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Design of a tracking controller for autonomous articulated heavy vehicles using a nonlinear model predictive control technique

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART K-JOURNAL OF MULTI-BODY DYNAMICS 2024年第2期238卷 334-362页

作者： Sharma, Tarun He, Yuping Univ Ontario Inst Technol Dept Automot & Mechatron Engn Oshawa ON Canada Univ Ontario Inst Technol Dept Automot & Mechatron Engn Oshawa ON L1G 0C5 Canada

This article proposes a design of a tracking controller for autonomous articulated heavy vehicles (AAHVs) using a nonlinear model predictive control (NLMPC) technique. Despite economic and environmental benefits in freight transportation, articulated heavy vehicles (AHVs) exhibit poor directional performance due to their large sizes, multi-unit vehicle configurations, and high centers of gravity (CGs). AHVs represent a 7.5 times higher risk of traffic accidents than single-unit vehicles (e.g. rigid trucks, cars, etc.) in highway operations. Human driver errors cause about 94% of traffic collisions. However, little attention has been paid to autonomous driving control of AHVs. To increase the safety of AHVs, we design a novel NLMPC-based tracking controller for an AHV, that is, a tractor/semi-trailer combination, and this tracking controller is distinguished from others with the feature of controlling both the lateral and longitudinal motions for both the leading and trailing units. To design the tracking controller, a new prediction AHV model is developed, which represents both the lateral and longitudinal dynamics of the vehicle and captures its rearward amplification feature over high-speed evasive maneuvers. With the proposed tracking controller, the AAHV tracks the predefined reference path and follows a planned forward-speed scheme. Co-simulation demonstrates the effectiveness and robustness of the proposed NLMPC tracking controller.

关键词： Autonomous driving control tracking-controller design nonlinear model predictive control articulated heavy vehicles multi-body vehicle models co-simulations

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ParNMPC - a parallel optimisation toolkit for real-time nonlinear model predictive control

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INTERNATIONAL JOURNAL OF control 2022年第2期95卷 390-405页

作者： Deng, Haoyang Ohtsuka, Toshiyuki Kyoto Univ Grad Sch Informat Dept Syst Sci Kyoto Japan

Real-time optimisation for nonlinear model predictive control (NMPC) has always been challenging, especially for fast-sampling and large-scale applications. This paper presents an efficient implementation of a highly parallelisable method for NMPC, called ParNMPC. The implementation details of ParNMPC are introduced, including a dedicated discretisation method suitable for parallelisation, a framework that unifies search direction calculation done using Newton's method and the parallel method, line search methods for guaranteeing convergence, and a warm start strategy for the interior-point method. To assess the performance of ParNMPC under different configurations, three experiments including a closed-loop simulation of a quadrotor, a real-world control example of a laboratory helicopter and a closed-loop simulation of a robot manipulator are shown. These experiments show the effectiveness and efficiency of ParNMPC both in serial and parallel.

关键词： nonlinear model predictive control parallel computing real-time optimisation

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Approximate nonlinear model predictive control with in situ adaptive tabulation

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COMPUTERS & CHEMICAL ENGINEERING 2008年第4-5期32卷 706-714页

作者： Hedengren, John D. Edgar, Thomas F. PAS Inc Houston TX 77062 USA Univ Texas Austin Dept Chem Engn Austin TX 78712 USA

In situ adaptive tabulation (ISAT) is applied to store and retrieve solutions of nonlinear model predictive control (NMPC) problems. ISAT controls approximation error by adaptively building the database of NMPC solutions with piecewise linear local approximations. Unlike initial state or constraint parameterized constrained quadratic programming (QP) solutions, ISAT approximates NMPC solutions within a specified tolerance, thereby easing the dimensionality difficulties of these other techniques. In the limit, as the specified tolerance is reduced to zero and for linear models with quadratic objective functions, ISAT becomes an adaptive version of state parameterized storage and retrieval of mp-QP problems. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： in situ adaptive tabulation nonlinear model predictive control computational reduction

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THE ROLE OF SAMPLING FOR STABILITY AND PERFORMANCE IN UNCONSTRAINED nonlinear model predictive control

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SIAM JOURNAL ON control AND OPTIMIZATION 2014年第1期52卷 581-605页

作者： Worthmann, Karl Reble, Marcus Gruene, Lars Allgoewer, Frank Ilmenau Univ Technol Math Inst D-98683 Ilmenau Germany Univ Stuttgart Inst Syst Theory & Automat Control D-70550 Stuttgart Germany Univ Bayreuth Math Inst D-95440 Bayreuth Germany

We investigate the impact of sampling on stability and performance estimates in nonlinear model predictive control without stabilizing terminal constraints or costs. Interpreting the sampling period as a discretization parameter, the relation between continuous and discrete time estimates depending on this parameter is analyzed. The technique presented in this paper allows us to determine the sampling rate required in order to approximate the continuous time suboptimality bound arbitrarily well and, thus, gives insight into the trade-off between sampling time and guaranteed performance.

