ON THE ROBUSTNESS OF A GENERALIZED PREDICTIVE CONTROLLER

In this paper, we present a quantitative analysis of the robustness of a generalized predictive controller. The result of stability analysis shows that, under a specific bounded modelling error, the closed-loop system is BIBO stable in the presence of unmodelled dynamics.

Hybrid Dynamic Variables-Dependent Event-Triggered Fuzzy Model Predictive Control

This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.

THE PRINCIPLE OF ROBUSTNESS IN GENERALIZED PREDICTIVE CONTROL

This paper deeply analyzes the closed-loop nature ofGPCin the fram ework ofinter- nalm odelcontrol(IMC) theory. A new sort ofrelation lies in the feedback structure so that robustreason can be satisfactorily explained. The resultissignificantbecause the previous con- clusions are only applied to open-loop stable plant(orm odel).

An Improved Robust Model Predictive Control Approach to Systems with Linear Fractional Transformation Perturbations

In this paper, a robust model predictive control approach is proposed for a class of uncertain systems with time-varying, linear fractional transformation perturbations. By adopting a sequence of feedback control laws instead of a single one, the control performance can be improved and the region of attraction can be enlarged compared with the existing model predictive control （MPC） approaches. Moreover, a synthesis approach of MPC is developed to achieve high performance with lower on-line computational burden. The effectiveness of the proposed approach is verified by simulation examples.

Comparative Study of Trajectory Tracking Control for Automated Vehicles via Model Predictive Control and Robust H-infinity State Feedback Control

A comparative study of model predictive control(MPC)schemes and robust Hstate feedback control(RSC)method for trajectory tracking is proposed in this paper.The main objective of this paper is to compare MPC and RSC controllers’performance in tracking predefined trajectory under different scenarios.MPC controller is designed based on the simple longitudinal-yaw-lateral motions of a single-track vehicle with a linear tire,which is an approximation of the more realistic model of a vehicle with double-track motion with a non-linear tire mode.RSC is designed on the basis of the same method as adopted for the MPC controller to achieve a fair comparison.Then,three test cases are built in CarSim-Simulink joint platform.Specifically,the verification test is used to test the tracking accuracy of MPC and RSC controller under well road conditions.Besides,the double lane change test with low road adhesion is designed to find the maximum velocity that both controllers can carry out while guaranteeing stability.Furthermore,an extreme curve test is built where the road adhesion changes suddenly,in order to test the performance of both controllers under extreme conditions.Finally,the advantages and disadvantages of MPC and RSC under different scenarios are also discussed.

Robust model predictive control for continuous uncertain systems with state delay

This paper is concerned with robust model predictive control for linear continuous uncertain systems with state delay and control constraints, A piecewise constant control sequence is calculated by minimizing the upper-bound of the infinite horizon quadratic cost function, At each sampling time, the sufficient conditions for the existence of the model predictive control are derived, and expressed as a set of linear matrix inequalities. The robust stability of the closed-loop svstems is guaranteed bv the proposed design method. A numerical example is given to illustrate the main results.

Optimal dispatching method for integrated energy system based on robust economic model predictive control considering source-load power interval prediction

Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainties of new energies and various types of loads in the IES.Accordingly,a robust optimal dispatching method for the IES based on a robust economic model predictive control(REMPC)strategy considering source-load power interval prediction is proposed.First,an operation model of the IES is established,and an interval prediction model based on the bidirectional long short-term memory network optimized by beetle antenna search and bootstrap is formulated and applied to predict the photovoltaic power and the cooling,heating,and electrical loads.Then,an optimal dispatching scheme based on REMPC is devised for the IES.The source-load interval prediction results are used to improve the robustness of the REPMC and reduce the influence of source-load uncertainties on dispatching.An actual IES case is selected to conduct simulations;the results show that compared with other prediction techniques,the proposed method has higher prediction interval coverage probability and prediction interval normalized averaged width.Moreover,the operational cost of the IES is decreased by the REMPC strategy.With the devised dispatching scheme,the ability of the IES to handle the dispatching risk caused by prediction errors is enhanced.Improved dispatching robustness and operational economy are also achieved.

