Adaptive linear MPC for a PMSM-driven autonomous EV with a filtered third-order generalized integrator observer.

This paper presents an adaptive Nonlinear Model Predictive Control (NMPC) for the path tracking control of a fixed-wing unmanned aircraft. The objective is to minimize the mean and maximum error between the reference trajectory and the UAV. Navigating in a cluttered environment requires accurate tracking. However linear controllers cannot provide good tracking performance due to nonlinearities that arise in the system dynamics and physical limitations such as actuator saturation and state constraints. NMPC provides an alternative since it can combine multiple objectives and constraints which minimize the objective function. However, computational complexity is a major barrier to the real time implementation of the NMPC. An indirect approach which uses gradient descent methods can speed up the optimization but it is dicult to specify a proper termination condition of the optimization. If a decreasing cost metric is used, it can cause control input oscillations. We propose a new optimization termination metric which can remove the control input oscillations. This can be achieved by adding the actuator slew limit to the optimization termination requirement in addition to the cost monotonocity. In addition, we propose an adaptive NMPC which varies the control horizon according to the path curvature profile for tight tracking. Simulation results show that the proposed optimization algorithm can remove control input oscillations and track the trajectory more accurately than the conventional fixed horizon NMPC.

Conventional nonlinear model predictive control (NMPC) relies on an accurate process model. However, real-world systems’ models are often imperfect due to parametric variations, modelling errors and additive noise, leading to degraded control performance. This study investigates the effectiveness of two adaptive NMPC methods in solving this problem, namely, linear parameter varying (LPV) model predictive control (LPV-MPC) and model predictive control based on successive linearization (SL-MPC). In addition, an innovative approach for identifying LPV models is proposed and applied to three simulation examples. The identified LPV models gave a very strong fit. Also, simulation results demonstrate that both adaptive predictive NMPC (LPV-MPC and SL-MPC) exhibit performance similar to conventional NMPC and superior to Linear MPC. Notably, the two adaptive predictive controllers offer significantly reduced computational time compared to conventional NMPC.

A graph search and neural network approach to adaptive nonlinear model predictive control

Performance and robustness evaluation of Nonlinear Autoregressive with Exogenous input Model Predictive Control in controlling industrial fermentation process

Solid-state transformer (SST) has gained increasing attention in the electrical grid. One of the SST architectures is a three stages SST where quadrable active bridge (QAB) can be used in the isolation stage. This work investigates power and voltage regulation of the QAB module-based SST design under unbalanced loading conditions. The proposed control system applied in the QAB implements an adaptive linear quadratic regulator (ALQR) and nonlinear model predictive control (NMPC) to achieve the regulation. The dynamic performance of the QAB based SST is proven analytically and supported by simulation results.

This paper presents an adaptive nonlinear model predictive control method with zero steady-state error (called offset free) in the presence of the plant-model mismatch and external disturbances. A neural network model is trained online to predict the process output recursively over the prediction horizon. The output of the neural network is modified by the current output prediction error to achieve offset-free model predictive control method. The stability of the closed-loop system is shown using the Lyapunov direct method. Simulation results on a pneumatic servo system show effectiveness of the control strategy as compared with the recently reported methods in literature under plant-model mismatches and unmeasured disturbances.

This book provides a comprehensive study of nonlinear adaptive robust model predictive control (MPC). Chapters 2–5 present a framework for the analysis and synthesis of nonlinear robust MPC. This framework includes the treatment of robustness, computation methods, and performance improvement. Chapters 6–7 show how to develop the basic ideas for the design and analysis of the nonlinear adaptive robust MPC. One of the key techniques is the set-based approach, in which the internal model identifier allows the MPC to compensate for future changes in the parameter estimates and uncertainty associated with the unknown model parameters. Chapters 8–12 illustrate how to implement the synthesis approaches for nonlinear adaptive robust MPC, and a robust adaptive economic MPC is also proposed. This text also gives a finite-time identification method, which can be used to estimate the unknown parameters in finite time, provided a persistence of excitation (PE) condition is satisfied. This identification method is particularly effective in the online implementation of MPC. The early chapters study continuous-time systems, and Chapters 13–14 extend the set-based estimation and robust adaptive MPC to discrete-time problems. While adaptive robust MPC is an improvement on robust MPC, this book shows that feedback MPC can be used to improve the open-loop MPC. At each sampling instant, a sequence of parameter estimates can be performed/invoked to improve the control performance. Economic MPC is also incorporated so as to improve the control performance in a broader way. This book is intended for someone learning functions of a complex variable and who enjoys using Matlab. It will enhance the experience of learning complex-variable theory and will strengthen the knowledge of someone already trained in this branch of advanced calculus. Supplying students with a bridge between the functions of complex-variable theory and Matlab, this supplemental text enables instructors to easily add a Matlab component to their complex-variables courses. The book shows students how Matlab can be a powerful learning aid in such staples of complex-variable theory as conformal mapping, infinite series, contour integration, and Laplace and Fourier transforms. In addition to Matlab programming problems, the text includes many examples in each chapter along with Matlab code.

