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non-linear control algorithms for an unmanned surface vehicle

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART M-JOURNAL OF ENGINEERING FOR THE MARITIME ENVIRONMENT 2014年第2期228卷 146-155页

作者： Sharma, Sanjay K. Sutton, Robert Motwani, Amit Annamalai, Andy Univ Plymouth Marine & Ind Dynam Anal Grp Ctr Adv Engn Syst & Interact Sch Marine Sci & Engn Plymouth PL4 8AA Devon England

Although intrinsically marine craft are known to exhibit non-linear dynamic characteristics, modern marine autopilot system designs continue to be developed based on both linear and non-linear control approaches. This article evaluates two novel non-linear autopilot designs based on non-linear local control network and non-linear model predictive control approaches to establish their effectiveness in terms of control activity expenditure, power consumption and mission duration length under similar operating conditions. From practical point of view, autopilot with less energy consumption would in reality provide the battery-powered vehicle with longer mission duration. The autopilot systems are used to control the non-linear yaw dynamics of an unmanned surface vehicle named Springer. The yaw dynamics of the vehicle being modelled using a multi-layer perceptron-type neural network. Simulation results showed that the autopilot based on local control network method performed better for Springer. Furthermore, on the whole, the local control network methodology can be regarded as a plausible paradigm for marine control system design.

关键词： Unmanned surface vehicle autopilot design non-linear model predictive control local control network genetic algorithm neural networks

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Generalized nonlinear Optimal predictive control using Iterative State-space Trajectories: Applications to Autonomous Flight of UAVs

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INTERNATIONAL JOURNAL OF control AUTOMATION AND SYSTEMS 2015年第2期13卷 361-370页

作者： Murillo, Marina H. Limache, Alejandro C. Rojas Fredini, Pablo S. Giovanini, Leonardo L. Consejo Nacl Invest Cient & Tecn Res Ctr Computat Methods CIMEC Santa Fe Argentina Univ Nacl Litoral FICH Res Ctr Signals Syst & Computat Intelligence SINC Santa Fe Argentina

model predictive control (MPC) is a modern technique that, nowadays, encapsulates different optimal control techniques. For the case of non-linear dynamics, many possible variants can be developed which can lead to new control algorithms. In this manuscript a novel generic control system method is presented. This method can be applied to control, in an optimal way, different systems having non-linear dynamics. Particularly, in this paper, the proposed technique is presented in the context of developing a control system for autonomous flight of UAVs. This technique can be used for different types of aerial vehicles having any type of generic non-linear dynamics. The presented method is based on the use of iteratively defined optimal candidate state-space trajectories in global state-space. The method uses, a generalized linearization process which, opposite to standard methods, does not need to be predefined in a certain equilibrium state but instead it is performed along any arbitrary state. The technique allows the inclusion of constraints with ease. The presented technique is used as a centralized control system unit that is able to control the full aircraft dynamics without the need of decoupling the system in different reduced modes. The technique is tested by making a Cessna 172 airplane model to perform the following autonomous unmanned maneuvers: climbing at constant speed to a desired altitude, heading change to a desired flight direction, and, coordinate turn.

关键词： non-linear model predictive control optimization state-space trajectories prediction unmanned aerial vehicles

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Logic Embedded NMPC of a Solar Air Conditioning Plant

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EUROPEAN JOURNAL OF control 2008年第6期14卷 484-500页

作者： Rodriguez, M. De Prada, C. Capraro, F. Cristea, S. Univ Valladolid Dept Syst Engn & Automat Control Valladolid Spain

This paper presents a hybrid controller for a solar air conditioning plant, located at the University of Seville, Spain, and used as a benchmark for the HYCON NoE of the European Union (EU). The plant uses two sources of energy: solar and gas, plus a set of accumulation tanks and an absorption tower to provide conditioned air to a university building. The hybrid character comes from the discrete decisions that have to be taken in relation with the energy source in use, which is selected by means of on/off valves, plus the continuous nature of the process and other manipulated variables. The control aim is to operate the conditioned air system using the smaller possible amount of energy from gas and maintaining other variables close to their set points. The hybrid control is based on a model predictive control strategy developed with the objective of dealing with the mixed discrete-continuous nature of the process in an efficient way. A novel approach incorporating an internal model with embedded logic control is used to transform the hybrid problem in a continuous-nonlinear one where NMPC can be applied. The paper presents results obtained both, in simulation and in the realsystem.

关键词： Hybrid control non-linear model predictive control solar air conditioning plants dynamic optimization

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Distributed nonlinear MPC of Multi-Agent Systems With Data Compression and Random Delays

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IEEE TRANSACTIONS ON AUTOMATIC control 2016年第3期61卷 817-822页

作者： El-Ferik, Sami Siddiqui, Bilal A. Lewis, Frank L. King Fahd Univ Petr & Minerals Dept Syst Engn Dhahran Saudi Arabia DHA Suffa Univ Dept Mech Engn Karachi Pakistan Univ Texas Arlington Res Inst Arlington TX 76019 USA

This note proposes an Input to State practically Stable (ISpS) formulation of distributed nonlinear model predictive controller (NMPC) for formation control of constrained autonomous vehicles in presence of communication bandwidth limitation and transmission delays. Planned trajectories are compressed using neural networks resulting in considerable reduction of data packet size, while being robust to propagation delays and uncertainty in neighbors' trajectories. Collision avoidance is achieved by means of spatially filtered potential field. Analytical results proving ISpS and generalized small gain conditions are presented for both strongly-and weakly-connected networks, and illustrated by simulations.