关键词： nonlinear model predictive control performance guarantee suboptimality estimate sampling rate multistep feedback laws discretization receding horizon control relaxed Lyapunov inequality

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Iterative nonlinear model predictive control. Stability, robustness and applications

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control ENGINEERING PRACTICE 2008年第9期16卷 1023-1034页

作者： Cueli, Jose R. Bordons, Carlos Univ Seville Escuela Super Ingn Dept Ingn Sistemas & Automat Seville 41092 Spain

The article presents a new methodology applicable to batch processes called iterative nonlinear model predictive control (INMPC). It incorporates the ability of learning from past batches (known as iterative learning control or ILC) to an underlying nonlinear model predictive controller. The main advantage with respect to the existent iterative controllers is a faster convergence to the set points and guaranteed stability. The convergence is proven for a wide class of nonlinear processes when the desired trajectory is reachable. The controller ensures convergence for the nominal model whatever the parameters are. In the presence of uncertainties, a numerical analysis using randomized algorithms is utilized to ensure the desired probability of stability. The controller is tested on a highly nonlinear example with noise and model uncertainty, a pH plant. A numerical robustness analysis has been done in order to verify the sensitivity and performance of this new algorithm. The results obtained in the experiments are good, bettering other existent controllers. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： iterative learning control nonlinear model predictive control batch processes

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Accurate Pseudospectral Optimization of nonlinear model predictive control for High-Performance Motion Planning

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第2期8卷 1034-1045页

作者： Gao, Feng Han, Yu Li, Shengbo Eben Xu, Shaobing Dang, Dongfang Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Shanghai Jiao Tong Univ Sichuan Res Inst Chengdu 610200 Peoples R China Tsinghua Univ Sch Vehicle & Mobil State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA Res & Dev Ctr Guangzhou Automobile Grp Guangzhou 511434 Peoples R China

nonlinear model predictive control (NMPC) is an effective method for motion planning of automated vehicles, but the high computational load of numerical optimization limits its practical application. This paper designs an NMPC based motion planner and presents two techniques, named adaptive Lagrange discretization and hybrid obstacle avoidance constraints, to accelerate the numerical optimization by reducing the optimization variables and simplifying the non-convex constraints. Given the high nonlinearity of vehicle dynamics, the Lagrange interpolation is adopted to convert the state equation of vehicle dynamics and the objective function to ensure a preset accuracy but with less discretization points. An adaptive strategy is then designed to adjust the order of Lagrange polynomials based on the distribution of discretization error. Moreover, a hybrid strategy is presented to construct the constraints for obstacle avoidance by combing the elliptic and linear time-varying methods together. It can ensure driving safety and also make a good balance between computing load and accuracy. The performance of these techniques on accelerating the NMPC based motion planner is validated and analyzed by comparative numerical simulations and experimental tests under various scenarios. Compared with traditional methods, the results show that these techniques improve accuracy and efficiency by 74% and 60%, respectively.

关键词： Optimization Vehicle dynamics Planning Collision avoidance Dynamics Numerical models Computational modeling Autonomous driving motion planning nonlinear model predictive control numerical optimization

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Coordinating Tire Forces to Avoid Obstacles Using nonlinear model predictive control

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2020年第1期5卷 21-31页

作者： Brown, Matthew Gerdes, J. Christian Stanford Univ Dept Mech Engn Stanford CA 94305 USA

In order to safely navigate highly dynamic scenarios, automated vehicles must be able to react quickly to changes in the environment and be able to understand trade-offs between lateral and longitudinal forces when limited by tire-road friction. We present a design and experimental validation of a nonlinear model predictive controller that is capable of handling these complex situations. By carefully selecting the vehicle model and mathematical encodings of the vehicle and obstacles, we enable the controller to quickly compute inputs while maintaining an accurate model of the vehicle's motion and its proximity to obstacles. Experimental results of a test vehicle performing an emergency double lane change to avoid two "pop-up" obstacles demonstrate the ability of the controller to coordinate lateral and longitudinal tire forces even in emergency situations when the tires are at their friction limits.

