Real-time model predictive line following control for underactuated marine vehicles

An important feature of linear model predictive control (MPC) is the ability to provide offset-free control through integral action. Linear MPC can utilize a steady-state target optimizer (SSTO) in conjunction with a dynamic optimization in order to manage systems that are non-square, have integrating modes, or encounter infeasible setpoints. Integral action does not ensure that the feasible steady-state target is closest to the true optimum when the desired setpoint is infeasible. This paper describes the modifications necessary to linear state-space MPC algorithms in order to address this problem (assuming systems with no integrating modes). The solution employs features of the integrated system optimization and parameter estimation (ISOPE) algorithm: the SSTO cost is modified by a term that results in matching of the true plant and model conditions necessary for optimality. This work combines well with prior work which has determined the situations where the modification is actually necessary.

This paper presents a novel function of energy-optimal adaptive cruise control (EACC) for electric vehicles based on model predictive control (MPC), which optimally plans the host car's speed trajectory for higher energy efficiency through taking the surrounding traffic information and the road conditions ahead into account. To cope with the nonlinearity of the system dynamics, nonlinear MPC (NMPC) is designed in this paper. Afterwards, the nonlinear equality constraints are relaxed so that the optimization problem has a convex feasible set. Moreover, it is proven that the nonlinear problem can be recast as a second-order cone programming (SOCP) problem. The performance of NMPC is evaluated in the real driving condition and compared to both linear MPC and receding horizon dynamic programming (RH-DP). In order to enable real-time control, the interior point method is used to solve both linear and nonlinear MPC problems and the computation time is investigated in this paper.

In this article, multi-input multi-output (MIMO) linear model predictive controller (LMPC) based on state space model and nonlinear model predictive controller based on neural network (NNMPC) are applied on a continuous stirred tank reactor (CSTR). The idea is to have a good control system that will be able to give optimal performance, reject high load disturbance, and track set point change. In order to study the performance of the two model predictive controllers, MIMO Proportional-Integral-Derivative controller (PID) strategy is used as benchmark. The LMPC, NNMPC, and PID strategies are used for controlling the residual concentration (CA) and reactor temperature (T). NNMPC control shows a superior performance over the LMPC and PID controllers by presenting a smaller overshoot and shorter settling time.

This paper presents a linear model predictive control scheme for a magnetic levitation system (Maglev). The Maglev dynamics involve nonlinearity, highly unstable, makes it more challenging to design a suitable control algorithm. The objective of this work is to control the position of a ferromagnetic ball in the air space of the nonlinear system. In this research, the proposed controller is designed based on the linear prediction model, which is obtained by linearizing the plant around a known operating point. The effectiveness of the proposed linear model predictive control algorithm is verified in the simulation environment. For comparative analysis, the performance of the proposed controller is compared with a 1-DOF PID control technique [1] and found better results.

In this paper a hierarchical model predictive control framework for the energy management of a hybrid electric vehicle is extended by replacing the supervisory linear model predictive controller with a nonlinear model predictive controller. The nonlinear system dynamics of a parallel hybrid electric vehicle are incorporated into a nonlinear optimal control problem which is solved at each time instance. The discretization of the continuous time system dynamics is realized via direct multiple shooting. The low-level linear model predictive controller considers the actuator dynamics for optimal tracking of the driver's torque demand while the high-level controller considers the slow dynamics of the vehicle and the battery. The proposed strategy is validated in simulation with the worldwide harmonized light vehicles test cycle and compared to a hierarchical model predictive control framework with linearized system dynamics.

An off-line Model Reduction-based Technique for On-line Linear MPC Applications for Nonlinear Large-Scale Distributed Systems

In this paper, the locomotion and steering control of a simulated Mini Cheetah quadruped robot was investigated in the presence of terrain characterised by low friction. Low-level locomotion and steering control were implemented via a central pattern generator approach, whereas high-level steering control manoeuvres were implemented by comparing a neural network and a linear model predictive controller in a dynamic simulation environment. A data-driven approach was adopted to identify the robot model using both a linear transfer function and a shallow artificial neural network. The results demonstrate that, whereas the linear approach showed good performance in high-friction terrain, in the presence of slippery conditions, the application of a neural network predictive controller improved trajectory accuracy and preserved robot safety with different steering manoeuvres. A comparative analysis was carried out using several performance indices.

This paper discusses the car-following algorithm and energy-efficiency torque allocation of distributed-drive electric vehicles under low-speed stop-and-go scenarios. In this scenario, the host vehicle needs to start and stop frequently. To improve the car-following performance of adaptive cruise control, a linear model predictive control is designed to meet the safety and comfort requirements of the host vehicle. In addition, combined with the characteristics of distributed drive, a genetic algorithm is used to solve the torque allocation objective function based on energy efficiency to minimize the energy consumption of the host vehicle. The genetic algorithm is solved offline to reduce the real-time computing burden of the onboard controller. Under the New York City Cycle, the co-simulation results of Carsim/Simulink show that the linear model predictive control can achieve better car-following performance, and the energy consumption of the host vehicle can be reduced by 1.7%.