Recently, model predictive control (MPC) is increasingly applied to path tracking of mobile devices, such as mobile robots. The characteristics of these MPC-based controllers are not identical due to the different approaches taken during design. According to the differences in the prediction models, we believe that the existing MPC-based path tracking controllers can be divided into four categories. We named them linear model predictive control (LMPC), linear error model predictive control (LEMPC), nonlinear model predictive control (NMPC), and nonlinear error model predictive control (NEMPC). Subsequently, we built these four controllers for the same mobile robot and compared them. By comparison, we got some conclusions. The real-time performance of LMPC and LEMPC is good, but they are less robust to reference paths and positioning errors. NMPC performs well when the reference velocity is high and the radius of the reference path is small. It is also robust to positioning errors. However, the real-time performance of NMPC is slightly worse. NEMPC has many disadvantages. Like LMPC and LEMPC, it performs poorly when the reference velocity is high and the radius of the reference path is small. Its real-time performance is also not good enough.

The dynamic neural network based adaptive direct nonlinear model predictive control is designed to control an industrial microwave heating pickling cold-rolled titanium process. The identifier of the direct adaptive nonlinear model identification and the controller of the adaptive nonlinear model predictive control are designed based on series-parallel dynamic neural network training by RLS algorithm with variable incremental factor, gain, and forgetting factor. These identifier and controller are used to constitute intelligent controller for adjusting the temperature of microwave heating acid. The correctness of the controller structure, the convergence, and feasibility of the control algorithms is tested by system simulation. For a given point tracking, model mismatch simulation results show that the controller can be implemented on the system to track and overcome the mismatch system model. The control model can be achieved to track on pickling solution concentration and temperature of a given reference and overcome the disturbance.

Adaptive Model Predictive Control for Constrained Nonlinear Systems

The major concern and basic requirement for control engineers, besides stability and trajectory tracking, is to consider and utilize available resources efficiently. It involves restriction of the energy consumption and associated costs in the presence of system constraints. Recently, such optimal control schemes and techniques that are constructed on the mathematical model of the system have been productively realized, both for linear and nonlinear systems, and are termed as Model Predictive Control (MPC). This paper deals with an Adaptive MPC for a nonlinear nonminimum phase single-link flexible joint manipulator (SFJM) system with constraints on the input control effort, states, and output. The non-linear system model is linearized along its trajectory. At each sampling interval, the quadratic optimization problem is solved. The optimum input control effort is implemented in Receding Horizon fashion. This meaningfully diminishes the computational burden involved in solving complex nonlinear differential equations and the associated non-convex optimization problem. The adaptive MPC offers performance which is comparable to Nonlinear Model Predictive Control (NMPC). In this paper, the closed-loop stability of the overall control scheme is presented, and its performance is matched to the general Linear MPC and NMPC. The simulation results show the superiority of the performance of adaptive MPC over linear MPC with a significant decrease in computational burden as compared to NMPC.

Dynamic obstacle avoidance of unmanned ship based on event-triggered adaptive nonlinear model predictive control

Intelligent state estimation for fault tolerant integrated frequent RTO and adaptive nonlinear MPC

SUMMARYA worldwide accident survey of manned and unmanned aircraft shows in‐flight loss of control remains a major contributor to aircraft accidents. Operation outside the normal fight envelope is usually subject to failure of components, inappropriate crew response, and environmental conditions. The aerodynamic model of aircraft associated with hazardous weather and abnormal conditions is inherently nonlinear and unsteady. A novel adaptive nonlinear model predictive controller is proposed and conceptually proven to ensure safe control of the Meridian unmanned aerial system in off‐nominal conditions. Nonlinear model predictive controllers are capable of handling input and output constraints that directly satisfy safety and performance requirements in off‐nominal conditions. The performance of a nonlinear model predictive controller relies heavily on the physics‐based model. Controller performance is improved by updating the physics‐based model by using real‐time nonlinear estimation of aerodynamic forces. Real‐time nonlinear estimation of aerodynamic forces is obtained utilizing artificial neural networks with time‐varying adaptive parameters. The adaptive nonlinear model predictive controller, coupled with real‐time parameter identification, is shown to exhibit robust characteristics and to successfully mitigate the impact of nonlinear and unsteady aerodynamics while preventing loss of control. Copyright © 2013 John Wiley & Sons, Ltd.

The rapid development of intelligent vehicles has paved the way for active chassis lateral stability, which is a novel issue and critical to vehicle stability and handling performance. To obtain active chassis lateral stability for intelligent vehicles, a nonlinear model predictive control (NMPC) method integrating active front steering and an additional yaw moment is proposed. It adopts the tire sideslip angle to express vehicle lateral stability, and addresses the actuator and security constraints and the nonlinear properties of the tire-road force effectively. Moreover, the hardware implementation, based on the field programmable gate array (FPGA), is presented to satisfy miniaturization and to discuss the computational efficiency of the proposed NMPC method. To verify the effectiveness of the presented NMPC method, offline simulations comparing the NMPC method with the direct yaw moment control (DYC) method under various running conditions and a real-time implementation experiment are carried out. The results indicate that the proposed NMPC method controls better than the DYC-based method. In addition, the presented NMPC method exhibits good robustness when the longitudinal velocity and tire-road friction coefficient vary within a suitable range. Moreover, the computational time of the proposed NMPC controller, implemented using the FPGA, is only 4.994 ms during one sampling period, which can satisfy the real-time requirement of active chassis lateral stability control.