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作者： M. J. Stijnman Delft University of Technology

学位级别：硕士

Offshore excavators are large hydraulically driven machines which are difficult to control due to slow dynamics, inherent nonlinearities, a varying environment and complex kinematics. As digging is performed under water, only limited visual feedback of the task can be provided by means of a visualization interface. Operators require an extensive amount of practice before being capable of achieving sufficient and consistent performance. Often, automation is implemented as a way of reducing costs related to expensive operators and attaining consistent performance. However, automation struggles with adapting to unforeseen situations and a large task variety, which are areas human operators excel in. Instead of attempting to fully automate excavators, this thesis takes a more human-centered approach, and focuses on the design and evaluation of a human-like controller to partially automate excavator operations, while assuming a human operator is still present to trade or share control with. In order to simultaneously deal with the various nonlinearities in the system while providing human-like control this work pro- poses the use of an adaptive model predictive controller, whose underlying principles are similar to those of humans. To determine whether the controller is indeed human- like a complex excavator model including a realistic soil model was developed and used to implement and tune the controller. Finally, a simulator experiment was conducted to compare the subjects and the controller in terms of per- formance for various tasks and the control behavior simi- larity for a well-trained task. Eight subjects controlled the excavator model and performed four stages, starting with a familiarization stage in which the subject got accustomed to the system. The other three stages (easy, difficult, boulder) featured a 9 m long target path, with conditions of varying difficulty between stages. The controller showed 2 to 3 times lower tracking errors for both the easy and d

关键词： Offshore excavator adaptive model predictive control extended kalman filter human-like control

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Robust discrete-time set-based adaptive predictive control for nonlinear systems

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JOURNAL OF PROCESS control 2016年 39卷 111-122页

作者： Goncalves, Guilherme A. A. Guay, Martin Univ Fed Rio de Janeiro COPPE Chem Engn Program BR-21941972 Rio De Janeiro Brazil Queens Univ Dept Chem Engn Kingston ON K7L 3N6 Canada

The problem of robust adaptive predictive control for a class of discrete-time. nonlinear systems is considered. First, a parameter estimation technique, based on an uncertainty set estimation, is formulated. This technique is able to provide robust performance for nonlinear systems subject to exogenous variables. Second, an adaptive MPC is developed to use the uncertainty estimation in a framework of min-max robust control. A Lipschitz-based approach, which provides a conservative approximation for the min-max problem, is used to solve the control problem, retaining the computational complexity of nominal MPC formulations and the robustness of the min-max approach. Finally, the set-based estimation algorithm and the robust predictive controller are successfully applied in two case studies. The first one is the control of anonisothermal CSTR governed by the van de Vusse reaction. Concentration and temperature regulation is considered with the simultaneous estimation of the frequency (or pre-exponential) factors of the Arrhenius equation. In the second example, a biomedical model for chemotherapy control is simulated using control actions provided by the proposed algorithm. The methods for estimation and control were tested using different disturbances scenarios. (C) 2016 Published by Elsevier Ltd.

关键词： adaptive model predictive control Robust model predictive control Nonlinear model predictive control

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A Repetitiveness Index-based adaptive Two Dimensional Iterative Learning model predictive control

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IFAC Proceedings Volumes 2014年第3期47卷 3092-3097页

作者： Jingyi Lu Zhixing Cao Furong Gao Department of Chemical and Biomolecular Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

In this paper, we consider about the control strategy design for batch processes with sever non-repetitive disturbances. An index is proposed to measure the repetitive extent of batch processes. An adaptive two dimensional iterative learning model predictive control (ILMPC) method is designed based on this index. The control algorithm is switched between an one-dimensional model predictive control (MPC) and a two time dimensional ILMPC according to this index. Simulation shows the superior effects of the proposed algorithm in handling abrupt changes of plant dynamics.

