DMC Control Research Articles

Global tracking control of quadrotor based on adaptive dynamic surface control

This paper presents a global nonlinear tracking control system for a quadrotor unmanned aerial vehicle (UAV) in the presence of underactuation, external disturbances and model uncertainties. Quadrotor systems lack enough independent control inputs to control their entire configuration space directly due to underactuation. The proposed solution is to adopt a cascade feedback technique that splits the system dynamics into attitude and position dynamics. The proposed controller is developed directly on the special Euclidean group with a region of attraction covering the entire configuration space where its stability is proven using Lyapunov functions. The controller guarantees the asymptotical convergence of tracking error in the presence of model uncertainties and external disturbances. In particular, the control method combines three techniques: a second-order sliding mode control (SMC), a dynamic surface control, and a non-parametric adaptation mechanism. The SMC is used to stabilize the position dynamics (internal dynamics) by generating a proper attitude command for the attitude controller. The DMC control guarantees the attitude dynamics stability globally and tracking performance while avoiding the mathematical complexities associated with the highly nonlinear dynamics. The adaptation mechanism includes a radial basis function neural network to observe uncertainties without the need for prior training. The uncertainties considered include unmodeled dynamics, external disturbances and parameter uncertainties including the mass and inertial matrices as well as motor coefficients. The desirable features of the proposed control system are illustrated by both numerical simulation and experiments on a UAV testbed.

DMC-PID Cascade Control Algorithm for the Constant Rate Temperature Rising Process in Differential Scanning Calorimeter

In view of the poor tracking performance of conventional control method in differential scanning calorimeter during constant speed heating progress, a DMC-PID cascade control algorithm for differential scanning calorimetry is proposed. Firstly, based on the predictability and optimality of the DMC algorithm, the DMC controller is used in the main circuit to control the generalized object composed of the PID controller and the reheating furnace. Secondly, on the basis of the characteristics that PID controller can improve the dynamic of the controlled object, the PID controller is used in the secondary loop of the control system to improve the linearity of the controlled object. Finally, the proposed control method is applied to differential scanning calorimetry constant temperature control process. Experimental results show that the proposed method can effectively reduce the static error and improve the tracking performance of the control process.

The Application of PID-DMC Algorithm with Feedforward Compensation in the PTZ Platform

According to the characteristics of PTZ camera itself pure delay of measurement and control, discontinuity of control system and unknown model, a new PID-DMC control algorithm with feedforward compensation is proposed. When using the conventional PID algorithm to control, due to the discontinuity of the control amount of the PTZ camera, the serious control jitter when the closed-loop system operating, the stability of the system is affected critically, and even the equipment will be damaged. For this reason, feedforward compensation is added on the basis of conventional PID control to alleviate the problem of increasing of the overshoot caused by the integral action. Meanwhile, DMC control is introduced to effectively reduce the fluctuation of the system, which makes the follow-up tracking of the system more stable and effective.

Heating Control of Heated Twin Radiosonde Humidity Sensor Based on DMC

Abstract In order to effectively solve condensation and icing problems of radiosonde in low-temperature environment at high altitude, humidity sensor heated automatic alternately to remove pollution and improve the measurement accuracy. Heat experiments obtained the curve of rising temperature and responsible time on heated twin humidity sensor in normal temperature and pressure, by expanded responsible curve to obtain heated model of twin heated humidity sensor and by the analysis of heating model, use DMC and PID control for heating respectively. Simulation results show that the DMC control meets the practical requirements of measure at high altitude.

The Application of Cascade Control Based on DMC in Stripping Section Temperature Control System

Predictive control is one of the robust control algorithms that suitable for computerized implementation, and requests lower accuracy of the model. It has been widely used in process control recently. Considering the features of distillation column stripping section, such as complex internal mechanism, time-varying parameters and large time delay, the DMC-PID cascade control strategy is adopted. The simulation results show that this strategy combined the advantages of predictive control and cascade control, can make distillation column stripping section have good dynamic and static characteristic in the presence of external disturbance and time delay. The performances are better than DMC and conventional PID control.

IMPLEMENTATION OF PREDICTIVE MULTIVARIABLE DMC CONTROLLER IN A PILOT PLANT

This study aims to implement the predictive multivariable DMC controller in a real plant and compare it to multi-loop PID. The practical application is made in a pilot plant located in the IFES / Serra, where pressure and level are controlled with pump speed and valve opening. Tuning of the DMC controller is obtained through a simulator based on genetic algorithm. The plant responses to step using multi-loop PID and DMC are compared with and without constraints on valve opening and pump speed. The DMC controller is also tested with output restriction (pressure and level). The didactic interface developed with LabVIEW software is used to interact with MATLAB and CompactRIO controller, allowing the use of MATLAB optimization functions in the implementation of the DMC controller.