Finite-Control-Set Model Predictive Control of Permanent Magnet Synchronous Motor Drive Systems——An Overview

Permanent magnet synchronous motors(PMSMs)have been widely employed in the industry. Finite-control-set model predictive control(FCS-MPC), as an advanced control scheme, has been developed and applied to improve the performance and efficiency of the holistic PMSM drive systems. Based on the three elements of model predictive control, this paper provides an overview of the superiority of the FCS-MPC control scheme and its shortcomings in current applications. The problems of parameter mismatch, computational burden, and unfixed switching frequency are summarized. Moreover, other performance improvement schemes, such as the multi-vector application strategy, delay compensation scheme, and weight factor adjustment, are reviewed. Finally, future trends in this field is discussed, and several promising research topics are highlighted.

Distributed Model Predictive Control with Actuator Saturation for Markovian Jump Linear System

This paper is concerned with the distributed model predictive control(MPC) problem for a class of discrete-time Markovian jump linear systems(MJLSs) subject to actuator saturation and polytopic uncertainty in system matrices. The global system is decomposed into several subsystems which coordinate with each other. A set of distributed controllers is designed by solving a min-max optimization problem in terms of the solutions of linear matrix inequalities(LMIs). An iterative algorithm is developed to achieve the online computation. Finally,a simulation example is employed to show the effectiveness of the proposed algorithm.

Enhanced two-loop model predictive control design for linear uncertain systems

Model predictive controllers(MPC)with the two-loop scheme are successful approaches practically and can be classified into two main categories,tube-based MPC and MPCbased reference governors(RG).In this paper,an enhanced twoloop MPC design is proposed for a pre-stabilized system with the bounded uncertainty subject to the input and state constraints.The proposed method offers less conservatism than the tube-based MPC methods by enlarging the restricted input constraint.Contrary to the MPC-based RGs,the investigated method improves tracking performance of the pre-stabilized system while satisfying the constraints.Additionally,the robust global asymptotic stability of the closed-loop system is guaranteed in a novel procedure with terminal constraint relaxation.Simulation of the proposed method on a servo system shows its effectiveness in comparison to the others.

Robust predictive control of polytopic uncertain systems with both state and input delays

A robust model predictive control algorithm for discrete linear systems with both state and input delays subjected to constrained input control is presented, where the polytopic uncertainties exist in both state matrices and input matrices. The algorithm optimizes an upper bound with respect to a state feedback control law. The feedback control law is presented based on the construction of a parameter-dependent Lyapunov function. The above optimization problem can be formulated as a LMI-based optimization. The feasibility of the optimization problem guarantees that the algorithm is robustly stable. The simulation results verify the effectiveness of the proposed algorithm.

Robustly stable model predictive control based on parallel support vector machines with linear kernel

Robustly stable multi-step-ahead model predictive control (MPC) based on parallel support vector machines (SVMs) with linear kernel was proposed. First, an analytical solution of optimal control laws of parallel SVMs based MPC was derived, and then the necessary and sufficient stability condition for MPC closed loop was given according to SVM model, and finally a method of judging the discrepancy between SVM model and the actual plant was presented, and consequently the constraint sets, which can guarantee that the stability condition is still robust for model/plant mismatch within some given bounds, were obtained by applying small-gain theorem. Simulation experiments show the proposed stability condition and robust constraint sets can provide a convenient way of adjusting controller parameters to ensure a closed-loop with larger stable margin.

Robust model predictive control for discrete uncertain nonlinear systems with time-delay via fuzzy model

An extended robust model predictive control approach for input constrained discrete uncertain nonlinear systems with time-delay based on a class of uncertain T-S fuzzy models that satisfy sector bound condition is presented. In this approach, the minimization problem of the “worst-case” objective function is converted into the linear objective minimization problem in- volving linear matrix inequalities (LMIs) constraints. The state feedback control law is obtained by solving convex optimization of a set of LMIs. Sufficient condition for stability and a new upper bound on robust performance index are given for these kinds of uncertain fuzzy systems with state time-delay. Simulation results of CSTR process show that the proposed robust predictive control approach is effective and feasible.

Robust Predictive Control of Uncertain Systems with Time-delay Using Tight Sets of Predicted States

A robust model predictive control (MPC) algorithm for discrete time linear systems with time-delay (RPC-TDS) subjected to constrained input control is presented, where the polytopic uncertainties exist in state matrices and input matrices. In the algorithm the standard optimization of quadratic objective function has been transformed into optimization of sum of N+1 upper bounds of the quadratic objective function with respect to N control moves and a state feedback control law, where N is the control horizon. The feasibility of the optimization problem guarantees that the algorithm is robustly stable. The simulation results verify the effectiveness of the proposed algorithm.

