Conventional Predictive Control Research Articles

An Immune System-Inspired Reconfigurable Controller

Based on the biological immune mechanism, a design approach for the immune reconfigurable controller (IRC) is proposed. Using four units to imitate the immune system’s surveillance process, response process, memory mechanism, and self-learning process, respectively, the IRC is capable of actuator fault detection and fault-tolerant control for multi-input multioutput systems. Meanwhile, in order to further improve the control performance, an online optimization process with the multiobjective clonal selection algorithm is designed. To verify its effectiveness, the IRC is applied to the coagulation bath of polyacrylonitrile carbon fiber production line. Comparison experiments with conventional PID and reconfigurable model-based predictive controller control schemes are conducted. The simulation results demonstrate that the IRC can rapidly eliminate the fluctuation due to the actuator fault and guarantees the stability of the coagulation bath. In addition, the IRC has the ability of quick response to the same failure as well as unknown faults.

State-Space Predictive-P Control for Liquid Level in an Industrial Coke Fractionation Tower

In this study, a predictive-p control system is developed for the level process in an industrial coke fractionation tower. Such processes typically have integrating and nonlinear dynamics causing the performance of conventional control designs and tuning to be poor or to require significant effort in practice. The process model is derived using data of step-response test and control implementation is designed through a new state-space structure. The closed-loop control system contains both the improved predictive control and P control. The performance of the proposed control for regulatory/servo, disturbance rejection, and measurement noise problems are studied and the obtained results show that the control system is of simple implementation with more robustness and provides better responses than conventional predictive control.

Model predictive control strategy for smooth path tracking of autonomous vehicles with steering actuator dynamics

Path tracking control is one of the most important functions for autonomous driving. In path tracking control, high accuracy and smooth tracking are required for safe and comfort driving. In order to meet these requirements, model predictive control approaches, which can obtain an optimized solution with respect to a predefined path, have been widely studied. Conventional predictive controllers have been studied based on a simple bicycle model. However, the conventional predictive controllers have a performance limitation in practical challenges due to the difference between the simple bicycle model and the actual vehicle. To overcome this limitation, the actuator dynamics of the steering system should be incorporated into the control design. In this paper, we propose a model predictive control based path tracking control algorithm to achieve the accurate and smooth tracking by incorporating the dynamic characteristics of the steering actuation system. In the proposed control algorithm, an optimal trajectory of the steering command is calculated by applying a quadratic programming optimization method. The proposed controller was verified by computer simulation with various driving scenarios. The simulation results show that the proposed controller can improve the tracking performance.

Finite control set predictive control based on Lyapunov function for three‐phase voltage source inverters

A Lyapunov-function-based technique for finite control set predictive control is proposed to control the load currents of three-phase voltage source inverters (VSIs). The developed technique, based on a discrete-time model of a VSI, determines a control law using the Lyapunov function. Based on the Lyapunov stability analysis considering inevitable quantisation errors between the proposed control law and control actions selected from the inherent finite control set of the VSI, all signals of the closed-loop dynamics are uniformly ultimately bounded and the current control errors converge to a neighbourhood of the origin. In addition to rendering the finite control set predictive control system globally stable, the proposed Lyapunov-function-based finite control set predictive control reduces the amount of calculations required to predict a future variable by half compared with the conventional finite control set predictive control, resulting in lower actuation time delay. Experimental results with three-phase VSIs are presented to validate the proposed Lyapunov-function-based control method.

MPC enhancement for tracking of complex profiles — The basic technique

Abstract This paper develops a unique approach that can be imposed on conventional predictive control schemes to provide tighter control when tracking complex setpoint trajectories. The method introduces a correction parameter η , independent of plant gain, evaluated online at each control timestep to drive the plant output to its reference more accurately than the original schemes. The strategy was applied to various systems that possess deadtime, higher order and multivariable characteristics with improved closed loop control. This is evidenced by significant reductions of mean square errors when using η . The strategy was applied on a nonlinear system and a practical setup, with reduced tracking error.

