Tae Woong Yoon

Observer design in receding-horizon predictive control

This paper considers the robust implementation of a class of predictive control methods represented by GPC. Such controllers are in two-degrees-of-freedom form where there are dynamics in both the forward and the feedback paths. The ‘tuning knobs’ of predictive controllers determine the characteristic polynomial PC , and for a given PC the observer or prefiltering polynomial T in the feedback path determines the robustness of the closed loop. Previous intuitive guidelines on the selection of T are shown to be limited in their effectiveness. For an open-loop stable plant, a simple criterion is provided which allows the feedback dynamics to be specified so as to enhance robustness. The T polynomial is then chosen to satisfy this criterion. In addition, robust design through T is related to an H∞ -optimal control scheme using the so-called Q-parametrization. Despite its simplicity, the new proposed approach to the design of T is seen to result in robustness comparable with that obtained from the H∞ method.

Modeling and predictive control of a reheating furnace

Reheating furnaces in iron and steel industry are main facilities of hot charge rolling processes. The main objective of such a reheating furnace is to control billet temperature uniformly, thereby resulting in successful rolling process performance and high productivity. In this paper, a dynamic model of the reheating furnace is derived using material and energy balances. A multivariable controller design procedure is then presented on the basis of a system identification technique and a predictive control algorithm. Simulations show the effectiveness of the proposed scheme.

Speed-Sensorless DTC-SVM for Matrix Converter Drives With Simple Nonlinearity Compensation

This paper presents a new method to improve the sensorless performance of matrix converter drives using a parameter estimation scheme. To improve low-speed sensorless performance, the nonlinearities of a matrix converter drive such as commutation delays, turn-on and turn-off times of switching devices, and ON-state switching device voltage drop is modeled using p-q-r transformation and compensated using a reference current control scheme. To eliminate the input current distortion due to the input voltage unbalance, a simple method using p-q-r transformation is also presented. The proposed compensation method is applied for high performance induction motor drives using a 3-kW matrix converter system without a speed sensor. Experimental results are shown to illustrate the feasibility of the proposed strategy.

Adaptive predictive control of the benchmark plant

Abstract One of the main motives for adaptation is to design a high-performance controller for time-varying systems. The benchmark plant is a time-varying system whose behaviour is not known. This paper applies an adaptive predictive control scheme, based on CRHPC, to the benchmark plant using minimal prior information, and shows that the adaptive controller with appropriately chosen filters can cope well with time-variations.

Dynamic anti-windup scheme for feedback linearizable nonlinear control systems with saturating inputs

This paper proposes a dynamic compensation scheme for input-constrained feedback linearizable nonlinear systems to cope with the windup phenomenon. Given a dynamic feedback linearizing controller designed without considering its input constraint, an additional dynamic compensator is proposed to account for the constraint. This dynamic anti-windup is based on the minimization of a reasonable performance index. The proposed strategy is a nonlinear extended version of [Park, J.-K., & Choi, C.-H. (1995). Dynamic compensation method for multivariable control systems with saturating actuators. IEEE Transactions on Automatic Control, 40(9), 1635-1640] with simplified derivation of an optimization solution under relaxed assumptions. The parameter matrices and structure of the solution are explicitly decided by mathematical optimization for infinite horizon without tuning of design parameters unlike previous schemes. During input saturation, the role of the anti-windup scheme with the proposed dynamic feedback compensator is to maintain the controller states to be exactly the same as those without input saturation. The local asymptotic stability and the total stability of the resulting systems are proved. The usefulness of the proposed design method is illustrated by comparative simulations for a constrained control system.

Indirect adaptive nonlinear control of a pH process

This paper presents an indirect adaptive control scheme for a pH process, which is difficult to control due to its nonlinear dynamics with uncertainties. A nonlinear controller is designed using a backstepping technique, which can deal with a large class of nonlinear systems with parametric uncertainties. The nonlinear controller is combined with a parameter estimator to estimate the concentrations of the reaction invariants in the wastewater stream, and the stability of the proposed adaptive scheme is proved. Simulations show that uniform closed-loop performance is obtained despite the nonlinearitics and parametric uncertainties.

Input-to-state stable finite horizon MPC for neutrally stable linear discrete-time systems with input constraints

Abstract MPC or model predictive control is representative of control methods which are able to handle inequality constraints. Closed-loop stability can therefore be ensured only locally in the presence of constraints of this type. However, if the system is neutrally stable, and if the constraints are imposed only on the input, global asymptotic stability can be obtained; until recently, use of infinite horizons was thought to be inevitable in this case. A globally stabilizing finite-horizon MPC has lately been suggested for neutrally stable continuous-time systems using a non-quadratic terminal cost which consists of cubic as well as quadratic functions of the state. The idea originates from the so-called small gain control, where the global stability is proven using a non-quadratic Lyapunov function. The newly developed finite-horizon MPC employs the same form of Lyapunov function as the terminal cost, thereby leading to global asymptotic stability. A discrete-time version of this finite-horizon MPC is presented here. Furthermore, it is proved that the closed-loop system resulting from the proposed MPC is ISS (Input-to-State Stable), provided that the external disturbance is sufficiently small. The proposed MPC algorithm is also coded using an SQP (Sequential Quadratic Programming) algorithm, and simulation results are given to show the effectiveness of the method.

A research on the characteristics of fault current of DC distribution system and AC distribution system

In this paper, research on the comparison of the fault characteristics of the direct current (DC) distribution system and the alternating current (AC) distribution system has been conducted. Since the DC distribution system is a promising topology to be used in future smart distribution systems having high efficiency and reliability, it is expected that there would be an increase in the installation of DC distribution systems in houses and buildings. For the analysis, DC and AC networks to be analyzed are implemented using PSCAD/EMTDC. Simulation results show that the DC distribution system has several benefits compared to the regular AC distribution system such as improved voltage drop and power quality characteristics at the customer-end during a disturbance. This is because the DC/DC converters of DC distribution system can mitigate voltage fluctuations caused by disturbance and increase power quality.

Receding-horizon predictive control with exponential weighting

Exponential weighting of future tracking errors and control increments is employed for receding-horizon predictive control and seen to improve the dynamic behaviour of the closed-loop system. A sufficient condition for the asymptotic stability of generalized predictive control (GPC) with these weightings is derived. The condition can be easily satisfied whereas the corresponding condition for GPC with constant weighting is highly restrictive. In the case of constrained receding-horizon predictive control (CRHPC), a prescribed degree of stability is obtained just as with infinite-horizon optimal control using the same type of weighting. This makes it possible to use a simplified CRHPC law with no weighting on the tracking error but which guarantees convergence to the set-point faster than a bounding exponential

A reformulation of receding-horizon predictive control

Receding-horizon predictive control offers a practical approach to complex control problems where robustness and adaptability are required. Important theoretical developments have been reported, leading to many algorithms and acronyms. This paper seeks to provide an encompassing treatment of a broad range of predictive schemes. Some new results on stability are also obtained.