A Physically Regularized Control-Oriented State Model and Nonlinear Model Predictive Control Framework for an Ice Rink Refrigeration System

In this article, we devise a nonlinear model predictive control framework for the energy management of nonhybrid hydrostatic drive transmissions. The controller determines the optimal control commands of the actuators by minimising a cost function over a receding horizon. With our approach, the velocity-tracking error is minimised while keeping the fuel economy of the system high. The hydrostatic drive transmission system studied in this article is a typical commercial work machine, that is, there is no energy storage or alternative power source in the system (a nonhybrid hydrostatic drive transmission). We evaluate success with a validated simulation model of the hydrostatic drive transmission of a municipal tractor. In our experiments, a detailed system model is used both in the system simulation and in the prediction phase of the nonlinear model predictive control. The use of a detailed model in the nonlinear model predictive control framework places our design as a benchmark for controlling nonhybrid hydrostatic drive transmissions, when compared to solutions using simplified models or computationally less intensive control methods as in earlier work by the authors. Our nonlinear model predictive control approach enables numerically robust optimisation convergence with the utilised complex nonlinear model. Above all, this is accomplished with stabilising terminal constraints and distinctive terminal cost, both based on an optimal steady-state solution. In addition, a simple method to generate initial guesses for optimisation is introduced. When compared with the performance of a controller based on quasi-static models, our results show notable improvement in velocity tracking while maintaining high fuel economy. Furthermore, our experiments demonstrate that framing energy management as a nonlinear model predictive control provides a flexible and rigorous framework for fast velocity tracking and high energy efficiency. We also compare the results with those of an industrial baseline controller.

This paper presents an online Nonlinear Model Predictive Control (NMPC) framework for trajectory tracking of autonomous vehicles. The operating environment is assumed to be unknown with various different types of obstacles. A bicycle model is used for the prediction of the future states in the NMPC framework, and a fully nonlinear CarSim vehicle model is used for the simulations. Real-time analysis is presented for a particular situation and the effect of warm initialization of optimization process on the computation time is elaborated. Simulation results show that the NMPC controller provides satisfactory online tracking performance while satisfying the real-time constraints, and warm initialization reduces the optimizer computational load significantly.

In this article, we present a quasi-infinite horizon nonlinear model predictive control (MPC) scheme for tracking of generic reference trajectories. This scheme is applicable to nonlinear systems, which are locally incrementally stabilizable. For such systems, we provide a reference generic offline procedure to compute an incrementally stabilizing feedback with a continuously parameterized quadratic quasi-infinite horizon terminal cost. As a result, we get a nonlinear reference tracking MPC scheme with a valid terminal cost for general reachable reference trajectories without increasing the online computational complexity. The practicality of this approach is demonstrated with a benchmark example.

Nonlinear Model Predictive Control of Food Sterilization Processes

This paper proposes a vision-based obstacle avoidance strategy in a dynamic environment for a fixed-wing unmanned aerial vehicle (UAV). In order to apply a nonlinear model predictive control (NMPC) framework to image-based visual servoing (IBVS), a dynamic model from UAV control input to image features is derived. From this dynamics, a visual information-based obstacle avoidance strategy in an unknown environment is proposed. When a vision system is employed on a UAV, it is easy to lose visibility of the target in the image plane due to its maneuvering. To address this issue, a visibility constraint is considered in the NMPC framework. The advantage of the proposed method is that the constraints (e.g., visibility maintaining, actuator saturation) can be modeled and solved in a unified framework. Numerical simulations on a UAV model show satisfactory results in reference tracking and obstacle avoidance maneuvers with the constraints.

We propose a nonlinear model predictive control (NMPC) framework based on a direct optimal control method that ensures continuous-time constraint satisfaction and accurate evaluation of the running cost, without compromising computational efficiency. We leverage the recently proposed successive convexification framework for trajectory optimization, where: (1) the path constraints and running cost are equivalently reformulated by augmenting the system dynamics, (2) multiple shooting is used for exact discretization, and (3) a convergence-guaranteed sequential convex programming (SCP) algorithm, the prox-linear method, is used to solve the discretized receding-horizon optimal control problems. The resulting NMPC framework is computationally efficient, owing to its support for warm-starting and premature termination of SCP, and its reliance on first-order information only. We demonstrate the effectiveness of the proposed NMPC framework by means of a numerical example with reference-tracking and obstacle avoidance. The implementation is available at https://github.com/UW-ACL/nmpc-ctcs.

