Repetitive processes are a distinct class of 2D linear systems with applications in areas ranging from long-wall coal cutting and metal rolling operations through to iterative learning control schemes. The main featur...
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Repetitive processes are a distinct class of 2D linear systems with applications in areas ranging from long-wall coal cutting and metal rolling operations through to iterative learning control schemes. The main feature which makes them distinct from other classes of 2D linear systems is that information propagation in one of the two independent directions only occurs over a finite duration. This, in turn, means that a distinct systems theory must be developed for them, which can then be translated into efficient routinely applicable controller design algorithms for applications domains. In this paper, we give the first significant results on a positive realness based approach to the analysis of these processes.
Using other agents' experiences and knowledge, a learning agent may learn faster, make fewer mistakes, and create some rides for unseen situations. These benefits will be gained if the learning agents know the are...
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Using other agents' experiences and knowledge, a learning agent may learn faster, make fewer mistakes, and create some rides for unseen situations. These benefits will be gained if the learning agents know the area of expertise and the expertness values of each other. In this paper, some Q-learning agents with different skills and expertness levels cooperate in learning. The agents use some criteria to judge others information and knowledge. Four expertness criterion, certainty and entropy measures are used to assign degrees of importance to others' Q-Tables. Effects of measuring these values based on their whole Q-Table, a portion of Q-Tables that reflects their proficiencies, and the states in Q-Tables on the learning quality are studied. Simple strategy sharing and two different weighted strategy-sharing methods are used to combine the acquired knowledge from different agents.
We present theoretical results on instability processes in nanoscale tunneling structures that were obtained from a computationally improved physics-based simulator. The results were obtained from a numerical implemen...
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ISBN:
(纸本)0780375386
We present theoretical results on instability processes in nanoscale tunneling structures that were obtained from a computationally improved physics-based simulator. The results were obtained from a numerical implementation of the Wigner-Poisson electron transport model upon a parallel-computing platform. These investigations considered various forms of multi-barrier resonant tunneling structures (RTSs) and they were used to test the robustness of the new modeling code. This improved modeling tool is shown to be fast and efficient with the potential to facilitate complete and rigorous studies of this time-dependent phenomenon. This is important because it will allow for the study of RTSs embedded in realistic circuit configurations. Hence, this advanced simulation tool will allow for the detailed study of RTS devices coupled to circuits where numerical simulations in time and iterative numerical optimization over the circuit parameters are required. Therefore, this work will enable the future study of RTS-based circuits operating at very high frequencies.
In multiagent reinforcement learning, inter-agent credit assignment is a fundamental problem, since a single scalar reinforcement signal is the only reliable feedback that teams of learning agents receive. This proble...
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In multiagent reinforcement learning, inter-agent credit assignment is a fundamental problem, since a single scalar reinforcement signal is the only reliable feedback that teams of learning agents receive. This problem is more critical in groups of independent learners with a joint task. In this research, it is assumed that a critic agent receives the environment feedback and assigns a proper credit to each agent using some measures. Three of such measures for a team of cooperative agents with a parallel and AND-type task are introduced. These measures somehow compare the agents' knowledge. One of these criteria, called normal expertness, is a non-relative measure while two other ones (certainty and relative normal expertness) are relative measure. It is experimentally shown that relative measures work better as they contain more information for the critic agent.
Substantial progress has been made recently towards designing, building and test-flying remotely piloted Micro Air Vehicles (MAVs) and small UAVs. We seek to complement this progress in overcoming the aerodynamic obst...
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Substantial progress has been made recently towards designing, building and test-flying remotely piloted Micro Air Vehicles (MAVs) and small UAVs. We seek to complement this progress in overcoming the aerodynamic obstacles to flight at very small scales with a vision-guided flight stability and autonomy system, based on a robust horizon detection algorithm. In this paper, we first motivate the use of computer vision for MAV autonomy, arguing that given current sensor technology, vision may be the only practical approach to the problem. We then describe our statistical vision-based horizon detection algorithm, which has been demonstrated at 30 Hz with over 99.9% correct horizon identification. Next, we develop robust schemes for the detection of extreme MAV attitudes, where no horizon is visible, and for the detection of horizon estimation errors, due to external factors such as video transmission noise. Finally, we discuss our feedback controller for self-stabilized flight, and report results on vision-based autonomous flights of duration exceeding ten minutes.
A MIMO (multiple-input, multiple-output) analog to the well-known Lyapunov-based SISO (single-input, single-output) design of MRAC (model-reference adaptive control) has been recently introduced by L. Hsu et al. (2001...
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A MIMO (multiple-input, multiple-output) analog to the well-known Lyapunov-based SISO (single-input, single-output) design of MRAC (model-reference adaptive control) has been recently introduced by L. Hsu et al. (2001). The new design utilizes a control parametrization derived from a factorization of the high-frequency gain matrix K/sub p/=SDU, where S is symmetric positive-definite, D is diagonal and U is unity upper-triangular. Only the signs of the entries of D or, equivalently, the signs of the leading principal minors of K/sub p/, were assumed to be known. However, the result was restricted to plants with (vector) relative degree one. In this paper, we extend the MRAC for more general plants with relative degree greater than one. We present three possible factorizations of K/sub p/ and the resulting update laws.
The design of model-reference adaptive control (MRAC) for MIMO linear systems has not yet been achieved, in spite of significant efforts, the completeness and simplicity of its SISO counterpart. One of the main obstac...
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The design of model-reference adaptive control (MRAC) for MIMO linear systems has not yet been achieved, in spite of significant efforts, the completeness and simplicity of its SISO counterpart. One of the main obstacles has been the generalization of the SISO assumption that the sign of the high-frequency gain is known. Here we overcome this obstacle and present a MIMO analog to the well known Lyapunov-based MRAC SISO design. Our algorithm makes use of a new control parametrization derived from a factorization of the high-frequency gain matrix K/sub p/=SDU, where S is symmetric positive definite, D is diagonal, and U is unity upper triangular. Only the signs of the entries of D or, equivalently, the signs of the leading principal minors of K/sub p/, are assumed to be known.
In the design of model predictive controller (MPC), the traditional approach of expanding the projected control signal uses the forward operator to obtain the linear-in-the-parameters relation for predicted output. In...
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In the design of model predictive controller (MPC), the traditional approach of expanding the projected control signal uses the forward operator to obtain the linear-in-the-parameters relation for predicted output. In this paper, by using a performance specification on the exponential change rate of the projected control signal, a more appropriate expansion, related to Laguerre networks, is introduced. It is shown that the number of terms used in the optimization procedure can be reduced to a fraction of that required by the usual procedure in the case of rapid sampling, and as a result, the numerical condition of the optimization algorithm can be significantly improved. Furthermore, the proposed algorithm is easy to tune for closed-loop performance with two explicit tuning parameters. By relaxing the constraint on the exponential change rate of the control signal and allowing arbitrary complexity in describing the trajectory, the proposed approach becomes equivalent to the traditional approach in MPC design.
Delay in the computation of the signal-to-interference ratio in communication systems is unavoidable. In the the case of mobile communication, delay is a very critical problem due the fast variation of the communicati...
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Delay in the computation of the signal-to-interference ratio in communication systems is unavoidable. In the the case of mobile communication, delay is a very critical problem due the fast variation of the communication channel and the need for effective, fast and accurate power control. In this paper we present our approach to dealing with the delay in mobile communication systems as well as our controller to achieve and maintain the desired signal quality. We will concentrate on the code division multiple access (CDMA) systems.
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