Robust predictive control of uncertain intergrating linear systems with input constraints

This paper presents a two-stage robust model predictive control (RMPC) aigorithm named as IRMFC for uncertain linear integrating plants described by a state-space model with input constraints. The global convergence of the resulted closed loop system is guaranteed under mild assumption. The simulation example shows its validity and better performance than conventional Min-Max RMPC strategies.

Robust Continuous-time Generalized Predictive Control for Large Time-delay System

A simple delay-predictive continuous-time generalized predictive controller with filter (F-SDCGPC) is proposed. By using modified predictive output signal and cost function, the delay compensator is incorporated in the control law with observer structure, and a filter is added for enhancing robustness. The design of filter does not affect the nominal set-point response, and it is more flexible than the design of observer polynomial. The analysis and simulation results show that the F-SDCGPC has better robustness than the observer structure without filter when large time-delay error is considered.

The Price Model of Aquatic Products Based on Predictive Control Theory

This paper discusses a disequilibrium cobweb model of price of aquatic products, and applies predictive control theory, so that the system operates stably, and the deviation between supply and demand of aquatic products smoothly tracks the pre-given target. It defines the supply and demand change model, and researches the impact of parameter selection in this model on dynamic state and robustness of the system. I conduct simulation by Matlab software, to get the response curve of this model. The results show that in the early period of commodities coming into the market, affected by lack of market information and many other factors, the price fluctuates greatly in a short time. The market will gradually achieve balance between supply and demand over time, and the price fluctuations in the neighbouring two periods are broadly consistent. The increase in model parameter can decrease overshoot, to promote the stability of system, but the slower the dynamic response, the longer the deviation between supply and demand to accurately track a given target. Therefore, by selecting different parameters, the decision-makers can establish different models of supply and demand changes to meet the actual needs, and ensure stable development of market. Simulation results verify the excellent performance of this algorithm.

An Improved Deadbeat Predictive Current Control Method for SPMSM Drives with a Novel Adaptive Disturbance Observer

To improve the dynamic performance of conventional deadbeat predictive current control(DPCC)under parameter mismatch,especially eliminate the current overshoot and oscillation during torque mutation,it is necessary to enhance the robustness of DPCC against various working conditions.However,the disturbance from parameter mismatch can deteriorate the dynamic performance.To deal with the above problem,firstly,traditional DPCC and the parameter sensitivity of DPCC are introduced and analyzed.Secondly,an extended state observer(ESO)combined with DPCC method is proposed,which can observe and suppress the disturbance due to various parameter mismatch.Thirdly,to improve the accuracy and stability of ESO,an adaptive extended state observer(AESO)using fuzzy controller based on ESO,is presented,and combined with DPCC method.The improved DPCC-AESO can switch the value of gain coefficients with fuzzy control,accelerating the current response speed and avoid the overshoot and oscillation,which improves the robustness and stability performance of SPMSM.Finally,the three methods,as well as conventional DPCC method,DPCC-ESO method,DPCC-AESO method,are comparatively analyzed in this paper.The effectiveness of the proposed two methods are verified by simulation and experimental results.

The prediction of projectile-target intersection for moving tank based on adaptive robust constraint-following control and interval uncertainty analysis

To improve the hit probability of tank at high speed,a prediction method of projectile-target intersection based on adaptive robust constraint-following control and interval uncertainty analysis is proposed.The method proposed provides a novel way to predict the impact point of projectile for moving tank.First,bidirectional stability constraints and stability constraint-following error are constructed using the Udwadia-Kalaba theory,and an adaptive robust constraint-following controller is designed considering uncertainties.Second,the exterior ballistic ordinary differential equation with uncertainties is integrated into the controller,and the pointing control of stability system is extended to the impact-point control of projectile.Third,based on the interval uncertainty analysis method combining Chebyshev polynomial expansion and affine arithmetic,a prediction method of projectile-target intersection is proposed.Finally,the co-simulation experiment is performed by establishing the multi-body system dynamic model of tank and mathematical model of control system.The results demonstrate that the prediction method of projectile-target intersection based on uncertainty analysis can effectively decrease the uncertainties of system,improve the prediction accuracy,and increase the hit probability.The adaptive robust constraint-following control can effectively restrain the uncertainties caused by road excitation and model error.