Robust Model Predictive Controller Design for Position DC Servomechanism System

— The objective of this paper is to investigate the Model Predictive Control (MPC) strategy, analyze and compare the control effects with Proportional-Integral-Derivative (PID) control strategy in maintaining a level of spherical tank system using VFD. An advanced control method, MPC has been widely used and well received in a wide variety of applications in process control, it utilizes an explicit process model to predict the future response of a process and solve an optimal control problem with a finite horizon at each sampling instant. In this paper, an algorithm is developed for controlling the speed of the pump using VFD to maintain the level of the spherical tank system. Next, the system is modeled and linearized for analysis and design. Model is implemented in a simulation environment based on LabVIEW. Both MPC and PID control methods are tried to design the controller for the water level system in a spherical tank and compared the results in terms of peak time, settling time, overshoot, and steady-state error under various operational conditions including time delays. The results showed the advantage of MPC for dealing with the system dynamic over PID and could be designed for more complex and fast system dynamics even in presence of constraints and Reduction of power consumption by the pump due to usage of VFD for controlling level by varying the speed of the pump based on the level of the tank. Keywords: Non Linear - Spherical Tank System – Level Control – Variable Frequency Drive – PID Controller – MPC Controller – Modeling - LabVIEW. Cite this Article R. Dhanasekar, S. Vijayachitra, M. Pravin Savaridass. Controlling of Level Process in Nonlinear System Using Variable Frequency Drive. Current Trends in Information Technology. 2019; 9(1): 24–33p.

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

This study proposes a layered hybrid control framework for automation and robotics systems that combines Tube-based Robust MPC (RMPC) as a safety envelope, adaptive parameter estimation to narrow online uncertainty, Sliding Mode Control (SMC) as a high-resilience fail-safe, and Reinforcement Learning (RL) as a performance booster through a warm-start/residual policy scheme that is always constrained by Control Barrier Functions (CBF). The study aims to achieve better tracking performance and computational efficiency without sacrificing safety and constraint compliance. The methodology includes constrained nonlinear plant modeling, design of tube invariance-based RMPC with constraint tightening, RLS/concurrent learning-based adaptive estimator, SMC for infeasibility/model drift conditions, and a CBF-based safety filter formulated as an online small QP. Evaluation is done through ablation studies on multi-domain benchmarks and HIL-style tests, with finite-horizon cost, ITAE, constraint violation rate, and computational latency (average and WCET) metrics. The results show that the RL+CBF configuration reduces the finite-horizon cost by about 15.7% compared to pure RMPC, maintaining a violation rate of ~0.35% (better than RMPC: 0.80%) and significantly below that of unfiltered RL (4.20%). The warm-start RL scheme accelerates the completion time of the MPC solver by ≈15–18% over various prediction horizons and reduces the WCET (26.4 → 23.3 ms), supporting real-time implementation. These findings confirm that the integration of RMPC–Adaptive–SMC–RL(+CBF) effectively bridges model-based optimality, online adaptivity, robust resilience, and learning from data in a single architecture that is safety-certified and feasible for real-world applications demanding high uptime.

Many robust model predictive control (MPC) schemes require the online solution of a computationally demanding convex program. For deterministic MPC schemes, multiparametric programming was successfully applied to move offline most of the computation. In this paper, we adopt a general approximate multiparametric algorithm recently suggested for convex problems and propose to apply it to a classical robust MPC scheme. This approach enables one to implement a robust MPC controller in real time for systems with polytopic uncertainty, ensuring robust constraint satisfaction and robust convergence to a given bounded set

Robust One-step Model Predictive Control for Discrete Time-delay Systems

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.

Two-stage inverters composed of the front DC-DC stage and the rear DC-AC stage are broadly employed for the current tracking. Structures vary according to the different control objectives and application occasions. The concerning structure in this paper is made up of the buck-boost converter stage and the single-phase inverter stage. The simulation results show that the intermediate voltage will affect the total harmonic distortion (THD) values. Moreover, under the same other conditions, a minimum THD can be attained by regulating the intermediate voltage. In this paper, a time-varying observer is introduced for the interface parameters identification based on the robustness of model predictive control (MPC) scheme. Combined with the input voltage, the reference current and grid voltage, the optimal intermediate voltage with lower THD can be determined. Besides, a cascaded proportional-integral (PI) MPC scheme is adopted for the intermediate voltage tracking without steady-state error. Meanwhile, based on the accurate interface parameters obtained by the observer, an adaptive MPC scheme is applied for the output current tracking. The simulation results on MATLAB/Simulink and experimental results on FPGA test bench validate the accuracy of the voltage determination and the effectiveness of the control scheme.

Robust model predictive control and PID control of shell-and-tube heat exchangers

This study aims to examine the use of Proportional Integral Derivative (PID) and Model Predictive Control (MPC) in Load Frequency Control (LFC). A balance between the generation and the load is necessary to ensure the consistency of the electrical supply. These days, MPC techniques are becoming popular due to their merits over conventional PID controllers. In this study, performance of the designed PID and MPC schemes has been tested and compared for two area interconnected power system. This system consists hydro unit in area-1 and three Thermal units in area-2. A step load perturbation scenario in both areas has been obtained to evaluate the effectiveness of the designed control strategies. The results show that both the designed control approaches performed successfully however, the MPC scheme outperform the PID scheme in terms of time domain specifications like reduced oscillation and smaller settling time. It can be concluded that MPC can be used as a secondary controller for LFC applications in future research.