关键词： Automated vehicles vehicle dynamics obstacle avoidance nonlinear model predictive control

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modeling and nonlinear model predictive control of a Variable-Sweep-Wing Morphing Waverider

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IEEE ACCESS 2021年 9卷 63510-63520页

作者： Dai, Pei Yan, Binbin Liu, Ruifan Liu, Shuangxi Wang, Mingang Northwestern Polytech Univ Sch Astronaut Xian 710072 Peoples R China

The variable-sweep-wing morphing waverider is a promising solution for the wide speed range flight with adaptability to various mission requirements. However, it is a highly nonlinear system with inherent uncertainties and complex internal couplings due to the alteration of its configuration. In this paper, a dynamic and kinematical model with explicit expression of nonlinear/coupled forces and moments caused by morphing is proposed in a six degree of freedom flight. However, the above morphing forces and moments are difficult to measure and they can be estimated by the designed nonlinear disturbance observer. Then, a nonlinear model predictive controller is developed based on the state space model considering estimated disturbances. The global asymptotic stability of the composite controller can be guaranteed through stability analysis. Lateral weaving numerical simulation cases of the morphing waverider are carried out to investigate lateral maneuverability and overall controller performance.

关键词： Aerodynamics predictive models Atmospheric modeling Aircraft Aerospace control Vehicle dynamics Adaptation models Morphing waverider nonlinear model predictive control disturbance observer variable-sweep-wing wide-speed-range vehicle

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A moving switching sequence approach for nonlinear model predictive control of switched systems with state-dependent switches and state jumps

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INTERNATIONAL JOURNAL OF ROBUST AND nonlinear control 2020年第2期30卷 719-740页

作者： Katayama, Sotaro Doi, Masahiro Ohtsuka, Toshiyuki Kyoto Univ Grad Sch Informat Dept Syst Sci Kyoto 6068501 Japan Toyota Motor Co Ltd Z Frontier Div Planning & Management Dept Adv Robot Grp Tokyo Japan

In this paper, we propose an approach for real-time implementation of nonlinear model predictive control (NMPC) for switched systems with state-dependent switches called the moving switching sequence approach. In this approach, the switching sequence on the horizon moves to the present time at each time as well as the optimal state trajectory and the optimal control input on the horizon. We assume that the switching sequence is basically invariant until the first predicted switching time reaches the current time or a new switch enters the horizon. This assumption is reasonable in NMPC for systems with state-dependent switches and reduces computational cost significantly compared with the direct optimization of the switching sequence all over the horizon. We update the switching sequence by checking whether an additional switch occurs or not at the last interval of the present switching sequence and whether the actual switch occurs or not between the current time and the next sampling time. We propose an algorithm consisting of two parts: (1) the local optimization of the control input and switching instants by solving the two-point boundary-value problem for the whole horizon under a given switching sequence and (2) the detection of an additional switch and the reconstruction of the solution taking into account the additional switch. We demonstrate the effectiveness of the proposed method through numerical simulations of a compass-like biped walking robot, which contains state-dependent switches and state jumps.

关键词： biped walking hybrid systems nonlinear model predictive control switched systems

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Lower boundary based nonlinear model predictive control of transmission power for smart grid WSNs

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COMPUTER COMMUNICATIONS 2022年 190卷 99-109页

作者： Xue, Xue Sun, Wei Wang, Jianping Li, Qiyue Zhang, Haiyan Mu, Daoming Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Peoples R China

In the application of data collection and communication in smart grid based on wireless sensor networks (WSNs), communication reliability is the key technical index of WSNs. Theoretically, adjusting the transmission power can control the signal-to-noise ratio (SNR), thereby improving the reliability of wireless communication. However, wireless signal is random changes caused by external device interference or environmental factors. Hence, we study a control of transmission power to meet reliability requirements while maximizing the overall efficiency of the network. Considering the random characteristics of wireless links, we introduce the predictive lower boundary with probability guarantees, and establish a nonlinear state-space model that chooses the SNR as the state variable. Subsequently, we adopt the lower boundary based nonlinear model predictive control (LB-NMPC) algorithm to solve the optimal transmission power, and creatively prove the feasibility and stability. Furthermore, we compare the proposed LB-NMPC algorithm with the adaptive transmission power control (ATPC) algorithm, the potential feedback control (PFC) algorithm and the confidence interval based model predictive control (CI-MPC) algorithm by simulation, and the comparative results show that our LB-NMPC algorithm has lower transmission power and less bad controls. Finally, we also test the LB-NMPC algorithm in real-world indoor substation and outdoor substation to verify its practicability and accuracy.

关键词： Wireless communication reliability Smart grid Transmission power control Lower boundary nonlinear model predictive control

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