Robust Predictive Control Scheme Research Articles

Robust predictive control strategy for grid‐connected inverters with ultra‐local model based on linear matrix inequality

AbstractTo address the issue of poor robustness in the model predictive control of grid‐connected inverters due to disturbances in load model parameters, this article developed an ultra‐local model robust predictive controller based on Linear Matrix Inequality (LMI). First, to avoid dependence on model parameters, an ultra‐local model of the system was constructed. Second, in the process of setting the state variable compensation gain, to simplify the complex computational steps of the traditional Lyapunov algorithm, the task of finding the gain that satisfies the Lyapunov stability condition was ingeniously transformed into an optimization problem of solving constrained LMI. Third, the optimized state variables are utilized to design a new cost function predictive model, thereby enhancing the precision of system control. Finally, the effectiveness of the proposed approach has been validated through simulations and experiments.

A robust predictive control scheme for uncertain continuous‐time delayed systems under actuator saturation

AbstractThis paper concentrates on synthesizing robust predictive controllers in uncertain models with time‐delay and saturated input. To handle the complexities simultaneously and reach the desired objective, a state‐feedback structure with unknown gains is considered the compensator. Then, to specify the instantaneous values of the regulator's gains, an optimization issue will be derived relying on linear matrix inequality. Accordingly, in uncertain continuous‐time systems with some constraints and time‐delays, the controller's coefficients would be found in an online way from such an optimization. Numerous continuous‐time simulations are numerically performed to reveal the merit and robustness of the suggested methodology over similar techniques.

Robust predictive control strategy of SCIG-based drive system

This article presents a robust finite control set predictive scheme for a stand-alone squirrel cage induction generator (SCIG) drive. This technique is considered an alternative to the drive system due to the inclusion of system nonlinearities and fast dynamic response. The control objective in the distributed generation environment is to fix the output voltage to follow the stand-alone requirement. The strategy establishes optimized switching instants for cost function minimization for both source and load converter control and diminished cross-coupling amid active and reactive power during transient scenarios. The scheme is designed to achieve the minimal effect caused by the parameter uncertainties. During source and load changes, this work will also address the maintenance of dc-link voltage, machine, and load variables at the set value, supported by machine and load-end converter control to achieve stand-alone load objectives. In addition, the presented scheme is also tested with random variation of speed to check the efficacy of the control configuration. The drive performance is evaluated by simulation using MATLAB/Simulink environment. Comprehensive real-time findings obtained from a scaled laboratory test bench using dSPACE-1104 are provided to verify the feasibility of the predictive solution.

Virtual unmodeled dynamic and data-driven nonlinear robust predictive control

This study presents a novel approach for controlling an industrial process that exhibits uncertainty and significant nonlinear features. The proposed method utilizes a virtual unmodeled dynamic and data-driven nonlinear robust predictive control strategy. The representation of a controlled object involves a composite state space model that combines both linear and high-order nonlinear elements. Moreover, a robust model predictive controller is developed using the linear component. In addition, the notion of one-step optimal feedforward is used in combination with a compensating controller to handle the high-order nonlinear factor specifically. Subsequently, a compensation controller with incremental characteristics is developed for a modified version of the high-order nonlinear term. Furthermore, the stability conditions of the closed-loop system are derived, and an analysis is conducted on the stability and convergence of the proposed approach. The TTS20 three-capacity water tank was utilized in both simulations and practical scenarios. The study demonstrated that the suggested approach successfully reduces system output variations and enhances overall performance in response to unpredictable changes in the process’s dynamic features.

A Polytopic Model-Based Robust Predictive Control Scheme for Path Tracking of Autonomous Vehicles

A Polytopic Model-Based Robust Predictive Control Scheme for Path Tracking of Autonomous Vehicles

RBF-ARX Model-Based Robust Predictive Control Strategy With One Degree-of-Freedom

RBF-ARX Model-Based Robust Predictive Control Strategy With One Degree-of-Freedom

Reduced-order Koopman modeling and predictive control of nonlinear processes

In this paper, we propose an efficient data-driven predictive control approach for general nonlinear processes based on a reduced-order Koopman operator. A Kalman-based sparse identification of nonlinear dynamics method is employed to select lifting functions for Koopman identification. The selected lifting functions are used to project the original nonlinear state–space into a higher-dimensional linear function space, in which Koopman-based linear models can be constructed for the underlying nonlinear process. To curb the significant increase in the dimensionality of the resulting full-order Koopman models caused by the use of lifting functions, we propose a reduced-order Koopman modeling approach based on proper orthogonal decomposition. A computationally efficient linear robust predictive control scheme is established based on the reduced-order Koopman model. A case study on a benchmark chemical process is conducted to illustrate the effectiveness of the proposed method. Comprehensive comparisons are conducted to demonstrate the advantage of the proposed method.