Papermaking Process Basis Weight Control Based on PIDDMC Optimal Algorithm

In the papermaking actual process basis weight of paper sheet often has properties of nonlinearity, time-varying, uncertainty and larger-time delay. With the improvement of control requirements, traditional PID control is not ideal, and DMC control depends on the process model which needs the actual feedback information to complete repeated online optimization. So a novel PIDDMC combined optimal algorithm is proposed to obtain the satisfied performance index. Simulation is analyzed to show that the algorithm can be well performed with high accuracy and stability.

Continuous analysis of move suppressed and shifted DMC

“Shifted DMC” (shifted dynamic matrix control) has been empirically shown to have significant improved closed-loop control characteristics over “move-suppressed DMC” where, in the latter, diagonal terms of the dynamic matrix DMC prediction model are augmented to reduce numerical ill conditioning. An added benefit of shifted DMC was that the so-called “shifting parameter,” replacing the move suppression parameter, was easily found from the open-loop response. Therefore a novel analytical method, based on a closed form, continuous approximation to closed-loop DMC control, is introduced here and used to quantify the previous empirical results. The dependence of slow and fast time scales of the closed-loop response on the parameters is examined for move-suppressed and shifted DMC methods. It is found that in move-suppressed DMC the slow control time scale is sharply dependent upon the sampling time and move-suppression parameter and that these difficulties are eliminated in shifted DMC.

Compensator for Internet-Based Advanced Control

Abstract Internet-based control is becoming next generations of control systems, in which time delay and data loss in Internet transmission are the major obstacles for bringing this control system into a reality. This paper proposes new control architecture in cooperated with two compensators to attack this major difficulty. These two compensators are located in the feedback and feed-forward channels in the architecture in order to compensate the control action and assure the stability of the control system. The novel compensators and control system architecture are illustrated and evaluated through a simulation example by using the DMC control algorithm

Neural DMC control strategy for a CSTR in presence of noise

The prime objective of this work is to assess a neural DMC strategy in the case of experimental data corrupted with white noise. The neural DMC structure relies on the simple and innovative dynamic neural model recently developed. (Baratti et al., 2000). A nonisothennal CSTR was considered as benchmark. The perfonnance of the DMC strategy was evaluated in tenns of set-point tracking and disturbance rejection capabilities. The results show that the inaccuracy of the dynamic neural model is overcome by simply integrating the DMC structure with the available on-line measurements even though they are corrupted with white noise.

TRACKING PERFORMANCE OF CONTROL METHODS

In this work, the optimal temperature control of a styrene solution polymerization reactor with two different control algorithms is considered. DMC and PFD control mefhods are used to accomplish the optimal temperature control of the polystyrene reactor. Reactor optimal temperature profiles at different initiator initiation concentrations were obtained by applying maximum principle to the mathematical model of the free radical batch polymerization reactor lo produce polystyrene with desired conversion and molecular weight in a minimum lime. The results obtained from the experimental implementation of DMC and PID controller for the control of optimal temperature path of the polymerization reactor were compared.

Production optimization with operating constraints for a fed-batch reactor with DMC predictive control

This work presents a production optimization approach with operating constraints applied to a fed-batch penicillin process with a DMC predictive controller. A deterministic and non-structured mathematical model of the fed-batch penicillin production process solved by a fourth order variable step Runge–Kutta–Gill algorithm type simulated the system. The production process optimization has been performed through the manipulation of the initial cell and the substrate fed-batch concentrations besides the fed-batch flow rate strategy. In order to carry out this procedure it was necessary to compute a production index that considers product amount and process operating constraints such as cell, substrate and dissolved oxygen concentrations besides the bioreactor final volume through the penalty functions concept. The optimization algorithm was based on the modified simplex method. The DMC control algorithm was implemented in order to stabilize the dissolved oxygen concentration through the agitation speed manipulation. This controller was found to be appropriate to work coupled with a optimization procedure due to the fact that it gives a smooth behavior to this critical constraint even with all disturbs during the convergence.

