Balanced Objective Model Predictive Control for Distance-Keeping and Tracking of Manoeuvring Vessels

This study presents a novel Distributed Robust Adaptive Model Predictive Control (DRAMPC) for tracking in multi‐agent systems. The framework is designed to work with dynamically coupled subsystems and limited communication, which is restricted to local neighborhoods. The proposed approach explicitly accounts for parametric uncertainties and additive disturbances by employing a tube‐based formulation to bound the system response for any possible uncertainty realizations. To ensure recursive feasibility and asymptotic stability, contractivity properties for the terminal cross‐section are derived alongside a structured stabilizing gain for the closed‐loop dynamics. The conservativeness of the tube‐based formulation is relaxed by exploiting a distributed set membership via recursive identification of the parameter uncertainty set. The control problem is formulated by leveraging the Artificial Reference method for piecewise reference signals to ensure feasibility even when the desired reference is not directly reachable. The consensus ADMM algorithm is employed to solve the distributed optimization problem efficiently while maintaining scalability as the number of agents increases. Furthermore, the artificial reference formulation is extended to trajectory tracking, allowing the controller to track time‐varying references while preserving feasibility. The effectiveness of the proposed method is demonstrated through illustrative examples, highlighting its capability to achieve accurate and robust tracking in multi‐agent uncertain systems.

MPC for tracking with optimal closed-loop performance

In this paper, a simplified dynamic vehicle model is established to accurately describe the dynamics of Unmanned Ground Vehicle (UGV) in trajectory tracking, while meeting the real-time computing requirement. And a modified model predictive control (MPC) algorithm with soft constraint for UGV trajectory tracking is proposed to improve the tracking stability and rapidity. The optimal control problem at each sampling time is converted into a quadratic program (QP), which has mature solutions. To verify the trajectory tracking capabilities, the proposed MPC controller is compared with a PD controller under different longitudinal velocities. The simulation results demonstrate that the MPC controller can effectively reduce the tracking error and ensure the vehicle's traveling smoothness.

Nonlinear Model Predictive Control for trajectory tracking and collision avoidance of underactuated vessels with disturbances

This paper investigates the optimal model predictive control for the path tracking of an autonomous vehicle. For the control algorithm of an autonomous vehicle following pre-calculated path, two indices must be considered: 1. The performance index including the path tracking error and the energy consumed in control process. 2. The computational cost. In this paper, an optimal model predictive controller is established by searching dynamically optimal linear model parameters when the model mismatch happens. Compared with the general nonlinear model predictive controller or the approximate linear model predictive controller, it is more suitable for path tracking of the autonomous vehicle due to the obvious reduction of computational burden and the good path tracking performance.

This paper focuses on the challenges of low trajectory tracking accuracy in intelligent vehicles caused by different road adhesion coefficients and vehicle speed conditions, aiming to address these challenges with an improved Model Predictive Control (MPC) algorithm. Firstly, a model encompassing vehicle dynamics featuring two degrees of freedom has been formulated, along with a corresponding model to track trajectory errors. Secondly, a Model Predictive Control (MPC) algorithm is designed with trajectory tracking accuracy and control increment as the objective functions. Additionally, incorporating a Particle Swarm Optimization (PSO) algorithm with MPC allows for the dynamic computation of the optimal prediction horizon size. Afterward, a unified simulation model is assembled, employing both CarSim and MATLAB/Simulink, to conduct dual-lane trajectory tracking simulation analysis under diverse road adhesion and vehicle speed conditions. The efficacy of the proposed control algorithms is validated through this process.

A helicopter flight control system is a typical multi-input, multi-output system with strong channel-coupling and nonlinear characteristics. This paper presents an explicit model predictive control (EMPC) for attitude regulation and tracking of a 3-Degree-of-Freedom (3-DOF) helicopter. A state-space representation of the system is established according to the characteristics of each degree-of-freedom motion. Multi-Parametric Quadratic Programming (MPQP) and online computation processes for explicit model predictive control and controller design for a 3-DOF helicopter are discussed. The controller design for set-point regulation and tracking time-varying reference signals of a 3-DOF helicopter are presented respectively. Numerical study of explicit model predictive control for attitude regulation and tracking of a 3-DOF helicopter are conducted. A hardware-in-the-loop experimental study of explicit model predictive control of a 3-DOF helicopter is made. To analyze the performances of an EMPC controlled helicopter system, an Active Mass Disturbance System and manual interference are considered in comparison with PID scheme. Numerical simulation and HIL experimental studies show that explicit model predictive control is valid and has satisfactory performance for a 3-DOF helicopter.