In this paper a real-time capable hierarchical nonlinear model predictive control framework for the energy management of a hybrid electric vehicle is presented. As high-level energy management, nonlinear model predictive control is employed. Therefore, a nonlinear optimal control problem is formulated using a control-oriented internal model derived from high-fidelity models and experimental data. The multiple shooting algorithm and an Euler backward scheme are used to discretize the optimal control problem. The resulting nonlinear problem is solved in real-time using Sequential Quadratic Programming. A rule-based gear choice and engine on / off strategy is added. Actuator dynamics and drivability are addressed in a fast low-level linear time-variant model predictive controller. The result is analyzed and compared to the optimal solution obtained by dynamic programming using a simplified model of the vehicle.

Nonlinear model predictive control of a pH neutralization process based on Wiener–Laguerre model

Nonlinear model predictive control for maximum wind energy extraction of semi-submersible floating offshore wind turbine based on simplified dynamics model

Optimization of battery charging strategy based on nonlinear model predictive control

Space target surveillance plays an important role in maintaining space advantages. Trajectory planning and attitude control are the key technologies of space target surveillance. We propose a nonlinear model predictive control framework to plan the trajectory of the surveillance satellite, track the surveillance target and keep a certain distance. In particular, the orbit dynamics and environmental disturbance of surveillance satellite are taken into account. A MatLab based real-time nonlinear model predictive control toolbox is used to solve these problems. The toolbox uses an efficient numerical method to calculate the maneuvering solution of MPC, which means that it allows the prediction horizon length to be greatly extended and save calculation time. The developed controller is simulated in a simulation scene, including the interference of near ground environment. We show that the proposed MPC method can be successfully applied to the satellite orbit planning and attitude control and the MATMPC toolbox can greatly speed up the solution of MPC optimization functions.

Development of a Nonlinear Model Predictive Control Framework for a PEM Fuel Cell System

The article studies the trajectory tracking control problem of underactuated marine vehicles via the nonlinear model predictive control (NMPC) strategy, where practical control and state constraints present. It is a well-known challenging issue that the conventional NMPC is not applicable for underactuated marine vehicles, due to the fact that there does not exist a local static continuous state-feedback controller to stabilize the underactuated dynamics. To resolve this issue, this article proposes to construct an auxiliary time-varying tracking controller to aid terminal constraint design in the NMPC framework, where the time-varying tracking controller borrows the ideas from Lyapunov's direct method and backstepping approach. Based on this, a novel NMPC algorithm is designed to ensure trajectory tracking control of underactuated marine vehicles. Furthermore, a systematic parameter design approach is developed. Under the designed parameters, we show that the tracking error system is input-to-state stable (ISS). Finally, the effectiveness of the designed algorithm is verified by thorough simulation and hardware experiments.

In this paper, a framework for nonlinear model predictive control (NMPC) for heavily noise-affected systems is presented. Within this framework, the noise influence, which originates from uncertainties during model identification or measurement, is explicitly considered. This leads to a significant increase in the control quality. One part of the proposed framework is the efficient state prediction, which is necessary for NMPC. It is based on transition density approximation by hybrid transition densities, which allows efficient closed-form state prediction of time-variant nonlinear systems with continuous state spaces in discrete time. Another part of the framework is a versatile value function representation using Gaussian mixtures, Dirac mixtures, and even a combination of both. Based on these methods, an efficient closed-form algorithm for calculating an approximate value function of the NMPC optimal control problem employing dynamic programming is presented. Thus, also very long optimization horizons can be used and furthermore it is possible to calculate the value function off-line, which reduces the on-line computational burden significantly. The capabilities of the framework and especially the benefits that can be gained by incorporating the noise in the controller are illustrated by the example of a miniature walking robot following a given path.

This paper proposes a method to solve nonlinear finite-horizon optimal control problems of discrete-time polynomial systems with polynomial terminal constraints. Algebraic equations with all variables at each time step, which are independent of variables at other time steps, are derived from the necessary conditions for optimality by eliminating variables recursively. The candidates of the optimal solution are obtained by solving these equations, and algorithms to find all of these candidates are also proposed. Because of its structure, the proposed method is suitable for nonlinear model predictive control that needs only the initial optimal control law. A simple example to illustrate the methodology and a practical example with the nonlinear model predictive control framework are provided.