Algorithm Development for Avoiding Both Moving and Stationary Obstacles in an Unstructured High-Speed Autonomous Vehicular Application Using a Nonlinear Model Predictive Controller

In a steam power plant, electricity is generated by a turbine-generator system driven by high-pressure steam produced by a drum-boiler. Changes in power output cause variations in the heating rate and water level, which, if not properly controlled, can lead to system shutdown. Water level control in such systems is still commonly performed manually or with PID controllers, which are less suitable due to the system’s nonlinear, MIMO characteristics and multiple operating points. Nonlinear Model Predictive Control (NMPC) has recently been adopted as it can handle these characteristics, but gradient-based optimization methods used in NMPC are prone to getting stuck in local optima. Genetic algorithms, as a gradient-free optimization method, offer an alternative by using a population-based approach, making them more capable of escaping local optima. This study aims to: (1) develop a Nonlinear Auto Regressive with Exogeneous Input (NARX) model representing the nonlinear drum-boiler system, and (2) design an NMPC controller based on genetic algorithms and compare its performance with NMPC using the interior-point method and PID gain scheduling in drum-boiler water level control system. The research involves collecting drum-boiler system data from industry, modeling with NARX, designing the genetic algorithm-based NMPC controller, tuning its parameters, and evaluating its performance. The challenge for the controller is to maintain water level despite varying power demands. The resulting NARX model achieved an RMSE of 5.2105 for training data and 3.6446 for testing data. The genetic algorithm-based NMPC maintained the water level effectively with an RMSE of 5.1944 and total control signal energy of 454590 (Ton/H)<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>. The interior-point NMPC yielded an RMSE of 13.0492 and energy of 264990 (Ton/H)<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, while the PID gain scheduling controller had an RMSE of 13.5215 and energy of 1016600 (Ton/H)<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>. This result demonstrates that the NMPC with genetic algorithm provides the best performance in maintaining water level stability with efficient control effort.

Economic Multi-stage Output Feedback NMPC using the Unscented Kalman Filter

This paper presents a practical demonstration of Nonlinear Model Predictive Control (NMPC) applied to the variable valve timing system of a 1 liter gasoline direct injection turbocharged engine over a transient maneuver. The nonlinear dynamic model utilized in the NMPC controller is a Takagi-Sugeno Neuro-Fuzzy model. The control problem has been formulated as a two-input-two-output multivariable control system with intake valve phase and exhaust valve phase angles as the two manipulated variables and engine fuel consumption and engine out NOx emissions as the two controlled variables. Two implementation methods were evaluated on the engine. In the first method, the NMPC using a sequential quadratic programming algorithm executed on a PC which was connected to the engine control unit over a controller area network. In the second method, the NMPC using a quasi-newton based optimization algorithm was deployed to and executed on the engine control unit. Results show the potential for >10% improvement in NOx emissions compared to the original engine strategy.

Nonlinear Model Predictive Control (NMPC) is a strong candidate for the control of large Multi-Mega Watt Wind Turbine Generators (WTG), especially when reliable Light Detection And Ranging (LIDAR) systems are available. Recently, a real-time NMPC for WTG control has been proposed, but had a limited reliability if deployed over the full WTG operating range due to the difficulty of handling the WTG aerodynamic coefficients over a large domain and of using a Gauss-Newton Hessian. In this paper, we address these two issues. A fast surface B-spline code has been developed specifically to tackle the interpolation of the aerodynamic coefficients for NMPC-based WTG control. A novel Hessian approximation for the NMPC problem is used, for which an efficient and inexpensive regularization is proposed. The approach presented is real-time feasible, and accurate over the full operating range of the WTG.

Aerial manipulation increases significantly the workspace size of robotic manipulators. However, aerial manipulation suffers from a lack of autonomy due to limited embedded energy. The Aerial Manipulator with Elastic Suspension (AMES) is designed to cope with this issue. It is an omnidirectional aerial vehicle equipped with a gripper and suspended under a robotic carrier by a spring for gravity compensation. In this paper, the AMES is controlled with a nonlinear model predictive controller (NMPC). To eliminate the steady-state errors, an observer based on a model of the AMES augmented with constant disturbances is implemented in conjunction with the NMPC controller. Experiments illustrate the efficiency of the NMPC by comparing it to a computed torque controller.

Event triggered nonlinear model predictive control for a wastewater treatment plant

The ability of automated vehicles to avoid obstacles on the road is one of the most critical functionalities, as it directly impacts passenger safety. The trajectory planning functionality plays a key role in this task, as it uses data from sensors to identify obstacles and plan a free obstacle path. Thus, this paper proposes a trajectory planning method based on a model predictive control (MPC) strategy that takes into account both static and dynamic obstacles. Additionally, a trajectory tracking controller based on a linear quadratic regulator (LQR) algorithm is designed to ensure the vehicle follows the planned path. To evaluate the proposed control solution for the lateral dynamics of the vehicle, a traffic scenario involving multiple obstacles (both static and dynamic) is used. The results show that the proposed solution effectively controls the vehicle's lateral dynamics and successfully avoids collisions with obstacles.

