Controlling of Level Process in Non-Linear System Using Variable Frequency Drive

Model predictive control (MPC) is a multi-objective control technique that can handle system constraints. However, the performance of an MPC controller highly relies on a proper prioritization weight for each objective, which highlights the need for a precise weight tuning technique. In this paper, we propose an analytical tuning technique by matching the MPC controller performance with the performance of a linear quadratic regulator (LQR) controller. The proposed methodology derives the transformation of a LQR weighting matrix with a fixed weighting factor using a discrete algebraic Riccati equation (DARE) and designs an MPC controller using the idea of a discrete time linear quadratic tracking problem (LQT) in the presence of constraints. The proposed methodology ensures optimal performance between unconstrained MPC and LQR controllers and provides a sub-optimal solution while the constraints are active during transient operations. The resulting MPC behaves as the discrete time LQR by selecting an appropriate weighting matrix in the MPC control problem and ensures the asymptotic stability of the system. In this paper, the effectiveness of the proposed technique is investigated in the application of a novel vehicle collision avoidance system that is designed in the form of linear inequality constraints within MPC. The simulation results confirm the potency of the proposed MPC control technique in performing a safe, feasible and collision-free path while respecting the inputs, states and collision avoidance constraints.

This paper discusses the difference between two tracking control strategies for a two-link robotic manipulator and compares the tracking results of the two strategies, in terms of accuracy and overshoots prevention. The design and simulation of the control approaches are based on the development of a nonlinear model for the complex dynamics of the manipulator system. The system modeling is based on Euler-Lagrange equations. The first strategy is a Proportional Integral Derivative (PID) control. Its design is based on the trial and error online tuning method, which is applied directly to control the two-joints of the robotic manipulator. On the other hand, the second strategy is a novel Model Predictive Control (MPC) strategy, which its design is based on feedback linearization analysis technique that utilizes the nonlinear model of the manipulator’s complex dynamics to design a feedback linearization control strategy to work as a primary control loop.

Lane changing is one of the crucial tasks for an autonomous vehicle to avoid an obstacle. This task can be performed by controlling the throttle, brake, and steering actuators appropriately based on the analysis of the vehicle’s surroundings. The problem with lane changing is that the control strategy is too complex and needs a high processor for real-time data analysis. In addition, lane changing involves high-level control for vehicle trajectory and low-level control for controlling the steering actuator. This study proposed a well-known method, namely Model Predictive Control (MPC), to determine the vehicle’s lateral position and yaw angle during lane changing maneuver. The optimum steering angle command can control the steer-by-wire (SBW) system from the lateral position and yaw angle in MPC. The Proportional-Integral-Derivative (PID) controller is implemented to control the steering wheel angle in the SBW’s system. Then, the SBW system will turn the wheel of the vehicle plant. From the simulation result, the PID controller can converge the error although the vehicle’s speed is increasing. The result shows that the mean absolute error (MAE) of the SBW system decreases slightly from 0.0115 to 0.0079 as the speed increase from 16 to 41 km/h. From this study, it can be concluded that the MPC and PID controllers can control the vehicle’s trajectory during lane changing by calculating an optimum lateral motion and yaw angle to provide an optimum steering angle for the vehicle to change lanes successfully.KeywordsSteer-by-wire (SBW)Model Predictive Control (MPC)PID controlLane changingAutonomous vehicle

This paper presents the comparative study of Model Predictive Controller (MPC) with PID Controller implemented for flow control loop in LABVIEW. Classical PID controller is mostly used in process instrumentation yet tuning of PID controller is the crucial part to be dealt with. Moreover, changes in the parameters of the loop components affects the tuning of the controller, hence over a period of time retuning of PID controller is required. On the other hand MPC controller works on the process model which predicts and optimizes the process performance. Here, Flow control loop performance was studied for MPC and PID controller. MPC worked on 1st order state space system model and PID was tuned using Internal Model Control (IMC) method. The result of implementation shows that, MPC controller performs much better than PID controller. Set point of with less settling time and zero peak overshoot was observed in MPC whereas PID has comparatively more settling time and peak overshoot.

