State-Space Dynamic Performance Preview-Predictive Controller

2006 ◽  
Vol 129 (2) ◽  
pp. 144-153 ◽  
Author(s):  
Andrzej W. Ordys ◽  
Masayoshi Tomizuka ◽  
Michael J. Grimble
Keyword(s):  
State Space ◽  
Predictive Control ◽  
Performance Index ◽  
Dynamic Performance ◽  
Optimization Procedure ◽  
Control Algorithms ◽  
Optimization Approach ◽  
Linear Quadratic ◽  
Preview Control ◽  
Predictive Controller

The paper discusses state-space generalized predictive control and the preview control algorithms. The optimization procedure used in the derivation of predictive control algorithms is considered. The performance index associated with the generalized predictive controller (GPC) is examined and compared with the linear quadratic (LQ) optimal control formulation used in preview control. A new performance index and consequently a new algorithm is proposed dynamic performance predictive controller (DPPC) that combines the features of both GPC and preview controller. This algorithm minimizes the performance index through a dynamic optimization. A simple example illustrates the features of the three algorithms and prompts a discussion on what is actually minimized in predictive control. The DPPC algorithm, derived in this paper, provides for a minimum of the predictive performance index. The differences and similarities between the preview control and the predictive control have been discussed and optimization approach of predictive control has been explained.

scholarly journals Trajectory Tracking and Stabilization of a Quadrotor Using Model Predictive Control of Laguerre Functions

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Mapopa Chipofya ◽  
Deok Jin Lee ◽  
Kil To Chong
Keyword(s):  
State Space ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Linear Quadratic Regulator ◽  
Laguerre Functions ◽  
Linear Quadratic ◽  
Model Predictive Controller ◽  
Predictive Controller ◽  
Linear State ◽  
Quadrotor System

This paper presents a solution to stability and trajectory tracking of a quadrotor system using a model predictive controller designed using a type of orthonormal functions called Laguerre functions. A linear model of the quadrotor is derived and used. To check the performance of the controller we compare it with a linear quadratic regulator and a more traditional linear state space MPC. Simulations for trajectory tracking and stability are performed in MATLAB and results provided in this paper.

scholarly journals A Simplified Predictive Control of Constrained Markov Jump System with Mixed Uncertainties

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yanyan Yin ◽  
Yanqing Liu ◽  
Hamid R. Karimi
Keyword(s):  
Predictive Control ◽  
Transition Probability ◽  
Dynamic Performance ◽  
Markov Jump ◽  
Markov Jump System ◽  
Predictive Controller ◽  
Model Predictive Control Algorithm ◽  
Simplified Algorithm ◽  
Jump Systems ◽  
Mixed Uncertainties

A simplified model predictive control algorithm is designed for discrete-time Markov jump systems with mixed uncertainties. The mixed uncertainties include model polytope uncertainty and partly unknown transition probability. The simplified algorithm involves finite steps. Firstly, in the previous steps, a simplified mode-dependent predictive controller is presented to drive the state to the neighbor area around the origin. Then the trajectory of states is driven as expected to the origin by the final-step mode-independent predictive controller. The computational burden is dramatically cut down and thus it costs less time but has the acceptable dynamic performance. Furthermore, the polyhedron invariant set is utilized to enlarge the initial feasible area. The numerical example is provided to illustrate the efficiency of the developed results.