关键词： Performance assessment adaptive model predictive control iterative learning control(ILC) two-dimensional control batch processes

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adaptive model predictive inventory controller for multiproduct batch plant

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AICHE JOURNAL 2015年第6期61卷 1867-1880页

作者： Yi, Gyeongbeom Reklaitis, Gintaras V. Pukyong Natl Univ Dept Chem Engn Busan 608739 South Korea Purdue Univ Sch Chem Engn W Lafayette IN 47907 USA

An inventory control system was developed for multiproduct batch plants with an arbitrary number of batch processes and storage units. Customer orders are received by the plant at order intervals and in order quantities that are subject to random fluctuations. The objective of the plant operation is to minimize the total cost while maintaining inventory levels within the storage or warehouse capacity by adjusting the startup times, the quantities of raw material orders, and production batch sizes. An adaptive model predictive control algorithm was developed that uses a periodic square wave model to represent the flows of the material between the processes and the storage units. The boundedness of the control output and the convergence of the estimated parameters in implementations of the proposed algorithm were mathematically proven under the assumption that disturbances in the orders are bounded. The effectiveness of this approach was demonstrated by performing simulations. (c) 2015 American Institute of Chemical Engineers AIChE J, 61: 1867-1880, 2015

关键词： adaptive model predictive control multiproduct batch plant periodic square wave model batch-storage network

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Tracking control of piezoelectric actuator using adaptive model

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Robotics and biomimetics 2016年第1期3卷 5页

作者： Tran Vu Minh Nguyen Manh Linh Xinkai Chen School of Mechanical Engineering Hanoi University of Science and Technology Hanoi Vietnam. Graduate School of Engineering and Science Shibaura Institute of Technology Saitama 337-8570 Japan. Department of Electronic and Information Systems Shibaura Institute of Technology Saitama 337-8570 Japan.

Piezoelectric actuators (PEAs) have been widely used in micro- and nanopositioning applications due to their fine resolution, rapid responses, and large actuating forces. However, a major deficiency of PEAs is that their accuracy is seriously limited by hysteresis. This paper presents adaptive model predictive control technique for reducing hysteresis in PEAs based on autoregressive exogenous model. Experimental results show the effectiveness of the proposed method.

关键词： adaptive model predictive control Hysteresis Piezoelectric actuator

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adaptive model predictive inventory controller for multiproduct warehouse system

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AUTOMATICA 2014年第9期50卷 2245-2253页

作者： Yi, Gyeongbeom Reklaitis, Gintaras V. Pukyong Natl Univ Dept Chem Engn Pusan 608739 South Korea Purdue Univ Sch Chem Engn W Lafayette IN 47907 USA

An inventory control system has been developed for a distribution system consisting of a single multi-product warehouse serving a set of customers and purchasing products from multiple vendors. Purchase orders requesting multiple products are delivered to the warehouse in a process referred to as joint replenishment. The receipt of customer orders by the warehouse proceeds in order intervals and in order quantities that are subject to random fluctuations. The objective of Warehouse operation is to minimize the total cost while maintaining inventory levels within the warehouse capacity by adjusting the purchase order intervals and quantities. An adaptive model predictive control algorithm is developed using a periodic square wave model to represent the material flows. The adaptive concept incorporates a stabilized minimum variance control-type input calculation coupled with input/output stream parameter predictions. The boundedness of the control output under the suggested algorithm is mathematically proven under the assumption that disturbances in the orders are bounded. The effectiveness of the scheme was demonstrated using simulations. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： adaptive model predictive control Multiproduct warehouse Periodic square wave model

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adaptive model predictive control OF DISSOLVED OXYGEN IN A BIOREACTOR

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IFAC Proceedings Volumes 2007年第5期40卷 53-58页

作者： R. Palpandi Raja Abhijit S. Badwe Sachin C. Patwardhan Santosh B. Noronha Department of Chemical Engineering Indian Institute of Technology Bombay Powai Mumbai 400076 India

Dissolved oxygen variation in a bioreactor is representative of a nonlinear time varying system. In this work we propose a novel algorithm for adaptive model predictive control (AMPC) of dissolved oxygen in a bioreactor. The model is comprised of deterministic and stochastic components and is updated on-line by Recursive Least Squares method. The deterministic and stochastic components are then combined to form a state space model to formulate AMPC at each sampling instant. The proposed AMPC scheme is shown to achieve tight control of dissolved oxygen in Escherichia coli cultivation in the presence of disturbances.

