State Space Model Predictive Control Research Articles

Improved state space model predictive fault-tolerant control for injection molding batch processes with partial actuator faults using GA optimization

A novel model predictive fault-tolerant control (MPFTC) strategy adopting genetic algorithm (GA) is proposed for batch processes under the case of disturbances and partial actuator faults. Based on the extended state space model in which the tracking error is contained, there are more degrees of freedom provided for the controller design and better control performance is obtained. In order to enhance the control performance further, the GA is introduced to optimize the relevant weighting matrices in the cost function. The effectiveness of the proposed MPFTC approach is tested on the injection velocity regulation of the injection molding process.

Fractional order MPC design using improved state space model

In order to improve the performance of control systems, a fractional order model predictive control (FMPC) algorithm using extended non-minimal state space (ENMSS) model is presented in this paper. For this propose, the fractional order model that describes the characteristics of practical processes more accurate than integer order model and the model predictive control (MPC) method based on ENMSS model that includes the state variable and output tracking error are considered. In this paper, the Grünwald-Letnikov (GL) definition is used to discretize the fractional order model and the fractional order cost function. This algorithm inherits the advantages of integer order extended non-minimal state space model predictive control (ENMSSMPC) with more adjustable parameters and better disturbance rejection, such that the controller tuning is more flexible. By comparing with the integer order ENMSSMPC, the results of the temperature control of an industrial heating furnace indicate the performance improvement of the proposed method.

A Model Predictive Guidance Strategy for a Multirotor Aerial Vehicle

The present study faces the problem of safely controlling the position trajectory of a multirotor aerial vehicle subjected to a conic constraint on the total thrust vector and a linear convex constraint on the position vector. The problem is solved using a linear state-space model predictive control strategy, whose optimization is made handy by replacing the original conic constraint set on the thrust vector by an inscribed pyramidal space, which renders a linear set of inequalities. The proposed method is evaluated on the basis of Monte Carlo simulations taking into account a random disturbance force. The simulation results show the effectiveness of the method in tracking the commanded trajectory while respecting the constraints. They also predict the effect of both the speed command and the maximum allowed inclination angle on the system performance.

Sensor-Less Predictive Drying Control of Pneumatic Conveying Batch Dryers

This paper presents predictive drying control design for a lab-scaled industrial pneumatic conveying dryer (PDC) involves with continuous/ batch processing of powder materials. The model predictive control (MPC) is an established method for drying control in various drying applications, such as fluidized bed dryers, rotary dryer, infrared dryer, timber dryers, baker’s yeast dryers and so on. But the predictive control of PDCs have not been studied in the literature, however, these dryers are widely used in food, agriculture, and chemical industries, particularly suitable for batch processing of fine grained materials. The unavailability of any suitable control oriented first principle’s model of these dryers make the predictive control design and implementation issues more challenging. Existing control methods for similar drying applications use outlet material moisture as a main control variable, online measurement, of which is difficult, costly, and unreliable due to the involvement of materials in granular/powder form. In the present contribution, an innovative control oriented model of the dryer is derived from first principle’s encompassing a soft sensor-based online powder-moisture measurement procedure replacing the physical moisture sensors. The proposed physical sensor-less powder moisture control strategy stands on the traditional two-layer predictive control paradigm involving detection of an economically best operating point for batch dryer operation by optimizing the various process economic objectives followed by employing a suitable state space MPC (SSMPC) law for steering the process to operate at economically best operating point. The developed control strategy has been implemented and tested under practical settings and shown its effectiveness in improving the drying performance and product quality compared with an inbuilt auto-tuned proportional integral plus derivative controller of Honeywell make HC900 programmable logic controller.

