Nonlinear Model Predictive Control Technique Research Articles

Variable speed limit control scheme for vehicle in the vicinity of signalized intersection

In this paper, a variable speed limit control approach for improving the efficiency of signalized intersection is proposed from the cyber-physical view. In the proposed control scheme, vehicle-to-vehicle communication is being utilized fully such that the preceding vehicle’s optimal information is exploited for optimizing the motion of the current vehicle. To prepare the control scheme for dynamical optimization, nonlinear model predictive control technique is used to optimally control vehicle speed limit and a new traffic model is constructed by considering vehicle dynamics. Moreover, this paper evaluates the influence of communication distance on the traffic. The results show that extending communication distance in the vicinity of signalized intersection can improve vehicles’ average fuel economy.

Simulation of three-phase induction motor using nonlinear model predictive control technique

In this paper, nonlinear model predictive control (NMPC) has been used to control an induction motor (IM). The IM model that is used in the control is third or fifth order model that is based in th...

A Real-Time Path-Planning Algorithm based on Receding Horizon Techniques

In this article we present a real-time path-planning algorithm that can be used to generate optimal and feasible paths for any kind of unmanned vehicle (UV). The proposed algorithm is based on the use of a simplified particle vehicle (PV) model, which includes the basic dynamics and constraints of the UV, and an iterated non-linear model predictive control (NMPC) technique that computes the optimal velocity vector (magnitude and orientation angles) that allows the PV to move toward desired targets. The computed paths are guaranteed to be feasible for any UV because: i) the PV is configured with similar characteristics (dynamics and physical constraints) as the UV, and ii) the feasibility of the optimization problem is guaranteed by the use of the iterated NMPC algorithm. As demonstration of the capabilities of the proposed path-planning algorithm, we explore several simulation examples in different scenarios. We consider the existence of static and dynamic obstacles and a follower condition.

Optimization of Bioethanol In Silico Production Process in a Fed-Batch Bioreactor Using Non-Linear Model Predictive Control and Evolutionary Computation Techniques

Due to growing worldwide energy demand, the search for diversification of the energy matrix stands out as an important research topic. Bioethanol represents a notable alternative of renewable and environmental-friendly energy sources extracted from biomass, the bioenergy. Thus, the assurance of optimal growth conditions in the fermenter through operational variables manipulation is cardinal for the maximization of the ethanol production process yield. The current work focuses in the determination of optimal control scheme for the fermenter feed rate and batch end-time, evaluating different parametrization profiles, and comparing evolutionary computation techniques, the genetic algorithm (GA) and differential evolution (DE), using a dynamic real-time optimization (DRTO) approach for the in silico ethanol production optimization. The DRTO was able to optimize the reactor feed rate considering disturbances in the process input. Open-loop tests results obtained for the algorithms were superior to several works presented in the literature. The results indicate that the interaction between the intervals of DRTO cycles and parametrization profile is more significant for the GA, both in terms of ethanol productivity and batch time. In general lines, the present work presents a methodology for control and optimization studies applicable to other bioenergy generation systems.

From Consumption to Prosumption - Operational Cost Optimization for Refrigeration System With Heat Waste Recovery

Implementation of a liquid cooling transforms a refrigeration system into a combined cooling and heating system. Reclaimed heat can be used for building heating purposes or can be sold. Carbon dioxide based refrigeration systems are considered to have a particularly high potential for becoming efficient heat energy producers. In this paper, a CO2 system that operates in the subcritical region is examined. The modelling approach is presented, and used for operation optimisation by way of non-linear model predictive control techniques. Assuming that the heat is sold, it turns out that the system has negative operational cost. Depending on the choice of objective function daily revenue varies from about 7.9 [eur] to 11.9 [eur].

Integrated Path Planning and Tracking Control of an AUV: A Unified Receding Horizon Optimization Approach

This paper attempts to develop a unified receding horizon optimization (RHO) scheme for the integrated path planning and tracking control of an autonomous underwater vehicle (AUV). Considering that the effective sensing range of onboard sensors is practically short, we formulate the path planning into RHO problems with the spline path template. The planned path is subsequently viewed as the state trajectory of a virtual reference system having the same kinematic and dynamic properties as the AUV's. Appropriately constructed error dynamics makes the AUV tracking control equivalent to the regulation problem of the error dynamic system, which facilitates the derivation of theoretical results via nonlinear MPC techniques. The model predictive control (MPC) tracking controller is designed so that closed-loop stability can be ensured. Due to the inherent RHO nature, both the path planning and tracking control are incorporated into an unified scheme. Simulation studies are conducted using a realistic dynamic model of the Falcon AUV, which was created in our previous experimental work. The simulation results demonstrate the effectiveness of the proposed control algorithm.

