Model predictive control block diagram

Documentation Help Center. The controller then calculates the control input that will optimize plant performance over a specified future time horizon. The first step in model predictive control is to determine the neural network plant model system identification. Next, the plant model is used by the controller to predict future performance.

model predictive control block diagram

The following section describes the system identification process. This is followed by a description of the optimization process. The first stage of model predictive control is to train a neural network to represent the forward dynamics of the plant.

The prediction error between the plant output and the neural network output is used as the neural network training signal. The process is represented by the following figure:. The neural network plant model uses previous inputs and previous plant outputs to predict future values of the plant output.

The structure of the neural network plant model is given in the following figure. This network can be trained offline in batch mode, using data collected from the operation of the plant.

You can use any of the training algorithms discussed in Multilayer Shallow Neural Networks and Backpropagation Training for network training. This process is discussed in more detail in following sections.

The model predictive control method is based on the receding horizon technique [ SoHa96 ]. The neural network model predicts the plant response over a specified time horizon. The predictions are used by a numerical optimization program to determine the control signal that minimizes the following performance criterion over the specified horizon.

The following block diagram illustrates the model predictive control process. The controller consists of the neural network plant model and the optimization block.

The controller block is implemented in Simulink, as described in the following section. This section shows how the NN Predictive Controller block is used. See the Simulink documentation if you are not sure how to do this.

This step is skipped in the following example.

Model predictive control

An example model is provided with the Deep Learning Toolbox software to show the use of the predictive controller. A diagram of the process is shown in the following figure.Model predictive control MPC is an advanced method of process control that is used to control a process while satisfying a set of constraints.

It has been in use in the process industries in chemical plants and oil refineries since the s. In recent years it has also been used in power system balancing models [1] and in power electronics [2]. Model predictive controllers rely on dynamic models of the process, most often linear empirical models obtained by system identification. The main advantage of MPC is the fact that it allows the current timeslot to be optimized, while keeping future timeslots in account.

This is achieved by optimizing a finite time-horizon, but only implementing the current timeslot and then optimizing again, repeatedly, thus differing from Linear-Quadratic Regulator LQR. Also MPC has the ability to anticipate future events and can take control actions accordingly. PID controllers do not have this predictive ability. MPC is nearly universally implemented as a digital control, although there is research into achieving faster response times with specially designed analog circuitry.

The models used in MPC are generally intended to represent the behavior of complex dynamical systems. The additional complexity of the MPC control algorithm is not generally needed to provide adequate control of simple systems, which are often controlled well by generic PID controllers.

Common dynamic characteristics that are difficult for PID controllers include large time delays and high-order dynamics.

Understanding Model Predictive Control, Part 6: How to Design an MPC Controller with Simulink

MPC models predict the change in the dependent variables of the modeled system that will be caused by changes in the independent variables. In a chemical process, independent variables that can be adjusted by the controller are often either the setpoints of regulatory PID controllers pressure, flow, temperature, etc.

Independent variables that cannot be adjusted by the controller are used as disturbances. Dependent variables in these processes are other measurements that represent either control objectives or process constraints.

MPC uses the current plant measurements, the current dynamic state of the process, the MPC models, and the process variable targets and limits to calculate future changes in the dependent variables. These changes are calculated to hold the dependent variables close to target while honoring constraints on both independent and dependent variables. The MPC typically sends out only the first change in each independent variable to be implemented, and repeats the calculation when the next change is required.

While many real processes are not linear, they can often be considered to be approximately linear over a small operating range. Linear MPC approaches are used in the majority of applications with the feedback mechanism of the MPC compensating for prediction errors due to structural mismatch between the model and the process.

In model predictive controllers that consist only of linear models, the superposition principle of linear algebra enables the effect of changes in multiple independent variables to be added together to predict the response of the dependent variables.

This simplifies the control problem to a series of direct matrix algebra calculations that are fast and robust. When linear models are not sufficiently accurate to represent the real process nonlinearities, several approaches can be used. The process can be controlled with nonlinear MPC that uses a nonlinear model directly in the control application.

The nonlinear model may be in the form of an empirical data fit e. The nonlinear model may be linearized to derive a Kalman filter or specify a model for linear MPC. An algorithmic study by El-Gherwi, Budman, and El Kamel shows that utilizing a dual-mode approach can provide significant reduction in online computations while maintaining comparative performance to a non-altered implementation. The proposed algorithm solves N convex optimization problems in parallel based on exchange of information among controllers.