State-Space Model Predictive Control Method for Core Power Control in Pressurized Water Reactor Nuclear Power Stations

Abstract A well-performed core power control to track load changes is crucial in pressurized water reactor (PWR) nuclear power stations. It is challenging to keep the core power stable at the desired value within acceptable error bands for the safety demands of the PWR due to the sensitivity of nuclear reactors. In this paper, a state-space model predictive control (MPC) method was applied to the control of the core power. The model for core power control was based on mathematical models of the reactor core, the MPC model, and quadratic programming (QP). The mathematical models of the reactor core were based on neutron dynamic models, thermal hydraulic models, and reactivity models. The MPC model was presented in state-space model form, and QP was introduced for optimization solution under system constraints. Simulations of the proposed state-space MPC control system in PWR were designed for control performance analysis, and the simulation results manifest the effectiveness and the good performance of the proposed control method for core power control.

Distributed model predictive coverage control for decoupled mobile robots

SUMMARYA distributed coverage control scheme based on the state space model predictive control, which is known as receding horizon control (RHC) for decoupled systems, is presented. An optimal control problem is formulated for a set of decoupled robotic systems where a cost function couples the dynamical behavior of the robots. The coupling is described through a connected graph using a Voronoi diagram, where each robot is a node and the cost and constraints of the optimization problem associated with each robot are a function of its state and of the states of its neighbors. The complexity of the problem is addressed by breaking a centralized receding horizon controller into distinct RHC controllers of smaller sizes. Each RHC controller is associated with a different node and it computes the local control inputs based only on the position of the robot and that of its neighbors. The stability of the distributed scheme is analyzed and its properties compared with the linear quadratic regulator (LQR) design which has been proposed in the literature. Moreover, the proposed coverage algorithm is also applied to deploy a group of mobile robots in a desired formation pattern. The simulation results are used to illustrate the good performance of the proposed coverage control scheme.

Tuning of PI–PD controller using extended non-minimal state space model predictive control for the stabilized gasoline vapor pressure in a stabilized tower

Nonlinearities, uncertainties and large time delay widely exist in the industrial processes, which pose great difficulty to traditional proportional-integral-derivative (PID) control. Proportional-integral–proportional-derivative (PI–PD) controller, which is a modified form of PID controller, can get a better performance than PID control. However, due to the fact that PI–PD controller has an extra parameter, the difficulty of parameter adjustment is a major problem. In this paper, a new PI–PD method optimized by the extended non-minimal state space model predictive control (ENMSSMPC) is proposed to get the optimal parameters of PI–PD controller for dealing with the process complexity. The proposed PI–PD controller combines the advantage of ENMSSMPC and the simple structure of the PID controller and is tested on the stabilized gasoline vapor pressure in a stabilized tower. Results show that it outperforms traditional controllers such as PID controller, PI–PD controller, traditional model predictive control based PID and PIPD controllers (TPFC-PID and TPFC-PIPD) and IMC based Robust PID controller (Robust IMC-PID).

NEO-Fuzzy State-Space Predictive Control

This paper describes the development of a novel state-space model predictive controller. The proposed modelling structure used to capture and predict the nonlinear process dynamics lies on the concept for a neo-fuzzy neuron, deployed in state-space. The introduced approach represents a set of simple fuzzy inferences along the temporal behaviour of each input node, whose dynamics is expressed as a singleton function. The learning algorithm for the proposed modelling structure is realized as a gradient descent procedure. On the basis of the obtained neo-fuzzy state-space model, a fuzzy predictor for the purpose of predictive control is developed. The achieved predictions are used to optimize the future system response by implementing a quadratic programming optimization procedure along the stated controller horizons. The potentials of the proposed approach are studied by simulation experiments to modelling and control of a nonlinear drying plant.

An improved decoupling structure based state space MPC design with improved performance

In this paper, an improved decoupling structure is first proposed for multivariable processes, where the resulted decoupler can avoid zero-pole cancelation and simultaneously guarantee feasibility by tuning of additional parameters. A subsequent single-input–single-output design of model predictive control (MPC) is then proposed using a new state space realization of the independent single loops, where we adopt a new extended non-minimal state space structure to enhance control performance. Through this new design model, dynamics of processes’ state variables can be directly taken into account. The decoupler property, closed-loop control performance, the relationship with traditional state space MPC and robust stability issues are fully analyzed. The efficiency of the proposed is evaluated through a typical process in the recent literature, in comparison with a typical non-minimal state space MPC recently developed.