关键词： adaptive model predictive control Recursive Least Squares Dissolved Oxygen Bioreactor control Escherichia coli

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A constrained recursive pseudo-linear regression scheme for on-line parameter estimation in adaptive control

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JOURNAL OF PROCESS control 2010年第5期20卷 559-572页

作者： Badwe, Abhijit S. Singh, Angadh Patwardhan, Sachin C. Gudi, Ravindra D. Indian Inst Technol Dept Chem Engn Bombay 400076 Maharashtra India

In adaptive control of systems with poles close to the unit circle, application of the recursive estimation techniques can lead to excursions of the poles of the identified model outside the unit circle even when the process is open loop stable These excursions can be of two types. The poles of the deterministic component of the model can drift outside unit circle even when the process has no unstable modes. Alternatively, the poles and/or zeros of the unmeasured disturbance (noise) model can drift outside the unit circle In either case, the identified model is not suitable for on-line controller adaptation In this work, a novel constrained recursive formulation is proposed for on-line parameter estimation based on the pseudo-linear regression (PLR) approach. The efficacy of the proposed approach is demonstrated by conducting experimental studies on a benchmark laboratory scale heater-mixer setup The analysis of the open and closed loop experimental results reveals that the proposed constrained parameter estimation scheme provides a systematic and computationally attractive approach to ensure that the identified model parameters are restricted to the feasible region (C) 2010 Elsevier Ltd All rights reserved.

关键词： Recursive parameter estimation Pseudo-linear regression Constrained state estimation adaptive model predictive control

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Cement Rotary Kiln control: A Supervised adaptive model predictive Approach

Cement Rotary Kiln Control: A Supervised Adaptive Model Pred...

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IEEE Conference and Exhibition on control, Communications and Automation

作者： Ziatabari, Javaneh Fatehi, Alireza Beheshti, Mohamad T. H. Univ Trabiat Modares Dept Elect Engn Tehran Iran K N Toosi Univ Technol Fac Elect Engn Adv Proc Automat & Control APAC Tehran Iran

ISBN: (纸本)9781424427468

Considering the need of an advanced process control in cement industry, this paper presents an adaptive model predictive algorithm to control a white cement rotary kiln. As any other burning process, the control scenario is to expect the controller to regulate the temperature and the period of baking a fixed quantity of raw material as desired, as well as to have the concentration of the combustion gases under control. To achieve these goals, this work presents a strategy which includes multivariable online identification of the kiln process and a constrained generalized predictive controller. An MLP neural network model derived from real plant data of Saveh cement factory in Iran is used as the kiln process simulator. The control efforts are made taken into account the operating constraints. At last the proposed control strategy is modified so as to gain good disturbance rejection ability.

关键词： adaptive model predictive control Time Delayed Process Disturbance rejection Rotary Cement Kiln Saveh White Cement Factory (SWCF)

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Trim of rotorcraft multibody models using a neural-augmented model-predictive auto-pilot

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MULTIBODY SYSTEM DYNAMICS 2007年第3期18卷 299-321页

作者： Bottasso, Carlo L. Riviello, Luca Politecn Milan Dipartimento Ingn Aerospaziale I-20156 Milan Italy

The aeromechanical analysis of rotorcraft using comprehensive multibody vehicle models is crucially dependent on the ability to accurately compute the model trim settings. Among the various techniques proposed in the literature, the auto-pilot approach, being independent of the complexity of the model, has the potential to solve trim problems efficiently even for the highly detailed aero-servo-elastic vehicle models that are developed using modern finite-element-based multibody analysis codes. Published proportional auto-pilots show to work well in many practical instances. However, their robustness with respect to the flight condition is often poor, so that they must be accurately tuned. In this paper, an auto-pilot based on adaptive non-linear model-predictive control is proposed. The formulation uses a non-linear reference model of the rotorcraft, which is augmented with an adaptive neural element. The adaptive element identifies and corrects the mismatch between reduced and comprehensive models, thereby improving the predictive capabilities of the controller. The methodology is tested on the wind-tunnel trim of a rotor multibody model and compared to an existing implementation of a classical auto-pilot for comprehensive rotorcraft analysis applications. The proposed controller shows improved stability over the conventional approach without the need for calibration.

关键词： rotor trim aeroelasticity flight mechanics adaptive model predictive control

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