Optimizing Semi-Closed Greenhouse Temperature, Humidity, CO2Concentration, and Light Intensity Using a Nonlinear Model Predictive Control Approach

Even though mobile robots have been around for decades, trajectory optimization and continuous time collision avoidance remain subject of active research. Existing methods trade off between path quality, computational complexity, and kinodynamic feasibility. This work approaches the problem using a nonlinear model predictive control (NMPC) framework, that is based on a novel convex inner approximation of the collision avoidance constraint. The proposed Convex Inner ApprOximation (CIAO) method finds kinodynamically feasible and continuous time collision free trajectories, in few iterations, typically one. For a feasible initialization, the approach is guaranteed to find a feasible solution, i.e. it preserves feasibility. Our experimental evaluation shows that CIAO outperforms state of the art baselines in terms of planning efficiency and path quality. Experiments show that it also efficiently scales to high-dimensional systems. Furthermore real-world experiments demonstrate its capability of unifying trajectory optimization and tracking for safe motion planning in dynamic environments.

Real-time random reference tracking nonlinear model predictive control: a case study on wind turbines.

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 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 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.

Optimization-based strategies for the operation of low-density polyethylene tubular reactors: nonlinear model predictive control

SUMMARYA multistep nonlinear model predictive control (MPC) framework is developed to achieve steady‐state offset‐free control in the presence of plant–model mismatch. Our formulation explicitly accounts for the effect of plant–model mismatch by involving the output feedback error, which is expressed as the difference between the measured process output and the predicted model output at the previous sampling instance, in the multistep model recursive prediction. The proposed scheme is capable of improving the performance of nonlinear MPC, because the plant–model mismatch is effectively compensated through the recursive prediction propagation. We prove that this formulation is able to remove the steady‐state error to achieve offset‐free control. The proposed nonlinear MPC framework is applied to a highly nonlinear two‐input two‐output continuous stirred tank reactor, in comparison with other MPC implementations. The results obtained demonstrate that the proposed technique outperforms some existing popular MPC schemes and can realise offset‐free control even under significant plant–model mismatch and unmeasured disturbances. Copyright © 2012 John Wiley & Sons, Ltd.

The integration and control of energy systems for power generation consists of multiple heterogeneous subsystems, such as chemical, electrochemical, and thermal, and contains challenges that arise from the multi-way interactions due to complex dynamic responses among the involved subsystems. The main motivation of this work is to design the control system for an autonomous automated and sustainable system that meets a certain power demand profile. A systematic methodology for the integration and control of a hybrid system that converts liquefied petroleum gas (LPG) to hydrogen, which is subsequently used to generate electrical power in a high-temperature fuel cell that charges a Li-Ion battery unit, is presented. An advanced nonlinear model predictive control (NMPC) framework is implemented to achieve this goal. The operational objective is the satisfaction of power demand while maintaining operation within a safe region and ensuring thermal and chemical balance. The proposed NMPC framework based on experimentally validated models is evaluated through simulation for realistic operation scenarios that involve static and dynamic variations of the power load.

This paper presents a constrained nonlinear model predictive control (NMPC) framework for legged locomotion. The framework assumes a legged robot as a floating base single rigid body with contact forces being applied to the body as external forces. With consideration of orientation dynamics evolving on the rotation manifold SO(3), analytic Jacobians which are necessary for constructing the gradient and the Gauss-Newton Hessian approximation of the objective function are derived. This procedure also includes the reparameterization of the robot orientation on SO(3) to orientation error in the tangent space of that manifold. Obtained gradient and Gauss-Newton Hessian approximation are utilized to solve nonlinear least squares problems formulated from NMPC in a computationally efficient manner. The proposed algorithm is verified on various types of legged robots and gaits in a simulation environment.

This work presents the design and implementation of a data-driven Nonlinear Model Predictive Control (NMPC) framework for an Unmanned Aerial Vehicle (UAV) equipped with a 3-DOF robotic arm. Real-world datawas collected using the Matrice 100 platform and Dynamixel MX-28AR actuators to identify a high-dimensional linear model via Dynamic Mode Decomposition with Control (DMDc), capturing the interactions between the aerial vehicle and the manipulator across 21 state variables. This DMDc-based model is embedded within the NMPC formulation to predict system behavior over finite horizons. The UAV’s orientation is represented using quaternions, enabling continuous and singularity-free attitude control. Additionally, the redundancy of the UAV-manipulator system allows for the integration of secondary objectives into the cost function, supporting flexible task execution. To meet real-time requirements, the control problem is solved using the ACADOS solver. The resulting controller achieves high-precision tracking while managing internal constraints, demonstrating the potential of data-driven NMPC in aerial manipulation tasks.

An autonomous vessel can improve the intelligence, efficiency and economy of shipping logistics. To realize autonomous navigation control of underactuated vessels (USVs), this paper presents a controller that can make USV track a reference trajectory with only 2 inputs, i.e., surge and yaw. A nonlinear state-space model with 2 inputs considering environmental disturbances induced by wind, current and waves for a 3 degree of freedom (DOF) surface vessel is considered. Based on this model, a trajectory tracking controller using Nonlinear Model Predictive Control (NMPC) is designed. System constraints on inputs, input increment and output are incorporated in the NMPC framework. Simulation results show that the controller can track an ellipse trajectory well and that the tracking errors are within acceptable ranges, while system constraints are satisfied.

Dynamic modelling and nonlinear model predictive control of a Fluid Catalytic Cracking Unit

Nonlinear Model Predictive Control approach in design of Adaptive Cruise Control with automated switching to cruise control

Control of machine learning models has emerged as an important paradigm for a broad range of robotics applications. In this paper, we present a sampling-based nonlinear model predictive control (NMPC) approach for control of neural network dynamics. We show its design in two parts: 1) formulating conventional optimization-based NMPC as a Bayesian state estimation problem, and 2) using particle filtering/smoothing to achieve the estimation. Through a principled sampling-based implementation, this approach can potentially make effective searches in the control action space for optimal control and also facilitate computation toward overcoming the challenges caused by neural network dynamics. We apply the proposed NMPC approach to motion planning for autonomous vehicles. The specific problem considers nonlinear unknown vehicle dynamics modeled as neural networks as well as dynamic on-road driving scenarios. The approach shows significant effectiveness in successful motion planning in case studies.

This paper proposes an optimal positioning and trajectory planning algorithm for unmanned aerial vehicles (UAVs) to improve a communication quality of a team of ground mobile nodes (vehicles) in a complex urban environment. In particular, a nonlinear model predictive control (NMPC)-based approach is proposed to find an efficient trajectory for UAVs with a discrete genetic algorithm while considering the dynamic constraints of fixed-wing UAVs. The advantages of using the proposed NMPC approach and the communication performance metrics are investigated through a number of scenarios with different horizon steps in the NMPC framework, the number of UAVs used, heading rates and speeds.