Temperature Control of Industrial Coke Furnace Using Novel State Space Model Predictive Control

This paper proposes an enhanced model predictive control (MPC) using a new state space structure for temperature control of an industrial coke furnace. The advantage of the proposed controller lies in the fact that its implementation only requires a simple step-response process model, whereas controller design can be based on state space formulation to improve temperature regulation. To ensure control performance effectiveness under model/process mismatch and uncertainties, model predictions and the cost function optimization are done on the basis of a new improved state space model. The proposed MPC is applied to an industrial coke furnace, where the outlet temperature in the radiation room is regulated. Simulation comparisons with traditional state space MPC are illustrated first. Then experimental results are shown in comparison with the original proportional-integral differential (PID) controller, demonstrating the effectiveness of the proposed methodology.

Design and implementation of an improved linear quadratic regulation control for oxygen content in a coke furnace

In view of the performance deterioration of conventional model predictive control strategies under model/plant mismatches, this study first proposes an improved linear quadratic regulation control (LQR) strategy and then tests it on the oxygen content in an industrial coke furnace. The control system design consists of two steps. Based on the direct use of the measured input and output variables and the formulation of output error, a state space model is first developed. Then, by incorporating the dynamics of process states into the cost function of the controller design, a new LQR is proposed to obtain an improved performance for closed-loop control systems. Comparisons with conventional state space model predictive control are also given in terms of both regulatory and servo performance. The case study on the oxygen content in the coke furnace shows that the proposed improves control performance under model/plant mismatches.

Real-Time Implementation of Improved State-Space MPC for Air Supply in a Coke Furnace

In an industrial coke process, the dynamic relationship between the input and output devices is complicated. Since performance is closely related to the control used, improved control alternatives are necessary. This paper deals with the oxygen content control loop and proposes an improved state-space model predictive control (MPC) aimed at satisfying steady air supply into the process. The details of the proposed MPC are first described and tested on a typical example and then implemented on an industrial coke furnace. Simulation examples and real-time implementation results both show the effectiveness of the proposed scheme.

Disturbance modeling and state estimation for offset-free predictive control with state-space process models

Abstract Disturbance modeling and design of state estimators for offset-free Model Predictive Control (MPC) with linear state-space process models is considered in the paper for deterministic constant-type external and internal disturbances (modeling errors). The application and importance of constant state disturbance prediction in the state-space MPC controller design is presented. In the case with a measured state, this leads to the control structure without disturbance state observers. In the case with an unmeasured state, a new, simpler MPC controller-observer structure is proposed, with observation of a pure process state only. The structure is not only simpler, but also with less restrictive applicability conditions than the conventional approach with extended process-and-disturbances state estimation. Theoretical analysis of the proposed structure is provided. The design approach is also applied to the case with an augmented state-space model in complete velocity form. The results are illustrated on a 2×2 example process problem.

Model Predictive Control with Feedforward Strategy for Gas Collectors of Coke Ovens

In coking process, the production quality, equipment life, energy consumption, and process safety are all influenced by the pressure in gas collector pipe of coke oven, which is frequently influenced by disturbances. The main control objectives for the gas collector pressure system are keeping the pressures in collector pipes at appropriate operating point. In this paper, model predictive control (MPC) strategy is introduced to control the collector pressure system due to its ability to handle constraint and good control performance. Based on a method proposed to simplify the system model, an extended state space model predictive control is designed, which combines the feedforward strategy to eliminate the disturbance. The simulation results in a system with two coke ovens show the feasibility and effectiveness of the control scheme.