The Brazing Furnace Intelligent Control Scheme Research of Application

2014 ◽  
Vol 602-605 ◽  
pp. 1135-1138
Author(s):  
Cai Hong Yao ◽  
Xing Jia Jiang
Keyword(s):  
State Space ◽  
Pid Control ◽  
Performance Index ◽  
High Performance ◽  
State Feedback ◽  
Simulation Analysis ◽  
Dynamic Performance ◽  
Optimum Control ◽  
Actual System ◽  
Control Scheme

For the brazing furnace work system with time-varying, hysteresis and nonlinearity characteristic, the traditional PID control cannot meet the requirements of high-performance. Fuzzy self-tuning PID control scheme, can effectively overcome the interference effect, has good adaptability. But the based on modern control theory state space analytic method uses state feedback, not only promulgating the system internal structural property, to realize the optimum control to system’s compound performance index. By combining state feedback and PID control of the respective advantages, determined the brazing furnace temperature system’s optimum control scheme. Either the simulation analysis or actual system operation fully explaining fuzzy intelligent designs of the tuning PID and the optimal control scheme based on state space method have improved significantly the system's steady-state and dynamic performance index.

scholarly journals Incremental State-Space Model Predictive Control of a Fresnel Solar Collector Field

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 3 ◽  
Author(s):  
Eduardo Camacho ◽  
Antonio Gallego ◽  
Adolfo Sanchez ◽  
Manuel Berenguel
Keyword(s):  
Model Predictive Control ◽  
Power Systems ◽  
State Space ◽  
Predictive Control ◽  
State Space Model ◽  
Distributed Parameter ◽  
Space Model ◽  
Luenberger Observer ◽  
Predictive Controller ◽  
Nonlinear Distributed Parameter Model

Model predictive control has been demonstrated to be one of the most efficient control techniques for solar power systems. An incremental offset-free state-space Model Predictive Controller (MPC) is developed for the Fresnel collector field located at the solar cooling plant installed on the roof of the Engineering School of Sevilla. A robust Luenberger observer is used for estimating the states of the plant which cannot be measured. The proposed strategy is tested on a nonlinear distributed parameter model of the Fresnel collector field. Its performance is compared to that obtained with a gain-scheduling generalized predictive controller. A real test carried out at the real plant is presented, showing that the proposed strategy achieves a very good performance.

Evaluation of Model Based Predictive Control Algorithms for Fractional Horse Power Drives

2015 ◽  
Vol 805 ◽  
pp. 231-240
Author(s):  
Matthias Blank ◽  
Sebastian Wendel ◽  
Philipp Loehdefink ◽  
Armin Dietz
Keyword(s):  
Cost Function ◽  
Predictive Control ◽  
Control Method ◽  
Dynamic Performance ◽  
Small Time ◽  
Control Algorithms ◽  
Model Based ◽  
Computational Requirement ◽  
Drive Systems ◽  
Horse Power

Model based predictive control is a new promising control method in the field of powerelectronics and electrical drives. The main advantages of MPC are simplicity and intuitiveness of thecontrol method. Constraints and nonlinearities of the system can easily be included, which makes thelinearisation of the system unnecessary. By using MPC it is possible to avoid the cascaded structureof common linear control methods and to gain a fast dynamic performance. A disadvantage is theneed to calculate the optimal actuating variable multiple times in every single sampling cycle leadsto a huge requirement of computational power. So far the computational requirement was the greatestbarrier for the practical application of model based predictive control in the field of power electronicsand electrical drive systems. In addition the small time constants of fractional horse power drivescomplicate the application of predictive control algorithms. In this paper, the feasibility of hardwareimplementation of a cost function based Finite Control Set MPC (FCS-MPC) algorithm for directspeed control of fractional horse power drives is explored. The cost function allows to address variouscontrol goals like dynamics of transitions and energy efficiency – an advantage linear conventionalcontrol methods cannot offer. Hitherto there are very few publications for direct predictive speed. Thepresented approach for direct predictive speed control includes a finite number of possible switchingstates of the converter. This considers the discrete nature of power converters and avoids the need formodulation. The basic principle of the control method is presented and the performance is demonstratedby simulations and experimental results for an industrial brushed type DC-motor.

