Motivated by the need for efficient control design, in this paper we consider the averaging of dynamics for a tail-actuated robotic fish, based on an experimentally validated dynamic model that incorporates rigid body...
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ISBN:
(纸本)9781479901777
Motivated by the need for efficient control design, in this paper we consider the averaging of dynamics for a tail-actuated robotic fish, based on an experimentally validated dynamic model that incorporates rigid body dynamics and Lighthill's large-amplitude elongated-body theory. We first show that classical averaging theory fails in this case because of the relatively large oscillatory input in the driving terms. On the other hand, while the first-order geometric averaging method for systems with highly oscillatory inputs is able to capture the original time-dependent dynamics, the resulting average model is overly complex for controller design. We propose a novel control-oriented, data-driven averaging approach for robotic fish dynamics, where a scaling function is introduced on top of the classical averaging method. We run extensive simulations for different combinations of tail-beat bias, amplitude, and frequency, and find that the scaling function is constant for the force equations and varies linearly with the tail-beat bias for the moment equation. The validity of the resulting average model has been confirmed in simulation results for open-loop dynamics with new sets of tail-beat parameters, and for closed-loop dynamics when proportional control of the tail-beat bias is used in target tracking.
This paper concerns application of data-derived approaches for analyzing and monitoring chemical process instruments, extracting product information, and designing estimation models for primary process variables, or d...
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ISBN:
(纸本)9781467357661
This paper concerns application of data-derived approaches for analyzing and monitoring chemical process instruments, extracting product information, and designing estimation models for primary process variables, or difficult to measure in real-time variables. Modeling of process with an optimized classical neural network, the multi-layer perceptron (MLP) is discussed. Tennessee Eastman Process, a well-known plant wide process benchmark, is applied to validate the proposed approach. Investigations and several algorithms as step response test, Lipschitz number method and forward selection are used. The main advancement introduced here is that a hierarchical level responsible strategy is applied for selection of input variables and respective efficient time delays to attain the highest possible prediction accuracy of the neural network model for industrial process identification.
A new likelihood-based stochastic knock controller, that achieves a significantly improved regulatory response relative to conventional strategies, while also maintaining a rapid transient response is presented. Up un...
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ISBN:
(纸本)9781467357159
A new likelihood-based stochastic knock controller, that achieves a significantly improved regulatory response relative to conventional strategies, while also maintaining a rapid transient response is presented. Up until now it has only been evaluated using simulations and the main contribution here is the implementation and validation of the knock controller on a five cylinder engine with variable compression ratio. Furthermore, an extension of the fast response strategy and a re-tuning of the controller is shown to improve performance. The controller is validated with respect to its robustness to changes in engine operating condition as well as compression ratio. The likelihood-based controller is demonstrated in engine tests and compared to a conventional controller and it is shown that it is able to operate closer to the knock limit with less variations in control action without increasing the risk of engine damage.
In this paper we propose a hybrid system that integrates reinforcement learning and flocking control in order to create an adaptive and intelligent multi-robot system. First, we present a flocking control algorithm th...
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In this paper we propose a hybrid system that integrates reinforcement learning and flocking control in order to create an adaptive and intelligent multi-robot system. First, we present a flocking control algorithm that allows multiple mobile robots to move together while avoiding obstacles. Second, we propose a distributed cooperative learning algorithm that can quickly enable the mobile robot network to avoid predator/enemy while maintaining the network connectivity and topology. The convergence of the cooperative learning algorithm is discussed. As a result, the hybrid system of flocking control and cooperative reinforcement learning results in an efficient integration of high level behaviors (discrete states and actions) and low level behaviors (continuous states and actions) for multi-robot cooperation. The simulations are performed to demonstrate the effectiveness of the proposed system.
The framework is designed to provide a simple and powerful way of Web application component development. Components of the framework can nest, and the framework is designed to be easy to reuse as a component, thus pro...
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In this paper design and control of planar cable-driven parallel robots are studied in an experimental prospective. Since in this class of manipulators, cable tensionability conditions must be met, feedback control of...
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In this paper design and control of planar cable-driven parallel robots are studied in an experimental prospective. Since in this class of manipulators, cable tensionability conditions must be met, feedback control of such robots becomes more challenging than for conventional robots. To meet these conditions, internal force control structure is introduced and used in addition to a PID control scheme to ensure that all cables remain in tension. A robust PID controller is proposed for partial knowledge of the robot, to keep the tracking errors bounded. Finally, the effectiveness of the proposed control algorithm is examined through experiments on K.N. Toosi planar cable-driven robot and it is shown that the proposed control structure is able to provide suitable performance in practice.
This paper deals with exponential stability of some classes of integral delay systems with a prescribed decay rate. By carefully exploring the literature on this topic, a delay decomposition approach is established to...
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ISBN:
(纸本)9781479901777
This paper deals with exponential stability of some classes of integral delay systems with a prescribed decay rate. By carefully exploring the literature on this topic, a delay decomposition approach is established to reduce the conservatism in the existing sufficient conditions by constructing new Lyapunov-Krasovskii functionals. It is proven that the proposed sufficient conditions are always less conservative than the existing ones. Numerical examples illustrate the effectiveness of the proposed approaches.
We study in this paper the consensus problem for multi-agent systems with agents characterized by high-order linear systems with time delays in both the communication network and inputs. Provided that the open-loop dy...
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ISBN:
(纸本)9781479901777
We study in this paper the consensus problem for multi-agent systems with agents characterized by high-order linear systems with time delays in both the communication network and inputs. Provided that the open-loop dynamics of the agents is not exponentially unstable, but may be polynomially unstable, and the communication topology contains a directed spanning tree, a truncated predictor feedback approach is established to solve the consensus problem. It is shown that, if the delays are constant and exactly known, the consensus problem can be solved by state feedback protocols for arbitrarily large bounded delays. If it is further assumed that the open-loop dynamics of the agents only contains zero eigenvalues, the delays are allowed to be time-varying and unknown. Numerical examples are worked out to illustrate the effectiveness of the proposed approaches.
We analyze real-life implementations of measurement-device-independent quantum-key-distribution (MDI-QKD): a general system model, a finite-decoy protocol and a finite-key analysis. Our work is relevant to not only QK...
Scalar field mapping has many applications including environmental monitoring, search and rescue, etc. In such applications there is a need to achieve a certain level of confidence regarding the estimates at each loca...
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ISBN:
(纸本)9781479915132
Scalar field mapping has many applications including environmental monitoring, search and rescue, etc. In such applications there is a need to achieve a certain level of confidence regarding the estimates at each location. In this paper, a cooperative and active sensing framework is developed to enable scalar field mapping using multiple mobile sensor nodes. The cooperative and active controller is designed via the real-time feedback of the sensing performance to steer the mobile sensors to new locations in order to improve the sensing quality. During the movement of the mobile sensors, the measurements from each sensor node and its neighbors are taken and fused with the corresponding confidences using distributed consensus filters. As a result an online map of the scalar field is built with a certain level of confidence of the estimates. We conducted computer simulations to validate and evaluate our proposed algorithms
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