Corridors optimization for robust MPC navigation in a cluttered environment

This study addresses the issue of navigation in a cluttered environment of an autonomous vehicle subject to disturbances that may result from different sources, for instance: measurement errors, modeling errors, or state estimation errors. The main contributions of the paper reside in the use of a new definition of the concept of navigation corridor and an algorithm allowing to optimize these corridors while simplifying planning and ensuring the feasibility of the control problem based on a robust predictive control strategy.

Analysis and Design of Drivetrain Control for the AEV With Network-Induced Compounding-Construction Loop Delays

This paper presents a robust predictive control scheme with a graphic-based delay boundary analysis to mitigate the electric vehicle (EV) drivetrain oscillating issues, subject to the multi-channel compounding-construction loop delays. The application of Controller Area Network (CAN) in autonomous electric vehicles (AEVs) inevitably induces multi-channel compounding-construction loop delays into the control loop. The in-deep analyzing and understanding of the network-induced loop delays is critical for the electrified powertrain and its motion control. This study aims to guarantee, explicitly, the motion stability of AEV drivetrains as safe-critical and hard real-time applications. Firstly, a graphic-based constructional representation approach is presented for modeling of the compounding-construction loop delays. To resolve the upper bound of the compounding-construction loop delays further, a mathematic expression of delay boundary-envelopment analysis is derived. Secondly, based on the reasonable upper bound, Taylor series expansion is applied to make the system model with nonlinear uncertainties caused by the network-induced loop delays represent in the form of the convex polytope. Then, with the convex polytope of the drivetrain system model, a robust model predictive control (RMPC) approach is developed to enhance the system robustness against the unexpected network-induced delays. To attenuate the online calculation burden, a scheme combining off-line design and on-line synthesis is provided. Finally, the satisfactory motion control performance in both the co-simulations (Matlab&Carsim) and bench experimental tests can strongly verify the effectiveness of the proposed approaches.

A Two-Stage Scheduling RPC Based on Time-Varying Coefficient Information of State-Dependent ARX Model

A two-stage scheduling robust predictive control (RPC) algorithm, which is based on the time-varying coefficient information of the state-dependent ARX (SD-ARX) model, is designed for the output tracking control of a class of nonlinear systems. First, by using the parameter variation range information of the SD-ARX, a strategy for constructing the system’s polytopic model is designed. To further reduce the conservativeness of the convex polytopic sets which are designed to wrap the system’s future dynamics, the variation range information of the SD-ARX model’s parameters is also considered and compressed. In this method, the polytopic state-space model of the system is constructed directly based on the special structure of the SD-ARX model itself, and there is no need to make such assumption that the bounds on the parameter’s variation range in the system model are known or measurable. And then, a two-stage scheduling RPC algorithm is designed for the output tracking control. A numerical example is presented to demonstrate the effectiveness of the proposed RPC strategy.

Robust predictive synchronization of uncertain fractional-order time-delayed chaotic systems

In this paper, a novel robust predictive control strategy is proposed for the synchronization of fractional-order time-delay chaotic systems. A recurrent non-singleton type-2 fuzzy neural network (RNT2FNN) is used for the estimation of the unknown functions. Additionally, another RNT2FNN is used for the modeling of the tracking error. A nonlinear model-based predictive controller is then designed based on the proposed fuzzy model. The asymptotic stability of the approach is derived based on the Lyapunov stability theorem. A number of simulation examples are presented to verify the effectiveness of the proposed control method for the synchronization of two uncertain fractional-order time-delay identical and nonidentical chaotic systems. The proposed control strategy is also employed for high-performance position control of a hydraulic actuator. In this example, the nonlinear mechanical model of the hydraulic actuator, instead of a mathematical model, is simulated. The example demonstrates that the proposed control strategy can be applied to a wide class of nonlinear systems.

Predictive Expert Control System of a Hybrid Pilot Rougher Flotation Circuit

Abstract: In the last decades heuristic approaches based on logic rules have been one favored tool to control flotation units and plants on top of distributed control systems. The lack of fundamental knowledge on flotation processes, expressed in reliable models, and programs to assure the quality of the information, contained in available measurements, have limited the developing of robust predictive control strategies. In this paper we explore the idea of using simplified models jointly with measured disturbances to modify the set points of froth depth and air flow rate controllers. Since these target predictions are only approximate, a conventional feedback expert system, based on logic rules, continues the task of calculating new set points of the distributed controllers, in order to decrease the gap between actual and desirable metallurgical targets. This combined actions permitted a fast response of the process to change the operation of the circuit when a measured disturbance change is detected. This control system was implemented and experimentally evaluated in a pilot rougher flotation circuit, with three pneumatic cells, with froth depth and air flow rate controllers in each cell. Since the circuit is operated for the air-water system, a phenomenological model, with parameters estimated from industrial data, is fed on-line with real operating variables and virtual values, characterizing the feed (grades, solids%, particle size distribution), in order to predict the metallurgical targets (recovery and concentrate grade). Several cases are discussed showing the benefits of this control system and the possible improvements to work on.