MPC is based on iterative, finite-horizon optimization of a plant model. Only the first step of the control strategy is implemented, then the plant state is sampled again and the calculations are repeated starting from the new current state, yielding a new control and new predicted state path.

The prediction horizon keeps being shifted forward and for this reason MPC is also called receding horizon control. Although this approach is not optimal, in practice it has given very good results. Much academic research has been done to find fast methods of solution of Euler—Lagrange type equations, to understand the global stability properties of MPC's local optimization, and in general to improve the MPC method.

To some extent the theoreticians have been trying to catch up with the control engineers when it comes to MPC.Documentation Help Center. An inlet stream of reagent A feeds into the tank at a constant rate. A first-order, irreversible, exothermic reaction takes place to produce the product stream, which exits the reactor at the same rate as the input stream. Coolant Temperature Tc — Reactor coolant temperature K.

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The control objective is to maintain the residual concentration, CAat its nominal setpoint by adjusting the coolant temperature, Tc. The reactor temperature, Tis usually controlled. However, for this example, ignore the reactor temperature, and assume that the residual concentration is measured directly.

In the Block Parameters dialog box, on the General tab, in the Additional Inports section, check the Measured disturbance md option. Click Apply to add the md inport to the controller block.

In the Simulink model window, connect the Feed Temperature block output to the md inport. This step requires Simulink Control Design software to linearize the Simulink model. The manipulated variable, measured disturbance, and measured output are already assigned to their respective Simulink signal lines, which are connected to the MPC Controller block.

In the Simulink model window, click the output signal from the Feed Concentration block. To compute such an operating point, add the CA signal as a trim output constraint, and specify its target constraint value.

The CA signal can now be used to define output specifications for calculating a model steady-state operating point.

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In the Trim the model dialog box, in the Outputs tab, check the box in the Known column for Channel-1 and specify a Value of 2. This setting constrains the value of the output signal during the operating point search to a known value. In the Edit dialog box, on the State tab, in the Actual dx column, the near-zero derivative values indicate that the computed operating point is at steady-state.

Click Initialize model to set the initial states of the Simulink model to the operating point values in the Actual Values column. Doing so enables you to later simulate the Simulink model at the computed operating point rather than at the default model initial conditions. To reset the model initial conditions, for example if you delete the exported operating point, clear the Input and Initial state parameters.

model predictive control block diagram

The linearized plant model is added to the Data Browser. Also, the following are added to the Data Browser :. A default MPC controller created using the linearized plant as an internal prediction model.

In the Input and Output Channel Specifications dialog box, in the Name column, specify meaningful names for each input and output channel. In the Unit column, specify appropriate units for each signal. The Nominal Value for each signal is the corresponding steady-state value at the computed operating point. To do so, the controller must reject both measured and unmeasured disturbances. In the Simulation Scenario dialog box, in the Reference Signals table, in the Signal drop-down list select Constant to hold the output setpoint at its nominal value.

In the Measured Disturbances table, in the Signal drop-down list, select Step. Specify a step Size of 10 and a step Time of 0. In the Data Browserunder Scenariosclick scenario1.Based on a generated model of this system, we design a set of Control Input sequences iteratively at successive time steps over some horizon from a current state and use this as the control law in a feedback loop.

Model Predictive Control historically s came about as a controller form, from the level of accuracy of mathematical models scientist and engineers have been able to come up with over the years. Hybrid methods: Merges MPC and other forms of control technology as its controller.

MPC is based on the model and the prediction model is utilized. The MPC algorithm is based on the model derived. MPC pays more attention to the function than to the formulation of the model. The function of a prediction model is based on the past information and the future inputs to predict the future output. Any information as long as it has the function of prediction can be used as the predictor model irrespective of the concrete form.

This simply means a transfer function of input output relationship or even a state space representation of a system qualifies The key point that MPC differs from other control techniques is that MPC adopts the receding horizon optimization and the control moves are implemented in a receding horizon manner. While the optimal control rationale is adoptedMPC does not discard the feedback in the traditional control techniques. Feedback is used to overcome disturbances and achieving closed-loop stability.

The MPC utilizes feedback correction. The effect of feedback is realized in adaptive MPC by online updates of the system model and a PID feedback controller used as transparent control is applied. Model based Prediction: On the subject prediction, two questions have to be answered.

Predictions are based on the model. Past information and information about the state of the state of the system is used to do this. The main requirement is that the cost depends on the future control and the low value of cost implies good closed-loop performance.