Actuator fault tolerance evaluation approach of nonlinear model predictive control systems using viability theory

This paper presents a nonlinear model predictive control (NMPC) framework for real-time formation control of autonomous ground vehicles (AGVs) operating under dynamic geometric patterns. The proposed method integrates a nonlinear kinematic bicycle model with a time-varying linearization strategy and constrained quadratic optimization to compute control inputs for each follower agent. Formation references are generated online using geometric transformation functions, enabling flexible spatial configurations such as line, rectangular, half-circle, and V-shaped formations. An exponential convergence model ensures smooth trajectory tracking, while input constraints are enforced at each control step. The controller is decentralized and scalable, with each agent solving its own NMPC problem using leader pose information. Extensive simulations validate the approach across multiple formations, demonstrating accurate tracking, constraint satisfaction, and real-time feasibility. The results confirm that the proposed NMPC architecture provides a unified and modular solution for multi-AGV formation control under nonlinear dynamics.

The field of autonomous driving vehicles is growing and expanding rapidly. However, the control systems for autonomous driving vehicles still pose challenges, since vehicle speed and steering angle are always subject to strict constraints in vehicle dynamics. The optimal control action for vehicle speed and steering angular velocity can be obtained from the online objective function, subject to the dynamic constraints of the vehicle’s physical limitations, the environmental conditions, and the surrounding obstacles. This paper presents the design of a nonlinear model predictive controller subject to hard and softened constraints. Nonlinear model predictive control subject to softened constraints provides a higher probability of the controller finding the optimal control actions and maintaining system stability. Different parameters of the nonlinear model predictive controller are simulated and analyzed. Results show that nonlinear model predictive control with softened constraints can considerably improve the ability of autonomous driving vehicles to track exactly on different trajectories.

Highly automated or autonomous vehicles will be dependent on systems that have to perceive the environment to make valid decisions during the driving cycle. One of the key enablers for autonomous and highly automated vehicles will be Light Detection And Ranging (LiDAR) technology. A Micro-Electro-Mechanical System (MEMS) based Micro-Scanning LiDAR is able to detect obstacles in a predefined Field-of-View (FoV). The point cloud stability of the scanned FoV is mandatory to be able to make a valid point where the obstacle is located in the scenery. Due to the fact that massive shocks can occur arbitrarily to the system, it is necessary to be able to detect and correct them as fast as possible that point cloud stability can be recovered as fast as possible. In this paper, we introduce a novel system architecture that enables a fast shock detection and correction of phase and frequency for MEMS-based Micro-Scanning LiDAR Systems. Our novel introduced fail-operational detection and correction system architecture was implemented in a 1D MEMS-based Micro-Scanning LiDAR FPGA platform to prove its feasibility and for performance evaluation.

The online computational load associated with Nonlinear Model Predictive Control (NMPC) is a serious barrier for its application to systems with fast dynamics. This paper addresses numerical approaches for fast and real-time feasible NMPC. The applicability of these methods to fast systems with sampling times in order of milliseconds is investigated through a nonlinear teleoperation system. The main ideas of the “real-time iteration” (RTI) approach for real-time optimization are addressed. Condensing approach to efficient structure exploitation of the described RTI scheme is then presented. The described method ensures a considerable reduction of the computational effort as required in real-time implementations. The NMPC algorithms based on these methods can therefore be carried out at higher sampling rates. The results show promising performance of the nonlinear teleoperation system using fast NMPC controller.

This paper presents an approach to collision-free, long-range trajectory generation for a mobile robot in an industrial environment with static and dynamic obstacles. For the long-range planning a visibility graph together with A* is used to find a collision-free path with respect to the static obstacles. This path is used as a reference path to the trajectory planning algorithm that in addition handles dynamic obstacles while complying with the robot dynamics and constraints. A Nonlinear Model Predictive Control (NMPC) solver generates a collision-free trajectory by staying close to the initial path but at the same time obeying all constraints. The NMPC problem is solved efficiently by leveraging the new numerical optimization method Proximal Averaged Newton for Optimal Control (PANOC). The algorithm was evaluated by simulation in various environments and successfully generated feasible trajectories spanning hundreds of meters in a tractable time frame.

The automotive industry is more concerned about vehicle and road safety. Nowadays the Govt. has introduced new safety regulations to improve the vehicle and road safety. Most of the accidents are happening because of human errors, violation of traffic rules, etc. To avoid or reduce these accidents and to meet the Govt. safety regulations the automotive industries started implementing new technologies like Advanced Driver Assistance System (ADAS), Passive and Active safety system in vehicles. ADAS is the most important safety feature in modern automobiles. The key functionality of ADAS is to provide more comfort to the driver and to improve road and vehicle safety by minimizing human errors. To achieve these functionalities the system use sensors like Radar (Radio Detection and Ranging), Camera, Light Detection and Ranging (LIDAR), and Ultrasonic sensors. Based on sensor input Electronic Control Unit (ECU) delivers the information to driver in terms of warnings, alarms, etc. or system takes the longitudinal and lateral control to bring the vehicle to safer zone. Focus of this paper is to discuss the hardware and software technologies used for the development of ADAS systems and its challenges.

Steering control for autonomous vehicles on slippery road conditions, such as on snow or ice, results in a highly nonlinear and therefore challenging online control problem, for which nonlinear model predictive control (NMPC) schemes have shown to be a promising approach. NMPC allows to deal with the nonlinear vehicle dynamics as well as the system limitations and geometric constraints in a rather natural way, given a desired trajectory that can be provided by a supervisory algorithm for path planning. Our aim is to study the real-time feasibility of NMPC-based steering control on an embedded computer and the importance of the appropriate vehicle model selection, the optimization solver choice and control horizon length. The presented computation times have been obtained on a Raspberry Pi 2 model, as a proof of concept for a future real-world implementation on an embedded microprocessor.