Path-tracking control is a crucial process for autonomous vehicles, ensuring that the vehicle drives safely along the reference path, and the suitable controller parameters ensure the accuracy and stability of this process. To enhance the adaptability of traditional path-tracking controller parameters, this paper proposes an adaptive model predictive control (MPC) controller based on a preview-based PID controller and deep deterministic policy gradient (DDPG) algorithm to achieve adaptive tuning of the controller parameters. Starting with the design of the dynamics tracking error model of the vehicle and the MPC controller. Based on the actor-critic reinforcement learning architecture, the DDPG agent is designed to tune the prediction horizon and weight matrix of the MPC controller. A preview-based PID controller is proposed to improve the efficiency and stability of reinforcement learning and compensate for the error in vehicle modeling. The improved algorithm performance is verified through the simulation scenarios of high-speed lane changing and accelerated overtaking scenarios constructed by MATLAB/Simulink. The results show that the improved algorithm significantly improves the adaptive ability of the traditional MPC controller to time-varying conditions and achieves higher tracking accuracy and stability.

Cascade control of effluent nitrate and ammonium in an activated sludge process

This thesis addresses the design of optimization-based control laws for the case where convergence to a desired set-point, minimization of an arbitrary performance index, or a combination of the two, is the desired objective. The results are developed within the sample-data Model Predictive Control (MPC) framework considering constrained nonlinear continuous-time time-varying dynamical systems. For a given time sampling, a sample-data MPC control strategy consists of i) choosing among all future finite horizon predictions of state and input trajectories of the system the one that minimizes the given performance index, ii) applying the optimal input trajectory to the system until a new time sample is reached, and iii) iterating this process. The performance index is chosen to describe the specific control problem under consideration. In a classic Tracking-MPC framework, where the main goal is to steer the state of the system to a desired steady-state, the performance index is properly chosen to penalize the distance from the current state to a desired one. In order to capture more complex control objectives, in recent years a growing attention has been dedicated to a new class of controllers that goes under the name of Economic-MPC. Here, the term economic is used to stress the fact that the performance index is a general index of interest that we wish to minimize, e.g., economic, which does not denote the distance to a desired set point. This setting makes full use of the potentialities of optimization-based control strategies. Although, it comes with disadvantages. In fact, by choosing an arbitrary performance index, it is difficult to predict, and therefore certify, the evolution of the closed-loop system, which could potentially manifest undersirable behaviors. This thesis provides analysis and certification of a variety of closed-loop behaviors stemming from the use Economic-MPC controllers. A set of tools for design of provably correct MPC controllers is provided for the case where the performance index is of the Tracking-MPC type, purely economic, or a combination of the two. The results focus the certification of both closed-loop economic performance and closed-loop state evolution. The proposed strategies are applied to a range of motion control problems for underactuated vehicles. An MPC controller for Trajectory-Tracking and Path-Following with convergence guarantees is first proposed and then extended, using the results presented on Economic-MPC, to address the control problems of distributed formation keeping, energy efficient trajectory-tracking, and target-following through highly observable trajectories.

Nowadays, classical Proportional-Integral-Derivative (PID) controllers are mainly used for operation of most hydro turbine governors. Although the PID controllers have many advantages, such as simple implementation, their main weaknesses are resiliency to the disturbances and uncertainties, as well as the integral windup. Therefore, a hydro turbine governor's speed controller based on the model predictive control (MPC) is developed and applied to a non-linear simulation model of a laboratory hydro power plant. The frequency response of the hydro power plant in the islanded mode with MPC controller is compared to the classical PID controller. The simulation results clearly indicate that the MPC provides better performance than the classical PID controller. In that regard, it can be concluded that this type of controller has a practical implementation potential.

Frequency regulation balances the supply and demand of power within a power network to maintain the network's frequency about its nominal value. Using residential loads to supply frequency regulation while respecting user comfort can require costly communication infrastructure. Developing control algorithms that are resilient to communication network imperfections, e.g., delays, can allow the usage of less costly, lower reliability communication infrastructure. Previous work developed a model predictive control (MPC) approach that enables aggregations of loads to track a desired power signal despite communication delays. This work presents a novel model-reduction method to produce a minimal aggregate load model, and then uses state augmentation to include the effect of input delays. This allows the reformulation of the MPC controller into a linear controller yielding a closed-form control law that reduces online computation. We present simulations that compare the MPC and linear controllers' tracking ability given input delays. The linear controller neglects input constraints explicitly included within the MPC controller, and we characterize the impact of neglecting these constraints through simulations. The linear controller reduces the computation time by a factor of 100, but the RMS tracking error increases 11% in the cases studied.