Here good is a predefined for the system in question. An MPC takes systematic account if constraints and allows for the compensations to give better performance and still keeps the robustness of the unconstrained control laws for each time step. Constraint handling depends on the MPC algorithm adopted Minimization of a quadratic function subject to linear constraints Convex and therefore fundamentally tractable Solution methods Active set method: Determination of the active set of constraints on the basis of the KKT condition Interior point method: Use of barrier function to trap the solution inside the feasible region, Newton iteration.

Deciding on the type of horizon to be used to be implemented depends on some base parameters of the system and the control scheme decided upon.This paper introduces an indirect adaptive fuzzy model predictive control strategy for a nonlinear rotational inverted pendulum with model uncertainties.

In the first stage, a nonlinear prediction model is provided based on the fuzzy sets, and the model parameters are tuned through the adaption rules.

In the second stage, the model predictive controller is designed based on the predicted inputs and outputs of the system. The control objective is to track the desired outputs with minimum error and to maintain closed-loop stability based on the Lyapunov theorem.

Combining the adaptive Mamdani fuzzy model with the model predictive control method is proposed for the first time for the nonlinear inverted pendulum. Moreover, the proposed approach considers the disturbances predictions as part of the system inputs which have not been considered in the previous related works.

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Thus, more accurate predictions resistant to the parameters variations enhance the system performance using the proposed approach. A classical model predictive controller is also applied to the plant, and the results of the proposed strategy are compared with the results from the classical approach.

Results proved that the proposed algorithm improves the control performance significantly with guaranteed stability and excellent tracking.

Keywords: Indirect adaptive fuzzy; Model predictive control; Nonlinear rotational inverted pendulum; Model uncertainties; Lyapunov stability theorem. Model Predictive Control MPC strategy has been recently known as a powerful control method for industrial applications, especially for highly nonlinear systems with uncertainties and constraints [1]. Defining an accurate prediction model is a difficult problem in predictive control algorithms since in practical applications the certain mathematical model of the system is not accessible.

Moreover, when the system is nonlinear, the optimization problem becomes non-convex, and there is no benefit from the use of standard prediction models in the predictive algorithms. Researchers have used various system modeling approaches such as neural networks and fuzzy systems to obtain accurate prediction model for predictive algorithms []; however, most of the related works in this area are applied to either a linearized plant or a nonlinear plant without considering its uncertainties.

Fuzzy systems are designed based on previous experiences or specific knowledge of the system. Indeed, if-then fuzzy rules are very applicable in system modeling, especially in modeling unknown nonlinear systems with uncertainties.

Among fuzzy systems, adaptive fuzzy systems are more powerful because their model parameters are robust to uncertainties and disturbances. Adaptive fuzzy systems are categorized into two frameworks: indirect adaptive fuzzy systems, and direct adaptive fuzzy systems. In designing an indirect adaptive fuzzy system, a fuzzy system is first constructed, and then its parameters are regulated based on the adaption rules. This paper presents how a controller based on model predictive control theory can be developed based on an indirect adaptive fuzzy model.

Nonlinear rotational inverted pendulum system is highly nonlinear with time-varying parameters and model uncertainties. A nonlinear Mamdani fuzzy model is considered as the prediction model.

Combination of the fuzzy modeling approach and model predictive control method is applied for the first time to the nonlinear rotational inverted pendulum benchmark problem. To evaluate the effectiveness of the proposed control methodology, a classical model predictive controller is also designed for the system, and the results of the two controllers are compared.

The rest of the paper is organized as follows. The system model and its parameters are described in section II. In section IV, the simulation results are shown, and the last section provides the conclusions.Documentation Help Center. The controller then calculates the control input that will optimize plant performance over a specified future time horizon. The first step in model predictive control is to determine the neural network plant model system identification.

Next, the plant model is used by the controller to predict future performance. The following section describes the system identification process.

This is followed by a description of the optimization process. The first stage of model predictive control is to train a neural network to represent the forward dynamics of the plant. The prediction error between the plant output and the neural network output is used as the neural network training signal. The process is represented by the following figure:. The neural network plant model uses previous inputs and previous plant outputs to predict future values of the plant output.

The structure of the neural network plant model is given in the following figure. This network can be trained offline in batch mode, using data collected from the operation of the plant. You can use any of the training algorithms discussed in Multilayer Shallow Neural Networks and Backpropagation Training for network training.

This process is discussed in more detail in following sections. The model predictive control method is based on the receding horizon technique [ SoHa96 ]. The neural network model predicts the plant response over a specified time horizon. The predictions are used by a numerical optimization program to determine the control signal that minimizes the following performance criterion over the specified horizon.