A Nonlinear, MPC-Based Motion Cueing Algorithm for a High-Performance, Nine-DOF Dynamic Simulator Platform

The use of dynamical driving simulators is nowadays common in many different application fields, such as driver training, vehicle development, and medical studies. Platforms with different mechanical structures have been designed, depending on the particular application and the corresponding targeted market. The effectiveness of such devices is related to their capabilities of well reproducing the driving sensations, and hence, it is crucial that the motion control strategies generate both realistic and feasible inputs to the platform, to ensure that it is kept within its limited operation space. Such strategies are called motion cueing algorithms (MCAs). In this brief, we describe an MCA based on nonlinear model predictive control (MPC) techniques, for a nine-degree of freedom simulator based on a hexapod mounted on a flat base moved by a tripod, exhibiting highly nonlinear behavior. The algorithm has been evaluated in a simulation environment. Simulation results show that the full exploitation of the working area is achieved, while managing at best all the limitations given by the particular structure and preserving the easiness and intuitiveness of tuning, which is typical of linear MPC-based approaches.

Design and Implementation of a Nonlinear PI Predictive Controller for a Grid-Tied Photovoltaic Inverter

This paper presents the design, implementation, and performance testing of a nonlinear proportional-integral (PI) predictive controller for a grid-tied inverter used in photovoltaic systems. A conventional cascade structure is adopted to design the proposed controller, where the outer loop is used to regulate the dc-link voltage, and the inner loop is designed as a current controller for adjusting the active and reactive powers injected into the grid. For each loop, the controller is derived based on combining a continuous-time nonlinear model predictive control and nonlinear disturbance observer techniques. It turns out that the composite controller reduces to a nonlinear PI controller with a predictive term that plays an important role in improving tracking performance. The salient feature of the proposed approach is its ability to approximately preserve the nominal tracking performance during the startup phase. Both simulation and experimental results are provided to demonstrate the effectiveness of the proposed approach in terms of nominal performance recovery, disturbance rejection, and current control.

Parallel Interacting Multiple Model-Based Human Motion Prediction for Motion Planning of Companion Robots

We propose in this paper an autonomous motion planning framework for companion robots to accompany humans in a socially desirable manner, which takes safety and comfort requirements into account. The overall framework consists of two parts: first, a novel parallel interacting multiple model-unscented Kalman filter (PIMM-UKF) approach is developed to simultaneously estimate human motion states and model mismatch, and then systematically predict the position and velocity of the human for a finite horizon. Second, based on the predicted human states, a nonlinear model predictive control (MPC) technique is utilized for the robot motion planning. The simulation results have demonstrated the superior performance in prediction using the PIMM-UKF approach. The effectiveness of the MPC planner is also shown by successfully facilitating the socially desirable companion behavior.

Chaos Control of Atomic Force Microscope System Using Nonlinear Model Predictive Control

AbstractOwing to robust and optimal specification, model predictive control method has received wide attentions over recent years. Since in certain operational conditions, an Atomic/scanning Force Microscope (AFM) shows chaos behavior, the chaos feedback control of the AFM system is considered. According to the nonlinear model of forces interacting between the tip of micro cantilever and the substrate of AFM; the nonlinear control methods are proposed. In the paper, the chaos control of a micro cantilever AFM based on the nonlinear model predictive control (NMPC) technique is presented. Through software simulation results, the effectiveness of the designed NMPC of the AFM is assessed. The simulation results together with analytical stability proofs indicate that the proposed method is effective in keeping the system in a stable range.

Formation Control of Leader–Follower Mobile Robots’ Systems Using Model Predictive Control Based on Neural-Dynamic Optimization

In this paper, a neural-dynamic optimization-based nonlinear model predictive control (NMPC) is developed for controlling leader–follower mobile robots formation. Consider obstacles in the environments, a control strategy is proposed for the formations which includes separation-bearing-orientation scheme (SBOS) for regular leader–follower formation and separation-distance scheme (SDS) for obstacle avoidance. During the formation motion, the leader robot shall track a desired trajectory and the desire leader–follower relationship can be maintained through SBOS method; meanwhile, the followers can avoid the collision by applying the SDS. The formation-error kinematics of both SBOS and SDS are derived and a constrained quadratic programming (QP) can be obtained by transforming the MPC method. Then, over a finite-receding horizon, the QP problem can be solved by utilizing the primal-dual neural network (PDNN) with parallel capability. The computation complexity can be greatly reduced by the implemented neural-dynamic optimization. Compared with other existing formation control approaches, the developed solution in this paper is rooted in NMPC techniques with input constraints and the novel QP problem formulation. Finally, experimental studies of the proposed formation control approach have been performed on several mobile robots to verify the effectiveness.

