In this article we have shown that receding horizon control offers a straightforward method for designing feedback controllers that deliver good performance while respecting complex constraints. A designer specifies the RHC controller by specifying the objective, constraints, prediction method, and horizon, each of which has a natural choice suggested directly by the application. In more traditional approaches, such as PID control, a designer tunes the controller coefficients, often using trial and error, to handle the objectives and constraints indirectly. In contrast, RHC con trollers can often obtain good performance with little tuning. In addition to the straightforward design process, we have seen that RHC controllers can be implemented in real time at kilohertz sampling rates. These speeds are useful for both real-time implementation of the controller as well as rapid Monte Carlo simulation for design and testing purposes. Thus, receding horizon control can no longer be considered a slow, computationally intensive policy. Indeed, RHC can be applied to a wide range of control problems, including applications involving fast dynamics.

/pdf/receding-horizon-control-52ph45wpm9.pdf

Receding Horizon Control

I. Introduction: Transcending Nationalism: The European Community 1958-2000

Dwarf Mongoose Optimization Algorithm

IEEE International Conference on Fuzzy Systems

This article applies an incremental online identification algorithm to develop a set of evolving fuzzy models (FMs) that characterize the nonlinear finger dynamics of the human hand for the myoelectric (ME)-based control of a prosthetic hand. The FM inputs are the ME signals obtained from eight ME sensors and past inputs and/or outputs. The FM outputs are the finger angles, considered here as the midcarpal joint angles, to ensure their control. The best evolving FMs that characterize each of the five fingers are described with the results validated on real data. Simple second-order linear models are next given to enable the cost-effective controller design. Five separate control loops are proposed, with proportional–integral (PI) controllers separately tuned by a frequency-domain approach. Simple PI-fuzzy controllers are designed starting with the linear PI controllers to ensure the control system performance improvement. The evolving FMs are used to simulate accurately the behavior of the human hand. Digital simulation results are included to show the effectiveness of the PI-fuzzy controllers and the performance improvement in comparison to the initial PI ones.

Evolving Fuzzy Models for Prosthetic Hand Myoelectric-Based Control

Iterative Feedback and Learning Control. Servo systems applications

Hybrid electric vehicles (HEVs) gain more and more attention, as they represent a more environmental friendly alternative to conventional vehicles. If combined with photovoltaic panels, they can lead to further reduction of emissions. The paper focuses on a solution for minimizing the fuel consumption in a series hybrid solar vehicle (HSV). After briefly introducing the model, first a global optimum in fuel consumption is presented using dynamic programming, as a reference value. As a real-time control strategy, model predictive control (MPC) is considered. A fuel consumption equivalent quantity is defined which is used for calculating the fuel needed to bring the battery state of charge to a starting value (set in this case for 0.7 in relative units). For different values of the MPC tuning parameters simulations are performed using the urban section of the New European Drive Cycle. Conclusions based on the simulations are presented.

/pdf/control-solutions-for-hybrid-solar-vehicle-fuel-consumption-1ovexmu158.pdf

Control Solutions for Hybrid Solar Vehicle Fuel Consumption Minimization

The paper presents some main aspects regarding multi-parametric quadratic programming (mp-QP) problems. Model Predictive Control (MPC) is considered as a particular mp-QP problem, and this powerful tool is applied for control and simulation through a case study. Since the solutions to mp-QP problems can be expressed as piecewise affine linear functions of the state, a new implementation in terms of adaptive network- based fuzzy inference systems is proposed. The presentation is focused on the double integrator plant as a frequently appearing case study (electrohydraulic servo-system).

Use of Multi-parametric Quadratic Programming in Fuzzy Control Systems

The invention relates to a solar thermal power plant (1) comprising a solar collector steam generator unit (2) for generating steam, a solar collector steam superheater unit (4), downstream of the solar collector steam generator unit (2), for superheating the steam, and a steam turbine (40) which is connected to an outlet of the solar collector steam superheater unit (4) via a steam conduit system (13), superheated steam being supplied to the steam turbine when in use The solar thermal power plant (1) comprises an intermediate storage (20) which is connected to the steam conduit system (13) at least in a first high-temperature storage connecting point (HA1) interposed between the solar thermal steam superheater unit (4) and the steam turbine (40) to remove steam superheated in a storage mode from the steam conduit system (13) and which comprises a heat reservoir (22, 23, 24) in which thermal energy is drained from the steam fed into during the storage mode and is accumulated and the stored thermal energy is given off to the steam in an extraction mode, said steam being fed to the steam conduit system (13) from the intermediate storage (20) The intermediate storage is connected to a condenser (65) and/or a relaxation device (89) of the solar thermal power plant (1) in a low-temperature storage connecting point (NA3) The invention also relates to a method for operating said solar thermal power plant (1)

Solar thermal power plant and method for operating a solar thermal power plant

In this paper a hybrid electric vehicle is considered, which contains both an internal combustion engine and an electric motor (EM). Without focusing on the other components of the vehicle, the EM is treated in detail, both regarding modelling aspects and control solutions. After a brief modelling of the plant, two cascade speed control solutions are presented: first a classical PI+PI cascade control solution is presented. The control systems related to traction electric motors (used in vehicle traction) must be able to cope with different requests, such as variation of the reference signal, load disturbances which depend on the transport conditions and parametric disturbances regarding changes in the total mass of the vehicle. For this purpose, in the design of the speed controller (external loop) a specific methodology based on extension of the symmetrical optimum method is presented. The controllers are developed using the Modulus-Optimum method for the inner loop, and the Extended Symmetrical Optimum Method, corrected based on the 2p-SO-method, for the outer loop (for a more efficient disturbance rejection). In order to force the behaviour of the system regarding the reference input, a correction term is introduced as a non-homogenous structured PI controller solution. Simulations were performed using numerical values taken from a real application consisting in a hybrid vehicle prototype, showing satisfactory behaviour.

Zsuzsa Preitl

Papers

Iterative Feedback and Learning Control. Servo systems applications

Control Solutions for Hybrid Solar Vehicle Fuel Consumption Minimization

Use of Multi-parametric Quadratic Programming in Fuzzy Control Systems

Solar thermal power plant and method for operating a solar thermal power plant

Cascade Control Solution for Traction Motor for Hybrid Electric Vehicles