Robust output feedback model predictive control using reduced order models

We consider the robust output feedback control of uncertain systems utilizing a robust predictive control scheme based on reduced order models. In detail, we assume that for the plant an uncertain, linear full order model is available. For this model a reduced order model is derived. The proposed control scheme utilizes an estimator based on the reduced order model, which is combined with a robust model predictive controller to robustly stabilize the system. The proposed framework allows to guarantee robust satisfaction of input and output constraints as well as robust stability. An efficient implementation is possible, because online only the solution of a standard model predictive control problem and a simple state estimation problem are needed, which both involve the reduced model. The applicability of the control scheme is outlined using simulation examples.

Robust Nonlinear Model Predictive Control for Regulation of Microalgae Culture in a Continuous Photobioreactor

This paper proposes the design of a robust predictive control strategy which guarantees robustness towards parameters mismatch for a simplified macroscopic continuous photobioreactor model, obtained from mass balance based modelling. Firstly, this work is focused on classical robust nonlinear model predictive control law under model parameters uncertainties implying solving min-max optimization problem for setpoint trajectory tracking. Secondly, a new approach is proposed, consisting in reducing the basic min-max problem into a regularized optimization problem based on the use of linearization techniques, to ensure a good trade-off between tracking accuracy and computation time. Finally, the developed control law is compared to classical and robust predictive controllers. Its effciency is illustrated through numerical results and robustness against parameter uncertainties is discussed for the worst case model mismatch.

Robust Predictive Control of Input Constraints and Interference Suppression for Semi-Trailer System

In this paper, an online receding robust predictive control scheme is proposed for inputconstrained semi-trailer system with delay and disturbance. The controller method meets the requirements of control constraint and, based on dual-mode control, the method is obtained by online optimization of performance index. The performance index is the cumulative sum of quadratic weighted value of minimal states. Controller output is calculated by means of linear matrix inequality (LMI), and the controller itself can ensure the asymptotic stability and disturbance attenuation of semi-trailer closed-loop system. Finally, simulation results confirm the effectiveness of the method.

Design and Implementation of a Robust Predictive Control Scheme for Active Power Filters

This paper presents an effective robust predictive control scheme for the active power filter (APF) using a smith-predictor based current regulator, which show superior features when compared to proportional-integral (PI) controllers in terms of an enhanced closed-loop bandwidth and an improved current tracking accuracy. A moving average filter (MAF) is implemented using a field programmable gate array (FPGA) for signal pre-processing to eliminate the switching ripple contamination. An adaptive linear neural network (ADALINE) is used for individual harmonic estimation to achieve selective compensation purpose. The effectiveness and validity of the devised control algorithm are confirmed by extensive simulation and experimental results.

Robust Predictive Control Strategy Applied for Propofol Dosing Using BIS as a Controlled Variable During Anesthesia

This paper presents the application of predictive control to drug dosing during anesthesia in patients undergoing surgery. The performance of a generic predictive control strategy in drug dosing control, with a previously reported anesthesia-specific control algorithm, has been evaluated. The robustness properties of the predictive controller are evaluated with respect to inter- and intrapatient variability. A single-input (propofol) single-output (bispectral index, BIS) model of the patient has been assumed for prediction as well as for simulation. A set of 12 patient models were studied and interpatient variability and disturbances are used to assess robustness of the controller. Furthermore, the controller guarantees the stability in a desired range. The applicability of the predictive controller in a real-life environment via simulation studies has been assessed.

Robust Model Predictive Control of a Diesel Engine Airpath

Model predictive control represents one of the most promising methods, also in fast industrial applications like a Diesel engine. But especially Diesel engine control meets problems of uncertainties and disturbances, thus model-plant mismatch is omnipresent, which obviously decreases the performance of model based control. This paper compares different robust predictive control strategies applied to a Diesel engine airpath.

Constrained predictive control of nonlinear plants via polytopic linear system embedding

This paper considers nonlinear system control via robust predictive controllers developed for constrained LTV systems with polytopic model uncertainties. The nonlinear systems dealt with are the ones whose trajectories can be embedded within those of a polytopic LTV system. This condition can be satisfied in bounded regions of the state space for a large class of nonlinear systems. In particular, we focus our attention on a robust predictive control scheme similar to the one recently developed by two of the authors for input-saturated polytopic LTV discrete-time systems, here extended so as to take into account also state constraints. The solution is based on the minimization, at each time instant, of an upper bound of the ‘worst-case’ infinite horizon quadratic cost under the constraint of steering the convex hull of the predicted state set into a feasible and robust positive invariant region. A condition on the initial state is given that suffices to ensure problem solvability for all subsequent time instants. The proposed predictive controller is proved to robustly quadratically stabilize input and state-constrained LTV polytopic systems, as well as any other embedded nonlinear system. Copyright © 2000 John Wiley & Sons, Ltd.

Robust predictive control for the flexible coordinated secondary voltage control of large-scale power systems

A robust predictive control strategy is proposed for the secondary voltage control of large-scale power systems. It is based on the decoupling between control and prediction. The asynchronous part of the input delay is taken into account in the controller synthesis, while the output delay plus the synchronous part of the input delay is handled using an original minimal state Kalman predictor.