The use of dynamical driving simulators is nowadays common practice in many different application fields, such as driver training, vehicle development, and medical studies. Platforms with different mechanical structure are 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 feelings, hence it is crucial that the motion control strategies generate both realistic and feasible signals to the platform, to assure that it is kept within its limited operation space. Such strategies are called Motion Cueing Algorithms (MCAs), and they are clearly tailored to the particular mechanical structure of the device. In this paper we describe an MCA based on non linear Model Predictive Control (NMPC) techniques for a simulator of new conception, that consists of an hexapod over a flat base moved by a tripod, thus exhibiting highly non linear behaviour. The procedure is based on previous works where a linear, MPC-based MCA has been applied to a simpler device. The algorithm has been evaluated on a simulation environment, and a first implementation on the real device is in progress. Preliminary results show that a full exploitation of the working area is achieved, while managing at best all the limitations given by the particular structure and preserving the ease of tune and intuitiveness of the linear approach.

Online optimal control of quadruped robots would enable them to adapt to varying inputs and changing conditions in real time. A common way of achieving this is linear model predictive control (LMPC), where a quadratic programming (QP) problem is formulated over a finite horizon with a quadratic cost and linear constraints obtained by linearizing the equations of motion and solved on the fly. However, the model linearization may lead to model inaccuracies. In this paper, we use the Koopman operator to create a linear model of the quadrupedal system in high dimensional space which preserves the nonlinearity of the equations of motion. Then using LMPC, we demonstrate high fidelity tracking and disturbance rejection on a quadrupedal robot. This is the first work that uses the Koopman operator theory for LMPC of quadrupedal locomotion.

When the linear Model Predictive Control (MPC) algorithm is used for trajectory tracking control of autonomous vehicles in high-speed turning conditions, the tracking accuracy is usually decreased due to the low fidelity of the predictive model. To deal with this problem, a trajectory tracking controller based on Neural Network Predictive Control (NNPC) is designed in this paper. The data collected from driving simulation experiments is used for the Back Propagation Neural Network (BPNN) training process to obtain the vehicle state predictive models. To reduce the iteration times of the rolling optimization module of NNPC, Particle Swarm Optimization (PSO) with Fitness Allocating Inertia Weights (PSO-FAIW) algorithm is proposed, which can allocate inertia weights in light of the distance between each particle’s fitness and the best fitness of the swarm. Finally, the performance of the controller designed in this paper is verified based on MATLAB /Simulink. The simulation results demonstrate that the controller proposed in this paper can give consideration to tracking accuracy and real-time performance.

Bridging the gap between the linear and nonlinear predictive control: Adaptations for efficient building climate control

The applicability of linear model predictive control to the 2-butene metathesis process is studied. Similarly to industrial practice, the model predictive controller is configured on a supervisory level, providing set points to basic process controllers. The development of the process model is based on open-loop identification from input–output data extracted from dynamic simulation performed in Aspen Plus Dynamics. The model predictive controller, designed using MATLAB tools, supervises a system consisting of two inputs (feed rate and reaction temperature) and two outputs (ethylene and propylene production rates). The performance of the model-based control strategy is assessed by Aspen Plus Dynamics-Simulink co-simulation and compared to regulatory control through several indexes (mean square error, integral square error, peak error, and integral absolute error). The model predictive controller outperforms the feedback controller. Considerations regarding the workflow for the implementation of model predictive control in an industrial environment are provided.

This paper investigates the usage of Discrete-time Linear Model Predictive Control in controlling a nonlinear Coupled Tanks System. Two different schemes of Model Predictive control are employed. To begin with, a basic Model Predictive Control based on Generalized Predictive Control is used and then a Model Predictive Control approach based on Laguerre functions. Simulation results have been included which demonstrate the performance of both controllers when used to control Single-Input Single-Output Coupled Tanks System and the performance when Laguerre based Model Predictive Control is applied to Multi-Input Multi-Output Coupled Tanks System.

Trajectory tracking on a low-speed vehicle using the model predictive control (MPC) algorithm usually assumes a simple road terrain. This assumption does not correspond to the actual road situation, leading to low tracking accuracy. Therefore, a trajectory tracking method considering road curvature based on MPC is proposed in this paper. In this method, the controller can automatically switch between MPC types. Linear model predictive control (LMPC) is selected for small road curvatures, while nonlinear model predictive control (NMPC) is employed for large road curvatures. In addition, the NMPC algorithm in this work considers the effect of road curvature on tracking accuracy, making it suitable for tracking time-varying curvature roads. To verify the feasibility of the algorithm, simulation comparisons with the basic MPC model were carried out at different testing roads and vehicle longitudinal speeds. The results indicate that the method significantly improves trajectory tracking accuracy, all while ensuring real-time calculations. The intelligent switching capability of control models based on road curvature allows its application to track trajectories on arbitrarily complex roads.