Multi-objective optimization of indoor air quality control and energy consumption minimization in a subway ventilation system

As offshore wind capacity could grow substantially in the coming years, floating offshore wind turbines (FOWTs) are particularly expected to make a significant contribution to the anticipated global installed capacity. However, FOWTs are prone to several issues due partly to environmental perturbations and their system configuration which affect their performances and jeopardize their structural integrity. Therefore, advanced control mechanisms are required to ensure good performance and operation of FOWTs. In this study, a model predictive control (MPC) is proposed to regulate FOWTs’ power, reposition their platforms to reach predefined target positions and ensure their structural stability. An efficient nonlinear state space model is used as the internal MPC predictive model. The control strategy is based on the direct manipulation of the thrust force using three control inputs, namely the yaw angle, the collective blade pitch angle, and the generator torque without the necessity of additional actuators. The proposed controller accounts for the environmental perturbations and satisfies the system constraints to ensure good performance and operation of the FOWTs. A realistic scenario for a 5-MW reference wind turbine, modeled using OpenFAST and Simulink, has been provided to demonstrate the robustness of the proposed MPC controller. Furthermore, the comparison of the MPC model and a proportional-integral-derivative (PID) model to satisfy the three predefined objectives indicates the superior performances of the MPC controller.

A FCC model is used to compare five different Model Predictive Control (MPC) strategies. The FCC process is a complex petrochemical unit with catalyst recycling that makes its behaviour highly nonlinear. The FCC comprises a riser, a separator and a regenerator with important heat coupling due to the endothermic cracking reactions of gas oil in the riser and the exothermic combustion reactions in the regenerator. The riser and the regenerator exhibit fast and slow dynamics respectively. The temperatures at riser top and in the regenerator should be controlled by manipulation of catalyst and air flow rates. All these nonlinear and coupled characteristics render the multivariable control problem difficult and thus the FCC process constitutes a valuable benchmark for comparing control strategies. Here, the performances of Dynamic Matrix Control, Quadratic Dynamic Matrix Control, MPC control with penalty on the outputs, NonLinear MPC control, Observer Based MPC control are compared.

Superheaters are heat exchangers that transfer energy from flue gas to superheated steam. Controlling of steam temperature at the superheater outlet is a very difficult task in thermal power plant. A superheater model and an appropriate optimal control strategy are the essential tools for improving the accuracy of this control system. Superheater is simulated as a unit of a control loop that generates steam of desired state values. To simulate the steam superheater on the computer, the heat exchanger assembly is described by sets of non-linear partial differential equations (PDE). The equations are then solved by modified finite difference method. A linearized model of the superheater is identified using system identification tool box in order to design optimal control strategies for Superheated Steam (SHS) temperature system. Then to tune Proportional Integral Derivative (PID) controller parameters optimally, different tuning schemes were applied to superheated steam temperature system. However, due to new challenges in keeping up with rapid changes in load demand and the transients, performance of the PID controller is far from being optimal. Hence a Model Predictive Controller (MPC) is designed for the superheated steam temperature regulation in a supercritical coal-fired power plant. Compared with the performance of the plant using a conventional PID controller, the steam temperature controlled by the MPC is found to be more optimal which leads to more efficient plant operation and energy saving.

In power system operation and control, automatic generation control (AGC) scheme is used to maintain the balance in between the net generation and the total load in a system by supplying sufficient power with good quality. Controller plays an important role in AGC scheme. This paper presents a control scheme based on model predictive control (MPC) concept for multiarea power system in deregulated scenario. For guaranteed stability each area controller is designed independently. MPC controller is designed in such a way that for any load perturbation, generators can regulate the real power output to settle down deviations in frequency and tie line power exchange at desired values. The proposed MPC control scheme has been implemented for deregulated two area thermal power system. The performance of MPC controller is also checked for 75 bus Indian power system. Various load conditions has been considered to evaluate the performance of controller. The results and performance analysis are presented to justify the robustness & effectiveness of control scheme. The comparative performance analysis of MPC scheme and conventional proportional integral controller (PID) control is also presented.

A model predictive control for a multi-chiller system in data center considering whole system energy conservation