Control of Inverse Response Process using Model Predictive Controller (Simulation)

Model predictive control is an important model-based control strategy devised for large multiple-input, multiple-output control problems with inequality constraints on the input and outputs. Applications typically involve two types of calculations: (1) a steady-state optimization to determine the optimum set points for the control calculations, and (2) control calculations to determine the input changes that will drive the process to the set points. The success of model-based control strategies such as MPC depends strongly on the availability of a reasonably accurate process model. Consequently, model development is the most critical step in applying MPC. As Rawlings (2000) has noted, “feedback can overcome some effects of poor model, but starting with a poor process model is a kind to driving a car at night without headlight.” Finally the MPC design should be chosen carefully. Model predictive control has had a major impact on industrial practice, with over 4500 applications worldwide. MPC has become the method of choice for difficult control problems in the oil refining and petrochemical industries. However, it is not a panacea for all difficult control problem(Shinkey, 1994; Hugo, 2000). Furthermore, MPC has had much less impact in the order process industries. Performance monitoring of MPC systems is an important topic of current research interest

Nonparametric nonlinear model predictive control

Model Predictive Control (MPC) has recently found wide acceptance in industrial applications, but its potential has been much impeded by linear models due to the lack of a similarly accepted nonlinear modeling or databased technique. Aimed at solving this problem, the paper addresses three issues: (i) extending second-order Volterra nonlinear MPC (NMPC) to higher-order for improved prediction and control; (ii) formulating NMPC directly with plant data without needing for parametric modeling, which has hindered the progress of NMPC; and (iii) incorporating an error estimator directly in the formulation and hence eliminating the need for a nonlinear state observer. Following analysis of NMPC objectives and existing solutions, nonparametric NMPC is derived in discrete-time using multidimensional convolution between plant data and Volterra kernel measurements. This approach is validated against the benchmark van de Vusse nonlinear process control problem and is applied to an industrial polymerization process by using Volterra kernels of up to the third order. Results show that the nonparametric approach is very efficient and effective and considerably outperforms existing methods, while retaining the original data-based spirit and characteristics of linear MPC

Real and Reactive Power Control of Induction Motor Drives

Induction motors are considered the workhorse in the majority of industrial applications. Their rugged, low-maintenance, and efficient designs keep finding new forms of use nowadays. In this work, power control strategies of induction motor drives based on principles of Direct Torque Control and Model Predictive Control are investigated. The proposed methods control the real and reactive power flow into/out of the machine by selecting and applying proper voltage space phasors to the stator. First, the impact of voltage space phasors on real and reactive power variations is explored. Based on these observations, two methods to choose the appropriate voltage space phasors are proposed based on: six-sector and twelve-sector direct power control, and model predictive power control. Methods to calculate reference and motor powers are then introduced. The presence of high currents during the motor start-up period is analyzed and solutions to limit them are proposed. Finally, simulations using ®Matlab ™Simulink are carried out to test the performance of the control strategies under different operating conditions, including presence of motor parameter variations

aVsIs: An Analytical-Solution-Based Solver for Model-Predictive Control With Hexagonal Constraints in Voltage-Source Inverter Applications

The theory of a new analytical-solution-based algorithm for calculating the optimal solution in model-predictive control applications with hexagonal constraints is discussed in this article. Three-phase voltage-source inverters for power electronic and electric motor drive applications are the target of the proposed method. The indirect model-predictive control requires a constrained quadratic programming (QP) solver to calculate the optimal solution. Most of the QP solvers use numerical algorithms, which may result in unbearable computational burdens. However, the optimal constrained solution can be calculated in an analytical way when the control horizon is limited to the first step. A computationally efficient algorithm with a certain maximum number of operations is proposed in this article. A thorough mathematical description of the solver in both the stationary and rotating reference frames is provided. Experimental results on real test rigs featuring either an electricmotor or a resistive-inductive load are reported to demonstrate the feasibility of the proposed solver, thus smoothing theway for its implementation in industrial applications. The name of the proposed solver is aVsIs, which is released under Apache License 2.0 in GitHub, and a free example is available in Code Ocean

Anti-disturbance composite tracking control for a coupled two-tank MIMO process with experimental studies