Design and experimental validation of a robust model predictive control for the optimal trajectory tracking of a small-scale autonomous bulldozer

This paper studies a model predictive hybrid tracking control scheme under a multiple harmonics time‐varying disturbance observer for a discrete‐time dynamics nonholonomic autonomous mobile robot (AMR) with disturbance. To solve the robust tracking control problem of the AMR and unmanned aerial vehicle (UAV) air–ground cooperative, a hybrid tracking control strategy combined with improved model predictive control (MPC) method is presented. First, a time‐varying air‐ground cooperative tracking control model based on the nonholonomic constraints AMR and UAV is established by polar coordinate transformation. Second, to estimate disturbances of the time‐varying model, a multiple harmonics disturbance observer with time‐varying gains is designed. A hybrid tracking control scheme for the AMR based on the estimated states and MPC method with relaxing factor and kinematics constraints is proposed. Finally, experimental results show the effectiveness of the proposed control strategy.

In this paper, the problem of optimal time-varying attitude tracking control for rigid spacecraft with system constraints and unknown additive disturbances is considered. Through the design of a new non-linear tube-based robust model predictive control (TRMPC) algorithm, a dual-loop cascaded tracking control framework is established. The proposed TRMPC algorithm explicitly considers the effect of disturbances and applies tightened system constraints to predict the motion of the nominal system. The obtained optimal control action is then combined with a non-linear feedback law such that the actual system trajectories can always be steered within a tube region centred around the nominal solution. To facilitate the recursive feasibility of the optimization process and guarantee the input-to-state stability of the tracking control process, the terminal controller and the corresponding terminal invariant set are also constructed. The effectiveness of using the proposed dual-loop TRMPC control scheme to track reference attitude trajectories is validated by experimental studies. A number of comparative studies were carried out, and the obtained results reveal that the proposed design is able to achieve more promising constraint handling and attitude tracking performance than that of the other newly developed methods investigated in this research.

Climbing robots are characterized by a secure surface coupling that is designed to prevent falling. The robot coupling ability is assured by an adhesion method leading to nonlinear dynamic models with time-varying parameters that affect the robot’s mobility. Additionally, the wheel friction and the force of gravity force are also relevant issues that can compromise the climbing ability if they are not well modeled. This work presents a model-based torque controller for velocity tracking in a four-wheeled climbing robot specially designed to inspect storage tanks. The model-based controller (MPC) compensates for the effects of nonlinearities due to the forces of gravity, friction, and adhesion through the dynamic and kinematic modeling of the climbing robot. Dynamic modeling is based on the Lagrange-Euler approach, which allows a better understanding of how forces and torques affect the robot’s movement. Besides, an analysis of the interaction force between the robot and the contact surface is proposed, since this force affects the motion of the climbing robot according to spatial orientation. Finally, simulations are carried out to examine the robot’s dynamics during the climbing movement, and the MPC is validated through the redrobot simulator V-REP and practical experiments. The presented results highlight the compensation of the nonlinear effects due to the robot’s climbing motion by the proposed MPC controller.

Event-based adaptive horizon nonlinear model predictive control for trajectory tracking of marine surface vessel

In this paper, we present the dynamic modeling and model predictive tracking control for a fin-actuated robot with barycentre regulating mechanism in multiple motions. Specifically, a dynamic model for the robot is established firstly. Based on the dynamic model, a model predictive tracking control algorithm is proposed. And simulations of of tracking rectangle trajectory, sine-like trajectory, ascending trajectory, and spiral trajectory are conducted to validate the algorithm. The simulation results demonstrate that the proposed algorithm is able to implement trajectory tracking of the robot with small position error and orientation error. This paper contributes to trajectory tracking for an underwater robot with controllable barycentre in multiple motions, which has been rarely explored.

Based on a modified min-max optimization strategy, an improved design of model predictive tracking control (MPC) is proposed to guarantee the performance of industrial process control systems when the networked system suffers from communication faults. Packet losses and uncertainties exist in the networked control system, which may deteriorate the performance of the systems and even cause a safety accident. To handle with this problem, an extended state space model is utilized in the proposed MPC design for enhancing control performance firstly. Then an improved min-max optimization approach is adopted for obtaining the optimal solution of the corresponding MPC method, where two separate optimization steps are proposed to enhance the control system operation. Different from MPC strategies that employ traditional state space models, the state variables can be tuned additionally for the modified MPC design because the tracking error information and the state variables are united in the improved model. As a consequence, extra degree of freedom is acquired for the relevant MPC scheme, and then better control performance is expected. Simulations on the temperature regulation process under model/plant mismatches and packet losses are simulated to evaluate the validity of the proposed MPC method finally.

On the Stability of Nonlinear Model Predictive Control for 3D Target Tracking