关键词： Collision avoidance distributed nonlinear control formation control multi-agent systems neural network non-linear model predictive control

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controller design for polymer electrolyte membrane fuel cell systems for automotive applications

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2021年第45期46卷 23263-23278页

作者： Gomez, Juan Carlos Serra, Maria Husar, Attila UPC CSIC Inst Robot & Informat Ind C Llorens & Artigas 4-6 Barcelona 08028 Spain

Continuous developments in Proton Exchange Membrane Fuel Cells (PEMFC) make them a promising technology to achieve zero emissions in multiple applications including mobility. Incremental advancements in fuel cells materials and manufacture processes make them now suitable for commercialization. However, the complex operation of fuel cell systems in automotive applications has some open issues yet. This work develops and compares three different controllers for PEMFC systems in automotive applications. All the controllers have a cascade control structure, where a generator of setpoints sends references to the subsystems controllers with the objective to maximize operational efficiency. To develop the setpoints generators, two techniques are evaluated: off-line optimization and model predictive control (MPC). With the first technique, the optimal setpoints are given by a map, obtained off-line, of the optimal steady state conditions and corresponding setpoints. With the second technique, the setpoints time profiles that maximize the efficiency in an incoming time horizon are continuously computed. The proposed MPC architecture divides the fast and slow dynamics in order to reduce the computational cost. Two different MPC solutions have been implemented to deal with this fast/slow dynamics separation. After the integration of the setpoints generators with the subsystems controllers, the different control systems are tested and compared using a dynamic detailed model of the automotive system in the INN-BALANCE project running under the New European Driving Cycle. (c) 2021 The Authors. Published by Elsevier Ltd on behalf of Hydrogen Energy Publications LLC. This is an open access article under the CC BY-NC-ND license (http:// ***/licenses/by-nc-nd/4.0/).

关键词： Automotive application control structure Efficiency optimization Fuel cell system non-linear model predictive control

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Artificial locomotion control: from human to robots

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ROBOTICS AND AUTONOMOUS SYSTEMS 2004年第4期47卷 203-223页

作者： Azevedo, C Poignet, P Espiau, B CNRS DPA P3M F-13402 Marseille 20 France LIRMM F-34392 Montpellier 05 France INRIA Rhone Alpes F-38334 Saint Ismier France

This paper concerns the simultaneous synthesis and control of walking gaits for biped robots. The goal is to propose an adaptable and reactive control law for two-legged machines. The problem is addressed with human locomotion as a reference. The starting point of our work is an analysis of human walking from descriptive (biomechanics) as well as explicative (neuroscience and physiology) points of view, the objective being to stress the relevant elements for the approach of robot control. The adopted principles are then: no joint trajectory tracking;explicit distinction and integration of postural and walking control;use of evolutive optimization objectives;on-line event handling and environment adaptation and anticipation. This leads to the synthesis of an original control scheme based on non-linear model predictive control: Trajectory Free NMPC. The movement is specified implicitly through coherent physical inequality constraints. Dynamic model and internal limitations of the system are part of the problem constraints. This work is validated by simulation results obtained for the Bip and Rabbit biped robots in various walking and standing situations and compared to human data recorded in these same situations. (C) 2004 Elsevier B.V. All rights reserved.

关键词： biped robots non-linear model predictive control optimization under constraints artificial walking TF-NMPC

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Thermal comfort control using a non-linear MPC strategy: A real case of study in a bioclimatic building

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JOURNAL OF PROCESS control 2014年第6期24卷 703-713页

作者： Castilla, M. Alvarez, J. D. Normey-Rico, J. E. Rodriguez, F. Univ Almeria CIESOL Joint Ctr Univ Almeria CIEMAT Automat Control Elect & Robot Res Grp Almeria Spain Univ Seville Dept Automat & Syst Technol Seville Spain Univ Fed Santa Catarina Dept Automat & Syst Florianopolis SC Brazil

The aim to maintain thermal comfort conditions in confined environments may require complex regulation procedures and the proper management of an HVAC (heating, ventilation and air conditioning) system. This problem is being widely analyzed, since it has a direct effect on users' productivity, and an indirect effect on energy saving. This paper presents a hierarchical thermal comfort control system with two layers. The upper layer includes a non-linear model predictive controller that allows to obtain a high thermal comfort level by optimizing the use of an HVAC system in order to reduce, as much as possible, the energy consumption. On the other hand, the lower layer is formed by a PIE) (proportional, integrative and derivative) controller with anti-windup function which is in charge of reach the setpoints calculated by the non-linear model predictive controller. In order to probe the effectiveness of the proposed control system, suitable real results obtained in a bioclimatic building are included and commented. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Hierarchical control non-linear model predictive control Thermal comfort PMV index Energy efficiency

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Data-Driven modeling and control of Cyclic Variability of an Engine Operating in Low Temperature Combustion Modes

Data-Driven Modeling and Control of Cyclic Variability of an...

