A neural control strategy for nonlinear processes with time-variant time-delay is proposed in this paper. In this strategy, a dynamic neural network based nonlinear Smith predictor is constructed to compensate for the...
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This paper considers the use of constrained minimum crest factor multisine signals as inputs for plant-friendly identification testing of chemical process systems. The approach developed in this paper greatly increase...
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This paper considers the use of constrained minimum crest factor multisine signals as inputs for plant-friendly identification testing of chemical process systems. The approach developed in this paper greatly increases their effectiveness in a process control setting by enabling the user to simultaneously specify important frequency and time-domain characteristics of these signals. Two problem formulations meaningful to both linear and nonlinear identification problems are presented. State-of-the-art computational methods are needed to solve the challenging optimization problems associated with crest factor minimization.
This paper presents a new approach towards the integration of process design and control. The approach compares alternative process designs based on the optimal closed-loop performance in the presence of stochastic di...
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Different types of nonlinear controllers are designed and compared for a simple continuous bioreactor operating near optimal productivity. This operating point is located close to a fold bifurcation point. Nonlinear a...
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This paper proposes a novel energy-based control law for biped robots based on an analysis of passive dynamic walking. Firstly we discuss the essence of dynamic walking using a passive walker on a gentle slope. In the...
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This paper proposes a novel energy-based control law for biped robots based on an analysis of passive dynamic walking. Firstly we discuss the essence of dynamic walking using a passive walker on a gentle slope. In the second, we propose a simple and effective control law which imitates the energy behavior in every cycle considering the ZMP condition and other factors of the active walker. The control strategy is formed by the feature of mechanical energy dissipation and restoration. By the effect of the proposed method, the robot can exhibit natural and reasonable walk on a level ground without any gait design in advance. The validity of the proposed method is examined by numerical simulations and experiments.
A neural control strategy for nonlinear processes with time-variant time-delay is proposed in this paper. In this strategy, a dynamic neural network based nonlinear Smith predictor is constructed to compensate for the...
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A neural control strategy for nonlinear processes with time-variant time-delay is proposed in this paper. In this strategy, a dynamic neural network based nonlinear Smith predictor is constructed to compensate for the effect of time-delay of a class of nonlinear processes. An on-line optimizing controller is illustrated based on the neural Smith predictor. It is known that the performance of the Smith predictor may be deteriorated if the time-delay of the process changes with time. In order to improve the performance of the Smith predictor, a time-delay adaptation mechanism is introduced into the control structure to track the variation of the time-delay. The simulation, comparing with the classical Smith predictive control, on a continuous-stirred-tank-reactor (CSTR), where the time-delay of the manipulating flow changes with time, is used for the test.
The estimation for the nonlinear dynamic system with time-varying input time-delay is an important issue for system identification. In order to estimate the dynamics of the process, a dynamic neural network with exter...
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Model Predictive control (MPC) is presented as a robust, flexible decision framework for dynamically managing inventories and meeting customer demand in demand networks (a.k.a. supply chains). Ultimately, required saf...
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In this paper model reduction methods are used to obtain a nonlinear process model for designing a model predictive controller (MPC). The corresponding controller and its closed-loop response is then compared with con...
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Model Predictive control (MPC) is presented as a robust, flexible decision framework for dynamically managing inventories and meeting customer demand in demand networks (a.k.a. supply chains). Ultimately, required saf...
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Model Predictive control (MPC) is presented as a robust, flexible decision framework for dynamically managing inventories and meeting customer demand in demand networks (a.k.a. supply chains). Ultimately, required safety stock levels in demand networks can be significantly reduced as a result of the performance demonstrated by the MPC approach. The translation of available information in the supply chain problem into MPC variables is demonstrated with a two-node supply chain example. A six-node, two-product, three-echelon demand network problem proposed by Intel is well managed by a partially decentralized MPC implementation under simultaneous demand forecast inaccuracies and plant-model mismatch.
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