Ecological Adaptive Cruise Controller for Plug-In Hybrid Electric Vehicles Using Nonlinear Model Predictive Control

Plug-in hybrid electric vehicles (PHEVs) are promising options for future transportation. Having two sources of energy enables them to offer better fuel economy and fewer emissions. Significant research has been done to take advantage of future route information to enhance vehicle performance. In this paper, an ecological adaptive cruise controller (Eco-ACC) is used to improve both fuel economy and safety of the Toyota Prius Plug-in Hybrid. Recently, an emerging trend in the research has been to improve the adaptive cruise controller. However, the majority of research to date has focused on driving safety, and only rare reports in the literature substantiate the applicability of such systems for PHEVs. Here, we demonstrate that using an Eco-ACC system can simultaneously improve total energy costs and vehicle safety. The developed controller is equipped with an onboard sensor that captures upcoming trip data to optimally adjust the speed of PHEVs. The nonlinear model predictive control technique (NMPC) is used to optimally control vehicle speed. To prepare the NMPC controller for real-time applications, a fast and efficient control-oriented model is developed. The authenticity of the model is validated using a high-fidelity Autonomie-based model. To evaluate the designed controller, the global optimum solution for cruise control problem is found using Pontryagin's minimum principle (PMP). To explore the efficacy of the controller, PID and linear MPC controllers are also applied to the same problem. Simulations are conducted for different driving scenarios such as driving over a hill and car following. These simulations demonstrate that NMPC improves the total energy cost up to 19%.

Fast Nonlinear Model Predictive Control on FPGA Using Particle Swarm Optimization

Nonlinear model predictive control (NMPC) requires a repeated online solution of a nonlinear optimal control problem. The computation load remains the main challenge for the real-time practical application of the NMPC technique, particularly for fast systems. This paper presents a fast NMPC algorithm implemented on a field-programmable gate array (FPGA) that employs a particle swarm optimization (PSO) algorithm to handle nonlinear optimization. The FPGA is used to explore the possibilities of parallel architecture for the substantial acceleration of NMPC. PSO is employed to achieve real-time operation due to its naturally parallel capabilities. The proposed FPGA-based NMPC-PSO controller consists of a random-number generator, a fixed-point arithmetic, a PSO solver, and a universal asynchronous receiver/transmitter communication interface. Then, this controller is applied to an engine idle speed control problem and demonstrated with an FPGA-in-the-loop testbench. The experimental results indicate that the NMPC-on-FPGA-chip strategy has good computational performance and achieves satisfactory control performance.

Robust control of a supermarket refrigeration system using multi-stage NMPC

Using Nonlinear Model Predictive Control (NMPC), real world systems that are subject to stringent constraints can be controlled efficiently when accurate plant models are employed. Model uncertainties however may lead to poor performance of the controller and to constraint violations because of the wrong predictions. As in real applications, there often is a significant plant model mismatch, the controller must be robust to plant-model mismatch without deteriorating the performance of the controller significantly. Multi-stage NMPC is a robust scheme which has been proven to be less conservative than open loop worst case solutions because of the presence of feedback information at the future time stages is explicitly accounted in the problem formulation. In this paper, we study the application of this NMPC technique to a supermarket refrigeration system under uncertainty and show that the presence of uncertainties in the model lead to constraint violations when standard NMPC scheme is applied. The robust multi-stage NMPC scheme improves the controller performance by accounting for the uncertainties in the prediction and results in a reliable operation of the system.

Distributed time-constrained guidance using nonlinear model predictive control

The paper presents a new time-constrained guidance approach for the multi-missile network by using the nonlinear model predictive control (MPC) technique. The objective is to coordinate the impact time of a group of interceptor missiles against the stationary target. The framework of a distributed MPC scheme is developed. Each missile is assigned its own finite-horizon optimal control problem (FHOCP) and only shares the information with its neighbors. The solutions of the local FHOCP are obtained by using the improved pigeon-inspired optimization method that serves as a convenient tool to deal with the equality and inequality constraints. Further, a safe distance-based penalty term is integrated into the local cost function to achieve no-fly zone avoidance for the multi-missile network. The numerical simulations show that the distributed MPC scheme is effective to implement the cooperative time-constrained guidance with satisfied accuracy of target capture. The Monte Carlo test also demonstrates the robustness of the proposed guidance approach in consideration of the no-fly zone constraint.

