This paper presents a new automatic tuning method for cascade controlsystems based on a single closed-loop step test. The proposed method identifies the required process information with the help of B-spline series e...
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In this paper the hardware development for the implementation of a Two Wheeled Inverted Pendulum (TWIP) and the closed loop identification process is described. In the identification process, a linear model for a sing...
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This paper considers optimal operation of an hybrid powered energy system. The two power sources include a direct methanol fuel cell and a lithium-ion battery. A portable system represented by characteristic dynamic l...
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Emulsion polymerization processes are characterized by exothermic reactions with complicated nonlinear dynamics. A very precise temperature control is necessary in order to ensure that the end product satisfies the qu...
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We present a new approach to motion planning under sensing and motion uncertainty by computing a locally optimal solution to a continuous partially observable Markov decision process (POMDP). Our approach represents b...
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We present a new approach to motion planning under sensing and motion uncertainty by computing a locally optimal solution to a continuous partially observable Markov decision process (POMDP). Our approach represents beliefs (the distributions of the robot's state estimate) by Gaussian distributions and is applicable to robot systems with non-linear dynamics and observation models. The method follows the general POMDP solution framework in which we approximate the belief dynamics using an extended Kalman filter and represent the value function by a quadratic function that is valid in the vicinity of a nominal trajectory through belief space. Using a belief space variant of iterative LQG (iLQG), our approach iterates with second-order convergence towards a linear control policy over the belief space that is locally optimal with respect to a user-defined cost function. Unlike previous work, our approach does not assume maximum-likelihood observations, does not assume fixed estimator or control gains, takes into account obstacles in the environment, and does not require discretization of the state and action spaces. The running time of the algorithm is polynomial (O[n(6)]) in the dimension n of the state space. We demonstrate the potential of our approach in simulation for holonomic and non-holonomic robots maneuvering through environments with obstacles with noisy and partial sensing and with non-linear dynamics and observation models.
Varicol is a continuous chromatographic separation process which is based on the simulated moving bed principle with an asynchronous commutation of the inlet and outlet streams. The objective of this work is to develo...
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This paper presents an original hardware-in-the-loop (HIL) solution for real-time testing and optimization of the frequency control mechanism in autonomous microgrids (MG), when battery energy storage systems (BESS) a...
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As one of the most important parts of a modern steam power plant, proper main steam temperature control is required to maintain high efficiency, flexible load following capability and minimum thermal stresses and equi...
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ISBN:
(纸本)9781622761371
As one of the most important parts of a modern steam power plant, proper main steam temperature control is required to maintain high efficiency, flexible load following capability and minimum thermal stresses and equipment cycling to maintain high unit availability. However, most fossil powered steam plants exhibit nonlinear, long time-delays and time-varying process characteristics which impose complexity for accurate control. Compared to conventional PID controllers, the L1 adaptive controller provides a better control performance in considering these complicated conditions of the thermal plant. The "L1 adaptive controller" technique has been proved to provide fast adaptation of transient response in a large variety of systems due to its basic control architecture that the state predictor offers a model estimation of the dynamics of the system, while the adaptive law deals with the mismatch of the model. Some of the model based control methods usually need to obtain the model of the plant at the beginning and depend on the accurateness of the model, while there is no such requirement in the L1 adaptive controller, which simplifies the process of controller design. On the other hand, some model-based controllers may not work properly if the dynamics of the system changes. However, the robustness of the L1 adaptive controller guarantees that a good control performance can also be achieved in such conditions. In this paper, the L1 adaptive controller has been examined for the control of a power plant's main steam temperature. Due to its robustness and fast adaptation, the L1 adaptive controller is demonstrated to obtain a better transient performance than the PID controller. Further the adaptive controller is shown to reduce the process transients caused by disturbances of changing MW and thermal load. The simulation results for both regulation around a typical load condition and control during load changes indicate that the control effect of L1 controller is superio
In this paper, a model-based sliding mode control strategy is suggested for temperature trajectory tracking in batch processes. The convergence property as well as the stability of the sliding mode control system is g...
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In this paper, a non-conservative robust nonlinear model predictive control scheme that can guarantee satisfaction of the constraints and optimal expected performance under uncertainty is introduced. The proposed cont...
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