Predictive control optimization based PID control for temperature in an industrial surfactant reactor

Due to the character of nonlinearity, uncertainties, time delays and so on in the industrial reactors, the performance of proportional-integral-derivative (PID) control cannot always achieve the desired effect. Model predictive control (MPC) is a useful control strategy in the fact that the process models do not need to be accurately known. However, limited by the cost, hardware and so on, the application of MPC is less convenient than PID. In this paper, the temperature control in an industrial surfactant reactor is studied, where an improved PID controller optimized by extended non-minimal state space model predictive control (ENMSSMPC) framework is employed. The temperature in the surfactant reactor is first modeled as a typical step-response model and then a corresponding improved state space transformation with subsequent MPC design is done. The overall strategy combines the advantages of both PID's simple structure and MPC's good control performance. The proposed method is compared with traditional PID and MPC controllers and results show that it provides improved performance.

State space model predictive fault-tolerant control for batch processes with partial actuator failure

This paper presents a state space model predictive fault-tolerant control scheme for batch processes with unknown disturbances and partial actuator faults. To develop the model predictive fault-tolerant control, the batch process is first treated into a non-minimal representation using state space transformation. The relevant concepts of the corresponding model predictive fault-tolerant control is thus introduced through state space formulation, where improved closed-loop control performance is achieved even with unknown disturbances and actuator faults, because, unlike traditional model predictive fault-tolerant control, the proposed control method can directly regulate the process output/input changes in the design. For performance comparison, a traditional model predictive fault-tolerant control is also designed. Application to injection velocity control shows that the proposed scheme achieve the design objective well with performance improvement.

Improved State Space Model Predictive Control Design for Linear Systems with Partial Actuator Failure

This paper presents a new state space model predictive control scheme for linear systems with partial actuator faults. To consider dealing with the uncertainties caused by actuator faults, the proc...

Centralized Discrete State Space Model Predictive Control And Decentralized Pi-D Controller Of An Aerothermic Process

Abstract The aerothermic process is a pilot scale heating and ventilation system equipped with a heater grid and a centrifugal blower. The interaction between its main variables is considered as challenging for mono-variable controllers. A change in the ventilator speed affects the temperature behavior which represents a factor that must be managed for energy saving and the human welfare. This paper presents an experimental comparison between a Centralized Discrete State Space Model Predictive Control (CDSSMPC) and a Decentralized PI-D (DPI-D) controller. These both techniques are designed by using respectively the Laguerres functions and the static decoupler approach. To demonstrate the effectiveness of the two methods, an implementation on an aerothermic process is performed. This pilot scale is fully connected through the Humusoft MF624 data acquisition system for real time control. The results show satisfactory performance in closed-loop of the DPI-D controller compared to the CDSSMPC and the conventional PID ones.

State Space Model Predictive Control Using Partial Decoupling and Output Weighting for Improved Model/Plant Mismatch Performance

Focusing on multivariable control, this paper presents a design method of model predictive control that enjoys the benefits of both the partial decoupling and the state space design. The multivariable process is first decoupled into a set of multi-input single-output (MISO) structures and then transformed into an extended state space model (partial decoupling extended state space model, PD-ESS). Consequently, a systematic design of model predictive control is proposed. The proposed controller is tested on three typical cases for comparison with previous controllers. Results show that control performance is improved. In addition, a closed-form of transfer function representation that facilitates frequency analysis of the control system is provided for further insight into the proposed method.

Subspace-Based Indirect Adaptive State-Space Model Predictive Control

In order to deal with nonlinear, time-varying and disturbance-involved characteristics in the practical industrial processes, an indirect adaptive state-space MPC (model predictive control) method based on subspace identification is proposed. The state-space model, obtained through the POMOESP (Past Output MOESP, MOESP is one form of the subspace identification methods) algorithm, is regarded as the system model. Then, this model is used to design the model predictive controller that involves the solution of a quadratic programming problem to constraints. This controller is applied to the process control simulation on a 2-CSTR. Through comparisons of performance with a linear state-space MPC scheme, the superiority of the proposed control method is illustrated.