Predictive Direct Power Control for Three-Phase Grid-Connected Converters Without Sector Information and Voltage Vector Selection

In this paper, a predictive direct power control (DPC) method without sector information and voltage vector selection is proposed for three-phase grid-connected converters. Different from conventional predictive direct power controllers, the proposed strategy always adopts fixed voltage vectors, instead of selecting voltage vectors according to the angular information of the grid-voltage vector or the virtual-flux vector. The converter switching time is obtained by minimizing the squared errors of the instantaneous active and reactive power. Switch duty cycles are then calculated and equivalently reconstructed. The proposed strategy not only presents rapid dynamic response due to the use of the predictive controller, but also possesses excellent steady-state performance as a result of duty cycle reconstruction. The main advantage of the proposed control scheme, compared with the classical DPC strategies, is that it is unnecessary to use sector information of the grid-voltage vector, and selection of active voltage vectors is also not required here. Therefore, incorrect selection of voltage vectors and the resulting performance deterioration are surely avoided, without the need of any additional compensation measures. An in-depth comparison and experimental assessment are given to validate the effectiveness of the proposed strategy.

A New Model-Free Predictive Control Method Using Input and Output Data

The purpose of this paper is to present a new predictive control utilizing online data and stored data of input/output of the controlled system. The conventional predictive control methods utilize the mathematical model of the control system to predict an optimal future input to control the system. The model is usually obtained by a standard system identification method from the measured input/output data. The proposed method does not require the mathematical model to predict the optimal future control input to achieve the desired output. This control strategy, called just-in-time, was originally proposed by Inoue and Yamamoto in 2004. In this paper, we proposed a simplified version of the original just-in-time predictive control method.

LMI-Based Multi-model Predictive Control of an Industrial C3/C4 Splitter

In this paper, the robust Model Predictive Control (MPC) of systems with model uncertainty is addressed. The robust approach usually involves the inclusion of nonlinear constraints to the optimization problem upon which the controller is based. At each time step the sequence of control actions is then calculated through the resolution of a NonLinear Programming problem, which can be too computer demanding for high dimension systems. Here, the conventional Multi-model Predictive Control (MMPC) problem is re-casted as an LMI-based problem that can be solved with a lower computational cost. The conventional and LMI-based robust controllers’ performances and computational costs are compared through simulations of the control of an industrial C3/C4 splitter.

토크예측제어를 이용한 매입형 영구자석 동기전동기의 토크리플저감기법

In this paper, a new torque predictive control method of interior permanent magnet synchronous motor is developed based on an extended rotor flux. Also, a duty ratio prediction method is proposed and allows the duty ratio of the active stator voltage vector to be continuously calculated. The proposed method makes it possible to relatively reduce the torque ripple under the steady state as well as to remain the good dynamic response in the transient state. With the duty ratio prediction method, the magnitude and time interval of the active stator voltage vector applied can be continuously controlled against the varying operation conditions. This paper shows a comparative study among the switching table direct torque control(DTC), the SVM-DTC, conventional torque predictive control, and the proposed torque predictive control. Simulation results show validity and effectiveness of this work.

Offset-Free Predictive Control for Variable Speed Wind Turbines

Wind turbine aerodynamics is known as a nonlinear and stochastic system; hence, a high-performance requirement of tight optimal regime tracking is beyond the capability of the conventional linear PI controller. On the other hand, the linear model of the nonlinear wind turbine aerodynamics at a specific operating point produces a mismatch between plant and model which is the main reason for reference tracking offset seen in a conventional predictive controller. This paper presents a new predictive control approach for variable speed wind turbines in order to guarantee maximum power captured under the rated wind speed. The proposed algorithm, offset-free predictive control (OF-PC), has been exploited to provide zero error tracking of the nonstationary optimal speed trajectory. The control scheme attempts to add additional output disturbance signals to the linear presentation of wind turbine aerodynamics together with an appropriate observer design to lump the plant-model mismatches and/or unmodeled disturbances. The simulation and experiment results demonstrate the effectiveness of the control method.