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modeling, Estimation and control Conference (MECC)

作者： Batool, Sadaf Naber, Jeffrey D. Shahbakhti, Mahdi Michigan Technol Univ Houghton MI 49931 USA Univ Alberta Edmonton AB Canada

Combustion cyclic variability in an internal combustion engine leads to cyclic variations in the engine torque output and emissions. Combustion cyclic variability is often characterized by coefficient of variation of indicated mean effective pressure (COVIMEP) that is used as an indicator of combustion stability. These cyclic variations are inevitable and cannot be completely eliminated but can be controlled to allow stable engine operation. This work focuses on control oriented modeling of COVIMEP to limit engine cyclic variations in low temperature combustion (LTC) modes. COVIMEP is generally stochastic in nature;thus, a data-driven approach is used to develop a predictive model of COVIMEP for Homogeneous Charge Compression Ignition (HCCI) and Reactivity controlled Compression Ignition (RRCI) modes. This work presents the development of a cycle-by-cycle model predictive controller for a 2.0 liter multi-mode LTC engine. Physics-based control-oriented models for combustion phasing (CA50) and IMEP are augmented with the new data-driven COVIMEP model to limit the cyclic variations below 3% for HCCI and RCCI modes. These models are then used to design closed-loop non-linear model predictive controllers to control CA50 and IMEP while constraining COVIMEP to ensure stable engine operation for varying load conditions. Copyright (C) 2021 The Authors.

关键词： Combustion Stability Machine Learning non-linear model predictive control Homogeneous Charge Compression Ignition (HCCI) Reactivity controlled Compression Ignition (RCCI)

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A Parameterized NMPC Scheme for Embedded control of Semi-active Suspension System

A Parameterized NMPC Scheme for Embedded Control of Semi-act...

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6th International-Federation-of-Automatic-control (IFAC) Conference on nonlinear-model-predictive-control (NMPC)

作者： Rathai, Karthik Murali Madhavan Alamir, Mazen Sename, Olivier Tang, Rattena Univ Grenoble Alpes CNRS Grenoble INP GIPSA Lab F-38000 Grenoble France

This paper proposes a parameterized NMPC scheme for control of semi-active suspension system for a quarter car vehicle model. The proposed controller is compared against a linearization based MPC to verify its performance under real-time (RT) embedded conditions. The linearization based MPC method linearizes the non-linear dynamical system/constraints at the current operating point and a linear MPC problem is solved by means of a quadratic program (QP) solver to obtain the optimal control input. The proposed parameterized NMPC method finitely parameterizes the control input and for each parameterized input, the nonlinear dynamical system is numerically simulated and the optimal input is elicited from the simulations which minimizes the objective function and satisfies the system constraints. The methods were considered on the basis of RT implementable scenario and practical viability for the target system. The methods were successfully tested by means of hardware in the loop (HIL) simulations under RT conditions for given control objective on dSPACE MicroAutoBox II hardware. The results from HIL simulations exudes better performance of parameterized NMPC against linearization based MPC in terms of computational efficiency and RT feasibility of the controller. (C) 2018, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Automotive control non-linear model predictive control Real-time control Vertical Dynamics

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Dynamic Obstacle Avoidance in Cobots with Multiple Random Moving Obstacles 10

Dynamic Obstacle Avoidance in Cobots with Multiple Random Mo...

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10th International Conference on control, Automation and Robotic (ICCAR)

作者： Kodandaramu, Laasya Srikantan, Maalavika Sanoj, Jacob V. Narayan, Kaushik Tripathi, Shikha PES Univ Dept Elect & Commun Engn Bengaluru India

ISBN: (纸本)9798350373172;9798350373189

Traditional industrial robots are generally massive, heavy, and dangerous and are therefore generally isolated from humans. Collaborative robots or COBOTs are designed to collaborate and work with humans in a shared workspace. These are typically smaller and lighter than traditional industrial robots and are therefore more flexible and easier to deploy in a variety of settings. One of the most crucial problems that need to be addressed for cobots is Dynamic Obstacle Avoidance as they will be working in an environment where there will be constant human movement. This paper presents an efficient solution to this problem employing a non-linear model predictive controller (NMPC) for real-time path planning. It has been used to obtain the most optimum trajectory while avoiding dynamic obstacles. The KINOVA Gen3 manipulator has been simulated with static and dynamic obstacle modelling. The NMPC controls the arm to minimize differences between the system's current state and the desired goal position while avoiding obstacles. A detailed visualization of the system motion has also been included. The work has been tested and the results have been shown for 4 different cases - reference case with no obstacles, case 1 with three obstacles, case 2 with four obstacles and case 3 with a simulated human-like obstacle to mimic a typical cobot scenario. Detailed error analysis including end-effector and implementation errors has also been shown for each of the 4 cases and is promising.

关键词： Cobots non-linear model predictive control Manipulator MATLAB Kinova Gen3 dynamic obstacles

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