Coupled multiple-tank systems are very important for a wide range of industrial applications due to their unique uses. However, the liquid level control for the coupled two-tank multi-input multi-output (MIMO) system is quite challenging because it has strong nonlinearity and coupling, and it is susceptible to multiple external disturbances. For this process, this paper proposes a novel anti-disturbance control strategy consisting on a nonlinear composite hierarchical anti-disturbance predictive control (CHADPC). First, a model-based explicit nonlinear model predictive controller (ENMPC) is designed assuming that all disturbances are measurable and its global exponential stability is proved. Then, a nonlinear disturbance observer (DO) is designed to estimate the lumped disturbances. The composite controller handling the estimated disturbances is then proposed. Finally, simulation and experimental tracking control tests under perturbations and comparisons with recently reported works have been carried out to highlight the promising performance of the proposed ENMPC and CHADPC schemes

Multicontroller: an object programming approach to introduce advanced control algorithms for the GCS large scale project

The GCS (Gas Control System) project team at CERN uses a Model Driven Approach with a Framework - UNICOS (UNified Industrial COntrol System) - based on PLC (Programming Language Controller) and SCADA (Supervisory Control And Data Acquisition) technologies. The first' UNICOS versions were able to provide a PID (Proportional Integrative Derivative) controller whereas the Gas Systems required more advanced control strategies. The MultiController is a new UNICOS object which provides the following advanced control algorithms: Smith Predictor, PFC (Predictive Function Control), RST* and GPC (Global Predictive Control). Its design is based on a monolithic entity with a global structure definition which is able to capture the desired set of parameters of any specific control algorithm supported by the object. The SCADA system -- PVSS - supervises the MultiController operation. The PVSS interface provides users with supervision faceplate, in particular it links any MultiController with recipes: the GCS experts are able to capture sets of relevant advanced control algorithm parameters to reuse them later. Starting by exposing the MultiController object design and implementation for a PVSS and Schneider PLC solution, this paper finishes by highlighting the benefits of the MultiController with the GCS applications

Tank Liquid Level Control using NARMA-L2 and MPC Controllers

Liquid level control is highly important in industrial applications such as boilers in nuclear power plants. In this paper a simple liquid level tank is designed based on NARMA-L2 and Model Predictive control controllers. The simple water level tank has one input, liquid flow inn and one output, liquid level. The proposed controllers is compared in MATLAB and then simulated in Simulink to test how the system actual liquid level track the desired liquid level with two input desired signals (step and white noise). The response of the NARMA-L2 controller is then compared with a MPC controller. The results are shown sequentially and the effectiveness of the controller is illustrated

Nonlinear Model Predictive Control: an Optimal Search Domain Reduction

Nonlinear Model Predictive Control (NMPC) is a powerful control method, used in many industrial contexts. NMPC is based on the online solution of a suitable Optimal Control Problem (OCP) but this operation may require high computational costs, which may compromise its implementation in “fast” real-time applications. In this paper, we propose a novel NMPC approach, aiming to improve the numerical efficiency of the underlying optimization process. In particular, a Set Membership approximation method is applied to derive from data tight bounds on the optimal NMPC control law. These bounds are used to restrict the search domain of the OCP, allowing a significant reduction of the computation time. The effectiveness of the proposed NMPC strategy is demonstrated in simulation, considering an overtaking maneuver in a realistic autonomous vehicle scenario

A Real-Time Predictive Vehicular Collision Avoidance System on an Embedded General-Purpose GPU

Collision avoidance is an essential capability for autonomous and assisted-driving ground vehicles. In this work, we developed a novel model predictive control based intelligent collision avoidance (CA) algorithm for a multi-trailer industrial ground vehicle implemented on a General Purpose Graphical Processing Unit (GPGPU). The CA problem is formulated as a multi-objective optimal control problem and solved using a limited look-ahead control scheme in real-time. Through hardware-in-the-loop-simulations and experimental results obtained in this work, we have demonstrated that the proposed algorithm, using NVIDA’s CUDA framework and the NVIDIA Jetson TX2 development platform, is capable of dynamically assisting drivers and maintaining the vehicle a safe distance from the detected obstacles on-thely. We have demonstrated that a GPGPU, paired with an appropriate algorithm, can be the key enabler in relieving the computational burden that is commonly associated with model-based control problems and thus make them suitable for real-time applications