The following block diagram illustrates the model predictive control process. The controller consists of the neural network plant model and the optimization block. The controller block is implemented in Simulink, as described in the following section.

This section shows how the NN Predictive Controller block is used.

Model Predictive Control

See the Simulink documentation if you are not sure how to do this. This step is skipped in the following example. An example model is provided with the Deep Learning Toolbox software to show the use of the predictive controller.

model predictive control block diagram

A diagram of the process is shown in the following figure. The objective of the controller is to maintain the product concentration by adjusting the flow w 1 t. The level of the tank h t is not controlled for this experiment. This command opens the Simulink Editor with the following model. The NN Predictive Controller block signals are connected as follows:.The utilization of renewable energy sources has been rising from the last few years due to their enormous advantages. This paper presents the design of bladeless wind turbine utilizing the principles of the wind.

It involves design of model predictive controller based maximum power point tracking system which tracks the voltage, current and power of the bladeless wind turbine. This paper also involves the design of water pumping system with the utilization of single phase induction motor and so the design of single phase inverter has been proposed. It presents the battery management system which continuously monitors the charging and discharging states of the battery based up on the power requirement.

The day to day changes in the environment lead to look for an alternative to design a system which overcomes the global warming effect and provides a healthy life. So natural energy sources provide a better solution for the above mentioned problem. Among the several non-conventional energy sources like solar, wind, tidal, biomass, hydro energy, wind has been chosen due to its nature of [] unlimited availability. But from several years there has been wind turbine which has been in usage for generating electricity using wind energy.

Even it involves certain disadvantages such as necessity of higher wind speeds, higher installation costs.

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Due to these disadvantages, there has been lagging of wind energy usage in the area of residential applications. So, to get rid of these drawbacks, a novel wind turbine has been proposed which works on the principle of oscillatory motion instead of rotatory motion with the help of vertical cylindrical structure. To convert the oscillatory motion, transducer has been chosen due to its accuracy, reliability and efficiency. As the wind speeds varies all the times, a controller has to be used to control the power produced due to the varying wind conditions.

Maximum power point tracking [] has been chosen as a controller for monitoring the system voltage, current and power. Among several power point trackers, perturb and observe tracking has been chosen which tracks the system voltage, current and power with several perturbations. During the lower wind speeds and no wind speeds conditions, there has to be a device which provides the continuous power supply reducing interruptions.

Lead acid battery has been chosen as an energy storage device due to its advantages like simple in maintenance, longer life time, longer charge and discharge cycles. The single-phase induction motor has been chosen for water pumping applications [11, 12] due to its advantages like simple and rugged in construction. The paper is organized as follows; the first section of the paper is dealing with the block diagram explanation of proposed vortex wind turbine. The second section deals with the MPPT with MPC controller and the third part discusses with the battery management system, fourth section presents the software realization of proposed system, results and discussion of the proposed system and the last part deals with the conclusion.

The block diagram of the proposed wind turbine is shown in below Figure 1. It consists of tapered cylindrical mast producing oscillations with the flow of wind, transducer for converting the oscillatory motions of the cylinder into electricity, rectifier for the conversion of AC power into DC power. Also, the system consists of high gain converter producing higher DC output voltage than the input voltage with lesser duty cycle.

As the wind is not constant, the output power may change due to varying weather conditions, so maximum power point tracking has been designed which obtains voltage and current of the rectifier as inputs. Based up on the voltage and current values, MPPT tracker provides duty cycle for the converter to obtain the higher output voltage.

The DC output of the high gain converter is fed to the single phase three level inverter which converts DC output into single phase AC output. This single-phase AC output has been fed to the single-phase induction motor for pumping the water which can be used for residential water pumping. Also, battery management controller has been used which obtains generated power and load power as inputs.

Up on the comparison of the powers, battery will be charged or discharged. When wind passes over the mast, vortex vibrations are generated around the mast of mass M, placed on the spring of stiffness S and then it oscillates with a force of F which results the displacement of the mast.

The displacement performed by the cylindrical mast can be expressed as. With this displacement as an input, the output will be generated across the output terminals of the transducer which can be expressed as.

The generated output at the output terminals of the transducer will be fed to the single-phase diode bridge rectifier whose DC output voltage can be given as. This DC output will be fed to the high gain-based converter whose output can be expressed as. This DC output from the high gain-based converter is fed to the single-phase inverter whose output voltage can be given as. This output will be fed to the single-phase induction motor whose torque expression can be given as.


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