Recently, different algorithms for identification of several uncertain nonlinear models, referred to as Volterra-type models, were introduced in the literature. This work deals with the development of a suitable model-based control algorithm able to cope with the uncertain characterization of those types of models. Particularly, a robust model predictive control (MPC) scheme for multi-input multi-output (MIMO) Volterra-type models is presented. A MPC paradigm is used to control the process under the presence of uncertainties and constraints on the manipulated and controlled variables. A simulation example is given to illustrate the capabilities of the proposed control methodology.

One of the key drivers in gaining the cost-effective, reliable and low-downtime Operation system for major Oil Fields is implementation of Water Injection System (WIS) with capability of online-automated regulation and control. This Paper outlines an application of Proportional, Integral, Derivative (PID) control loop algorithm for Decentralized WIS (with direct injection from Water Source wells to Injection wells by means of high capacity ESP's) used in West Qurna-2 oilfield located in Iraq, operated by LUKOIL. PID control loop algorithm has being used in the industry for the years, but not commonly applied as integral component of Water Injection System allowing to maintain optimal equipment modes. Such limited PID deployment is explained by complexity of operational parameters synchronizing for every Water Injection System component. The decentralized Water Injection System at the West Qurna 2 project is common and united pipeline system for every WellPad that provides water transfer from Water source wells (3-5 wells per WellPad) to injection wells (3-6 wells per WellPad) with line pressure of 70-130 Barg by means of high-capacity ESP equipped with VSD. Water injection process is regulated by controlling both the conventional ESP parameters (frequency, flow rate, motor current and load, Intake and discharge pressures), line pressure, required injection rate by means of flow control valves, and implemented PID control loop algorithm. This paper discusses the techniques of PID control loop algorithm application as the tool for: –Pipeline protection against high pressure without full shutdown of water injection process and–ESP automated regulation to ensure operation inside Recommended Operated Range (ROR) and smoothening of ESP mode when any changes in the system happen (i.e. shutdown of other water source well ESP's at the same WellPad or shutdown of injection well) with immediate feedback to Operator Control Room. Implemented approach allows to decrease number of ESP shutdowns and avoid high jumping of ESP loads that consequently leads to maximizing of ESP run life. Comparative examples and case studies of submersible equipment operation results before and after the application of PID regulation are specified. Mainly by implementing PID regulation and control stable and smooth operation of equipment and ESP's used for WIS (direct injection from Water Source Well to Injection Well) has been achieved. New approach for PID application as a tool for pipeline overpressure protection has been formed, which allowed to reduce Capital Expenses for the WIS construction works. Specified case can be used as example for further development and projects in the oil and gas industry.

Electric load simulator (ELS) plays a vital role in the ground test and simulation of the electromechanical actuator (EMA) of an unmanned aerial vehicle (UAV), which is preferred by researchers for its dynamic response and high precision. However, clearance is an inevitable problem of the ELS system which can introduce backlash influencing the stability of the system. The backlash nonlinear can induce mechanical impact and impose limitations on the quality of loading torque control. In order to increase the accuracy of loading torque, a switching control method is presented to compensate for the backlash nonlinear. A time-optimal sliding mode control (TOSMC) method is used when the ELS system entering backlash, and a nonlinear proportional integral derivative (PID) controller is presented for the different system dynamic. A switching controller is also introduced to acquire suitable moment switching from TOSMC to nonlinear PID controller, ensuring the stability of the system. Experimental tests show that compared with the traditional PID control, this method can effectively reduce the torque overshoot when ELS crossing backlash and guarantee torque tracking speed and precision of ELS.

A methodology is proposed to design a robust Gain-Scheduled Model Predictive Control (MPC) strategy and to quantify the relative advantages of this controller versus a Linear MPC strategy. For the purpose of analysis and controller design, the process is represented by a nonlinear state-affine model identified from input−output data. This model can be split in linear and nonlinear terms where the linear part is used for controller design and the nonlinear part is accounted for as model uncertainty. Then, robust stability and robust performance tests are formulated based on linear matrix inequalities where the manipulated variables weight of the controllers is tuned to maximize performance. The uncertainty bounds used for the robustness tests are obtained in an iterative fashion by using the frequency response of the manipulated variable with respect to the feedback error. The control strategy performance is quantified by the ratio between the error norm and the disturbance norm. Finally, a case study involving a multiple-input−multiple-output bioreactor is presented. The study is able to predict for which range of operation the Gain-Scheduling MPC surpasses the performance of the Linear MPC.

A novel robust model predictive control strategy is presented for a class of discrete-time time-varying system with delays. The modified quadratic Lyapunov-Krasovskii functionals for uncertain discrete-time system with both states and input delays is proposed. A robust model predictive control law is derived such that the closed-loop system is asymptotically stable and a dasiaworst-casepsila infinite horizon performance objective is minimized. A feasible state feedback model predictive control law is obtained by using linear matrix inequalities approach. And then, a convex optimization problem is formulated to design the robust model predictive controller which minimizes the upper bound of the closed-loop performance objective. The robust model predictive controller design is illustrated with numerical examples.

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.