A Predictive Traffic Controller for Sustainable Mobility Using Parameterized Control Policies

We present a freeway-traffic control strategy that continuously adapts traffic control measures to prevailing traffic conditions and features faster computation speed than conventional model-based predictive control (MPC). The control approach is based on the principles of state feedback control and MPC. Instead of computing the control input sequence, the proposed controller optimizes the parameters of control laws that parametrize the control input sequences. This way, the computational burden of the controller is substantially reduced. We demonstrate the proposed control approach on a calibrated model of part of the Dutch A12 freeway using variable speed limits and ramp-metering rate.

Predictive Functional Control-Based Missile Autopilot Design

Predictive functional control is applied to design a missile autopilot considering control constraints. The missile nonlinear dynamics are first transformed into a linear structure with state-dependent coefficient matrices. At each sampling instant, the internal state-space model for prediction is obtained through a normal discretization procedure. Based upon this model, a predictive functional controller for the nonlinear missile autopilot is proposed. Compared with the conventional predictive control algorithms, which usually involve an online quadratic programming in the practical implementation, the new controller demands less online computation resources. Simulation results validate the effectiveness and robustness of the proposed algorithm.

Robust Predictive Current Controller Based on a Disturbance Estimator in a Three-Phase Grid-Connected Inverter

This paper proposes a robust predictive current controller based on a disturbance estimator for a three-phase grid-connected inverter. The various components that deteriorate the performance of a conventional predictive current controller are regarded as disturbances, and a disturbance estimator is constructed using an inverter output voltage and current values. Furthermore, a grid angle is extracted using a phase-locked loop (PLL) with the estimated reactive component of the disturbance; therefore, sensorless grid voltage control is possible. In order to determine the relevant gains in the disturbance estimator, the frequency components corresponding to the unbalanced grid condition and low-order harmonics, as well as the PLL bandwidth, are considered. Moreover, the stability of the disturbance estimator due to parameter errors in the inductor filter is analyzed. The proposed method has an inherent rapid dynamic response due to the conventional predictive current controller, as well as low-cost implementation and robust control performance with regard to the disturbance and noise interference due to use of the combined estimation algorithm. The feasibility of the proposed predictive current control strategy is verified through the examination of experimental results.

Plant input-mapping-based predictive control of systems through band-limited networks

A new control method for network-based control systems, with guaranteed closed-loop stability, is proposed. The method substantially enhances the conventional networked model-based predictive control (NMBPC) schemes. In conventional NMBPC methods, associated with a pre-specified range of possible time delays, a vector of stabilising control signals is determined and transmitted to the plant side of the network, where, based on the actually occurred time delay, just one entry of the control vector is selected and applied to the plant. In practice, stability issues may arise when the network time delay is not small enough. A modified method, using the plant-input mapping (PIM) discretisation technique, with the possibility of guaranteeing the closed-loop stability is introduced. In order to alleviate the deficiency of some existing transfer function-based methods, which implicitly assume a non-realistic zero initial condition at the outset of every sampling instant, a state-space representation is proposed. Simulation studies on well-known benchmark problems demonstrate the effectiveness of the proposed PIM-based NMBPC method.

Efficient algorithms for trading off feasibility and performance in predictive control

This article introduces two simple modifications to conventional predictive control algorithms with the aim of enlarging the feasible regions without increasing computational complexity. It is shown that despite the relatively large feasibility gains, the loss in performance may be far smaller than expected and thus the algorithms give mechanisms for achieving low computational loads with good feasibility and good performance while using a simple algorithm set-up. Both algorithms have standard convergence and feasibility guarantees.