Model Predictive Control of Spacecraft Relative Motion Maneuvers Using the IPA-SQP Approach

2013 ◽  
Author(s):  
Hyeongjun Park ◽  
Ilya Kolmanovsky ◽  
Jing Sun
Keyword(s):  
Performance Evaluation ◽  
Predictive Control ◽  
Relative Motion ◽  
Sequential Quadratic Programming ◽  
Nonlinear Constraints ◽  
Linear Quadratic ◽  
Model Predictive Controller ◽  
Polyhedral Norm ◽  
Predictive Controller ◽  
Performance Evaluation And Comparison

In this paper, a Model Predictive Controller (MPC) based on the Integrated Perturbation Analysis and Sequential Quadratic Programming (IPA-SQP) is designed and analyzed for spacecraft relative motion maneuvering. To evaluate the effectiveness of the IPA-SQP MPC, the results are compared with the linear quadratic MPC algorithm developed in [4, 13–15]. It is shown that the IPA-SQP algorithm can handle directly nonlinear constraints on thrust magnitude without resorting to saturation or polyhedral norm approximations. Spacecraft fuel consumption related metrics are examined for performance evaluation and comparison.

scholarly journals Improving the Performance of Single-Intersection Urban Traffic Networks Based on a Model Predictive Controller

2021 ◽  
Vol 13 (10) ◽  
pp. 5630
Author(s):  
Sadiqa Jafari ◽  
Zeinab Shahbazi ◽  
Yung-Cheol Byun
Keyword(s):  
Model Predictive Control ◽  
State Space ◽  
Predictive Control ◽  
Urban Traffic ◽  
Capital City ◽  
Traffic Network ◽  
Model Predictive Controller ◽  
Traffic Volumes ◽  
Predictive Controller ◽  
Control Traffic

The use of a Model Predictive Controller (MPC) in an urban traffic network allows for controlling the infrastructure of a traffic network and errors in its operations. In this research, a novel, stable predictive controller for urban traffic is proposed and state-space dynamics are used to estimate the number of vehicles at an isolated intersection and the length of its queue. This is a novel control strategy based on the type of traffic light and on the duration of the green-light phase and aims to achieve an optimal balance at intersections. This balance should be adaptable to the unchanging behavior of time and to the randomness of traffic situations. The proposed method reduces traffic volumes and the number of crashes involving cars by controlling traffic on an urban road using model predictive control. A single intersection in Tehran, the capital city of Iran, was considered in our study to control traffic signal timing, and model predictive control was used to reduce traffic. A model of traffic systems was extracted at the intersection, and the state-space parameters of the intersection were designed using the model predictive controller to control traffic signals based on the length of the vehicle queue and on the number of inbound and outbound vehicles, which were used as inputs. This process demonstrates that this method is able to reduce traffic volumes at each leg of an intersection and to optimize flow in a road network compared to the fixed-time method.

Controller Design for Multi-Body Ground Vehicle Rollover Prevention Using Modified LQR Framework

2008 ◽  
Author(s):  
Rama K. Yedavalli ◽  
Hsun-Hsuan Huang
Keyword(s):  
Performance Index ◽  
Controller Design ◽  
Optimization Procedure ◽  
Linear Quadratic Regulator ◽  
Physical Nature ◽  
Linear Quadratic ◽  
Ground Vehicle ◽  
Rollover Prevention ◽  
Control Gain ◽  
Multi Body

This paper addresses the issue of control design for rollover prevention of a multi-body ground vehicle with army applications. The novelty of the approach lies in expressing the traditional rollover index as a quadratic performance index in Linear-quadratic regulator (LQR) design thereby integrating the roll over index directly and explicitly in the optimization procedure. The control gain obtained based on rollover performance index is more effective for rollover prevention than the gain obtained based on standard LQR design in which there is much ambiguity and labor involved in assigning the weights. The control gain obtained based on the proposed rollover performance index outperforms the standard LQR design, essentially because it introduces a cross coupling term between the state and control variables in the modified performance index. Thus, the proposed rollover prevention technique effectively incorporates the physical nature of the vehicle dynamics in the problem formulation resulting in significantly improved performance.

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