Application of a novel approach for DMC predictive controller design by response surface analysis in a fed-batch bioreactor

This work presents a novel approach for tuning a predictive DMC controller implemented in a fed-batch penicillin bioreactor in order to stabilize the dissolved oxygen concentration by agitation speed manipulation. The operating process variables were calculated by a deterministic and non-structured model solved by a fourth order Runge-Kutta-Gill numerical technique with variable steps. The parameters of the model were obtained from experiments and the literature. The estimated parameters of the DMC controller were model, prediction and control horizons, suppression factor and reference trajectory. The tuning approach employed complete factorial design in order to estimate the influence of these parameters on the integral of the absolute error between the controlled variable and the set point. Response surface analysis provided the optimal parameters. This study showed negligible influence of model, prediction and control horizons while the suppression factor and reference trajectory were very important for the controller. Another important feature of the DMC controller was the that the parameters had negligible influence on each other making design of the controller easier. The performance of the DMC controller was evaluated using several delay times and sample periods of the controlled variable. The behavior of this predictive controller was better than a PID controller tuned by the Modified Simplex method.

Fuzzy Model - Based DMC for Nonlinear Process Control

This paper proposes a new approach to predictive control of highly nonlinear processes based on a fuzzy model of the Takagi-Sugeno form. Standard Model Based Predictive Control (MBPC) methods use linear process models and are therefore unable to deal with strong process nonlinearities. But, the advantage of linear MBPC is in implementation due to fast optimization algorithms and guaranteed convergence within each time sample. In our approach, step responses for different operating points are extracted on-line from the nonlinear fuzzy model and a modified linear DMC algorithm is used. In this way, all the advantages of both fuzzy modeling of nonlinear processes and DMC control are accomplished. Proposed approach is simple and efficient also for real-time control. In the paper two illustrative examples are shown: a highly nonlinear simulated pH-process and a laboratory scale thermal plant.

A Sensitivity Analysis of Real-Time Optimizer in Industrial Constrained Predictive Control

Abstract At some particular control executions of the industrial predictive control, [DMC]™, the operating targets given by real-time LP optimizer become unstable because of the sensitiveness of the optimal LP-solutions to process noise and disturbances. This paper proposes a practical method of analyzing the sensitivity of LP-solutions of the predictive control, and providing an appropriate stability for the operating targets by either relaxing the control limits or LP coefficients. The proposed method is applied to an industrial ammonia plant of Mitsubishi Chemicals where DMC control is performed.

A practical method of removing ill-conditioning in industrial constrained predictive control

At any control execution of large scale Dynamic Matrix Control [DMC]™, the process may require excessive input movements. This problem arises from the ill-conditioned internal models of the predictive controller. In this paper, a practical method of removing the ill-conditioning is developed by making the best use of the Singular Value Analysis (SVA) and the charateristic of LP optimizer in DMC. The proposed method is applied to an industrial ammonia plant, where non-square (25 controlled - 12 manipulated variables) DMC controller is installed. By redesigning some internal models which are indicated by the method, the resulting controller can suppress the excessive input movements and improve the control performance.

Fuzzy predictive control of highly nonlinear pH process

This paper proposes a new approach to predictive control of highly nonlinear processes based on a fuzzy model of the Takagi-Sugeno form. Standard Model Based Predictive Control (MBPC) methods use linear process models and are therefore unable to deal with strong process nonlinearities. But, the advantage of linear MBPC is in implementation due to fast optimization algorithms and guaranteed convergence within each time sample. In our approach, step responses for different operating points are extracted on-line from the nonlinear fuzzy model and a modified linear DMC algorithm is used. In this way, all the advantages of both fuzzy modeling of nonlinear processes and DMC control are accomplished. For performance evaluation of this simple but efficient approach, a nonlinear pH-process is used.

A Dynamic Model of a Superfractionator: A Test Case for Comparing Distillation Control Techniques

A detailed dynamic simulator of a propylene/propane (C) splitter has been developed to compare dual composition control performance for distillation control techniques. This column represents a large class of industrial distillation control problems: a high reflux ratio, low relative volatility separation with a large numbers of stages (superfractionator). The C3 splitter was designed to produce polymer grade propylene (99.7% propylene) using 234 trays with a reflux ratio of 12.6. The relative volatility was modeled as a function of liquid composition and pressure. Liquid dynamics were modeled using a hydraulic time constant and equimolal overflow was assumed. The simulator was equipped with loosely tuned PI level controls.The simulator, which uses the (L,B) configuration, was bench marked against data from an industrial C splitter. A set of standard test scenarios come with the simulator. One of the tests, which involves periodic changes in feed composition, has been shown to provide a more critical evaluation of controller performance than standard step tests. This simulator has been used earlier to compare control results for a diagonal PI controller, a DMC controller and a nonlinear model based controller.