Predictive-space vector PWM current control method for asymmetrical dual three-phase induction motor drives

The interest in multiphase drives has re-emerged in the last decade, being the asymmetrical dual three-phase induction motor drive one of the most popular options. Predictive control techniques, already implemented in three-phase drives, have been recently adapted to the multiphase case. Schemes proposed so far have demonstrated high performance at the expenses of a higher degree of computational cost and ill-defined switching frequency. In this study, a predictive space vector PWM (SVPWM) current control technique with fixed switching frequency is proposed for asymmetrical dual three-phase AC drives. Fast torque and current response are achieved similar to those obtained using conventional predictive current control techniques. Electrical noise suppression is favoured as in PWM current control methods. Experimental results are provided to examine the benefits of the proposed control method.

Modeling and predictive control using fuzzy logic: Application for a polymerization system

AbstractIn this study, a predictive control system based on type Takagi‐Sugeno fuzzy models was developed for a polymerization process. Such processes typically have a highly nonlinear dynamic behavior causing the performance of controllers based on conventional internal models to be poor or to require considerable effort in controller tuning. The copolymerization of methyl methacrylate with vinyl acetate was considered for analysis of the performance of the proposed control system. A nonlinear mathematical model which describes the reaction plant was used for data generation and implementation of the controller. The modeling using the fuzzy approach showed an excellent capacity for output prediction as a function of dynamic data input. The performance of the projected control system and dynamic matrix control for regulatory and servo problems were compared and the obtained results showed that the control system design is robust, of simple implementation and provides a better response than conventional predictive control. © 2009 American Institute of Chemical Engineers AIChE J, 2010

Adaptive extended state space predictive control for a kind of nonlinear systems

This paper presents an adaptive nonlinear predictive control design strategy for a kind of nonlinear systems with output feedback coupling and results in the improvement of regulatory capacity for reference tracking, robustness and disturbance rejection. The nonlinear system is first transformed into an equal time-variant system by analyzing the nonlinear part. Then an extended state space predictive controller with a similar structure of a PI optimal regulator and with P -step setpoint feedforward control is designed. Because changes of the system state variables are considered in the objective function, the control performance is superior to conventional state space predictive control designs which only consider the predicted output errors. The proposed method is tested and compared with latest methods in literature. Tracking performance, robustness and disturbance rejection are improved.

A Digital Predictive Current Control With Improved Sampled Inductor Current for Cascaded Inverters

This paper proposes a current-control technique with a predictive control method for multilevel inverters. The proposed current-control method adopts a variable sampling point scheme for inductor current sensing. It is able to avoid the switching noise caused by the turn-on or turn-off of power devices. The measured value of the inductor current is used to estimate the inverter output voltage at the next switching period with a simple linear extrapolation to force the output current to follow the current reference. Compared to the conventional predictive current controllers, the proposed control features are as follows: (1) only the inductor current measurement is required, and it is able to achieve a cost-effective and less-complex circuit (the output voltage and current measurements are required for a conventional controller) and (2) the proposed variable sampling technique is designed to avoid the switching noise, thus a robust current-control system can be achieved. Finally, a seven-level cascaded inverter with three DC-AC converter modules was constructed and tested. Analysis, simulations, and experimental results are presented to demonstrate the superiority of the proposed system.

Fault-Tolerant Subspace Predictive Control Applied to a Boeing 747 Model

This paper presents a fault-tolerant control system based on a combination of predictive control and subspace identification called subspace predictive control. In this control method, the mathematical model used by conventional predictive controllers to predict the future output is replaced by a subspace predictor. Because the subspace predictor is continuously updated in a closed-loop setting based on new input-output data, it can naturally adapt the controller after a fault has occurred. This property is very useful for fault-tolerant control because faults might be unanticipated. A novel feature of the presented subspace predictive control algorithm is that the predictor is recursively updated in a computationally efficient way. A multiple-model fault classification scheme is used to distinguish between anticipated and unanticipated faults. For anticipated faults the control system is configured such that it can accommodate these faults more quickly. The proposed fault-tolerant control system is evaluated on a detailed model of a Boeing 747.