC) problem formulations in [2] and [4], where a simple bicycle model is used, and reformulate the same problem by using a more complex vehicle model including roll dynamics. We present and discuss simulation results of a vehicle autonomously performing high speed double lane change maneuvers, where load transfer effects due to roll dynamics become relevant. The results demonstrate that the inclusion of the roll dynamics in the prediction model of the MPC controller significantly improves the vehicle behavior at high speed on high friction surfaces, when significant lateral load transfers occur.

Robust vehicle lateral stabilisation via set-based methods for uncertain piecewise affine systems

The paper presents the design of a lateral stability controller for ground vehicles based on front steering and four wheels independent braking. The control objective is to track yaw rate and lateral velocity reference signals while avoiding front and rear wheel traction force saturation. Control design is based on an approximate piecewise-affine nonlinear dynamical model of the vehicle. Vehicle longitudinal velocity and drivers steering input are modelled as measured disturbances taking values in a compact set. A time-optimal control strategy which ensures convergence into a maximal robust control invariant (RCI) set is proposed. This paper presents the uncertain model, the RCI computation, and the control algorithm. Experimental tests at high-speed on ice with aggressive driver manoeuvres show the effectiveness of the proposed scheme.

A preliminary study to integrate LTV-MPC lateral vehicle dynamics control with a slip control

In this paper we present the integration of a Linear-Time-Varying Model-Predictive-Control (LTV-MPC), designed to stabilize a vehicle during sudden lane change or excessive speed-entry in curve, with a slip controller that converts the desired longitudinal tire force variation in pressure variation in the brake system. The lateral controller is designed using a three-degrees-of-freedom vehicle model taking into account both yaw rate and side slip angle of vehicle while the slip controller is a nonlinear gain scheduling P with feedforward action. The performances are validated with SIL technique, in particular, the authors use a proprietary simulator calibrated on an oversteering sport commercial car. Simulation results show the benefits of the control methodology used.

Optimal Power Flow model with energy storage, an extension towards large integration of renewable energy sources.

Abstract The integration of renewable energy sources (RES) into modern electrical grids contributes to satisfying the continuously increasing energy demand. This can be done in a sustainable way since renewable sources are both inexhaustible and non-polluting. Different renewable energy devices, such as wind power, hydro power, and photovoltaic generators are available nowadays. The main issue with the integration of such devices is their irregular generation capacity (in particular for wind and solar energy). Therefore energy storage units are used to mitigate the fluctuations during generation and supply. In this paper we formulate a model for the Alternate Current Optimal Power Flow (ACOPF) problem consisting of simple dynamics for energy storage systems cast as a finite-horizon optimal control problem. The effect of energy storage is examined by solving a Norwegian demo network. The simulation results illustrate that the addition of energy storage, along with demand based cost functions, significantly reduces the generation costs and flattens the generation profiles.

Model Predictive Control for Optimization of Combined Heat and Electric Power Microgrid

Abstract This is a contribution to the economic dispatch problem of combined electrical and heat power microgrids. A mixed integer linear microgrid model has been developed; the microgrid operations optimization problem has been formulated using Mixed-Integer Linear Programming and Model Predictive Control technique has been applied to take system uncertainties into account. The proposed optimization algorithm has been applied to a tertiary site microgrid, located in Finland; the obtained numerical results have been compared with a heuristic algorithm.

Robust vehicle lateral stabilization via set-based methods for uncertain piecewise affine systems: Experimental results

The paper presents the design of a lateral stability controller for ground vehicles based on front steering and four wheels independent braking. The control objective is to track yaw rate and lateral velocity reference signals while avoiding front and rear wheel traction force saturation. Control design is based on an approximate piecewise-affine nonlinear dynamical model of the vehicle. Vehicle longitudinal velocity and driver steering input are modeled as measured disturbances taking values in a compact set. We use a time-optimal control strategy which ensures convergence into a maximal robust control invariant set. This paper presents the controller experimental results on a vehicle equipped with active front steering and differential braking. In particular, tests at high-speed on ice with aggressive driver maneuvers show the effectiveness of the proposed scheme.

An Integrated LTV-MPC Lateral Vehicle Dynamics Control: Simulation Results

In this work we present the integration of a Linear-time-varying Model-predictive-control (LTV-MPC), designed to stabilize a vehicle during sudden lane change or excessive entry-speed in curve, with a slip controller that converts the desired longitudinal tire force variation to pressure variation in the brake system. The lateral controller is designed using a 3DOF vehicle model taking into account both yaw rate and side slip angle of vehicle while the slip controller is a gain scheduled proportional controller with feedforward action. The performances are validated through simulation: in particular, the authors use a proprietary simulator calibrated on an oversteering sport commercial car and commercial simulator calibrated on a standard light car. Simulation results show the benefits of the control methodology in that very effective steering manoeuvres can be obtained as a result of this feedback policy while satisfying input constraints and show the importance of the introduction of inputs constraints in the control strategy design.

A Cyber-Physical Systems Approach for Implementing the Receding Horizon Optimal Power Flow in Smart Grids

Two major challenges in securing reliable Optimal Power Flow (OPF) operations are: (i) fluctuations induced due to renewable generators and energy demand, and (ii) interaction and interoperability among the different entities. Addressing these issues requires handling both physical (e.g., power flows) and cyber aspects (computing and communication) of the energy grids, i.e, a cyber-physical systems (CPS) approach is necessitated. First, this investigation proposes a receding horizon control (RHC) based approach for solving OPF to deal with the uncertainties. It uses forecasts on renewable generation and demand and an optimization model solving a predictive control problem to secure energy balance while meeting the network constraints. Second, to handle the interoperability issues, a middleware using common information model (CIM) for exchanging information among applications and the associated profiles are presented. CIM profiles modelling various components and aspects of the RHC based OPF is proposed. In addition, a middleware architecture and services to collect information is discussed. The proposed CPS approach is illustrated in a distribution grid in Steinkjer, Norway having 85 nodes, 700 customers, three hydrogenerators, and various industrial loads. Our results demonstrate the benefits of CPS approach to implement OPF addressing also the interoperability issues.

A HIERARCHICAL CONTROL STRATEGY FOR ENERGY OPTIMIZATION

Abstract A new hierarchical and adaptive control strategy, integrating the heating and lighting systems, is proposed combining the classical control techniques, such as LQ optimal control and PI regulator, with fuzzy logic rules. The strategy is aimed at guaranteeing the satisfaction of comfort objectives, both thermal and lighting, by using the minimum amount of energy. The main idea is to maximize (in winter) or reject (in summer) solar gain by acting on window blind and avoiding glare on working plane. The effectiveness of the strategy has been tested by using a purposely designed simulator, developed in Java language and Matlab/Simulink environment. It is chosen the simulation environment to test the benefits of optimization strategy and in future in the Graces laboratory will designed an hardware demonstrator.