Stochastic Nonlinear Model Predictive Control of an Uncertain Batch Polymerization Reactor

This paper presents a stochastic nonlinear model predictive control technique for discrete-time uncertain nonlinear systems with particular focus on the batch polymerization reactor application. We consider a nonlinear dynamical system subject to chance constraints (i.e. need to be satisfied probabilistically up to a pre-assigned level). This formulation leads to a finite-horizon chance-constrained optimization problem at each sampling time, which is in general non-convex and hard to solve.We propose a heuristic methodology to handle uncertainty for highly nonlinear systems. In our framework, the uncertainty propagation is modelled via a Markov chain and a randomization technique, the so-called scenario approach, is employed yielding a tractable formulation. The efficiency and limitations of the proposed methodology is illustrated through its application to an uncertain batch polymerization reactor model and a comparison with deterministic nonlinear model predictive control is presented.

Optimal control of grinding mill circuit using model predictive static programming: A new nonlinear MPC paradigm

The recently developed reference-command tracking version of model predictive static programming (MPSP) is successfully applied to a single-stage closed grinding mill circuit. MPSP is an innovative optimal control technique that combines the philosophies of model predictive control (MPC) and approximate dynamic programming. The performance of the proposed MPSP control technique, which can be viewed as a ‘new paradigm’ under the nonlinear MPC philosophy, is compared to the performance of a standard nonlinear MPC technique applied to the same plant for the same conditions. Results show that the MPSP control technique is more than capable of tracking the desired set-point in the presence of model-plant mismatch, disturbances and measurement noise. The performance of MPSP and nonlinear MPC compare very well, with definite advantages offered by MPSP. The computational speed of MPSP is increased through a sequence of innovations such as the conversion of the dynamic optimization problem to a low-dimensional static optimization problem, the recursive computation of sensitivity matrices and using a closed form expression to update the control. To alleviate the burden on the optimization procedure in standard MPC, the control horizon is normally restricted. However, in the MPSP technique the control horizon is extended to the prediction horizon with a minor increase in the computational time. Furthermore, the MPSP technique generally takes only a couple of iterations to converge, even when input constraints are applied. Therefore, MPSP can be regarded as a potential candidate for online applications of the nonlinear MPC philosophy to real-world industrial process plants.

Trajectory Tracking Nonlinear Model Predictive Control for Autonomous Surface Craft

This paper presents a solution to the problem of trajectory tracking control for autonomous surface craft (ASC) in the presence of ocean currents. The proposed solution is rooted in nonlinear model predictive control (NMPC) techniques and addresses explicitly state and input constraints. Whereas state saturation constraints are added to the underlying optimization cost functional as penalties, input saturation constraints are made intrinsic to the nonlinear model used in the optimization problem, thus reducing the computational burden of the resulting NMPC algorithm. Simulation results, obtained with a nonlinear dynamic model of a prototype ASC, show that the NMPC strategy adopted yields good performance in the presence of constant currents. Experimental results are also provided to validate the real-time implementation of the NMPC techniques for ASCs.

Predictive optimal control for thermo-mechanical pulping processes with multi-stage low consistency refining

In this paper, we present a nonlinear model based dynamic optimization approach for optimal control for thermo-mechanical pulping (TMP) processes. The method is based on nonlinear model predictive control (NMPC) technique with an economic objective function. In our previous work [1], an economically oriented NMPC (econNMPC) was presented for a two-stage TMP process with high consistency (HC) refiners. The two-stage TMP process was simulated up to the secondary refiner. In this study, we extend the previous study to multiple stages that include low consistency (LC) refining. Here we can exploit the complexity of interactions of multiple stage TMP for both performance and optimal operations. The first process is a conventional refining process with two stages of HC refining with a primary and a secondary refiner, followed by a latency chest and a third-stage LC refiner. The second TMP process has only a HC primary refiner, and the secondary stage HC refiner is replaced by multiple stages of LC refiners after the latency chest. Both TMP schemes are dynamically optimized against disturbances to produce the same target final pulp qualities while minimizing specific energy consumption of the processes. The simulation results show the potential economic benefits of the proposed method, through a reduction in total specific energy, about 24%, for the second TMP process.

Climate Change and Technical Progress: Impact of Informational Constraints

In this paper we analyse a growth model that includes environmental and economic variables as well as technological progress under different informational constraints on the behavior of economic agents. To simulate the informationally constrained economy, we make use of the non-linear model predictive control technique. We compare models with exogenous and endogenous technical change as well as directed and undirected endogenous technical change under different informational structures. We show that endogenous technical change yields lower environmental damages than exogenous technical change with fully informed agents. At the same time, welfare may rise or decline depending on the efficiency of the technology in use. In the case of directed technical change, a green growth scenario generates a smaller temperature increase that, however, goes along with less output and lower welfare. This holds both for informationally constrained and unconstrained behaviour of agents. We find that the effects of informational constraints, with respect to the climate system, increase with the degree of endogeneity of technology in the model.