Abstract:Abstract. We consider a linear system, such as an estimator or a controller, in which several signals are transmitted over wireless communication channels. With the coding and medium access schemes of the communication system fixed, the achievable bit rates are determined by the allocation of communications resources such as transmit powers and bandwidths, to different channels. Assuming conventional uniform quantization and a standard white-noise model for quantization errors, we consider two specific problem… Show more
“…Especially, x t is not affected by the rate allocation, and the instantaneous distortion functions are separable. Hence, the major challenge lies in deriving a useful expression of the mean-squared error (MSE) for the instantaneous distortion (5). In general, it is hard to formulate closed-form expressions, even in the case of simple uniform quantizers.…”
Section: High-rate Approximation Of the Msementioning
confidence: 99%
“…Performance in Fig. 1 is measured by the distortion (5). The distortion is obtained by averaging over 50 IA's and each IA 150 000 samples.…”
Section: Numerical Experimentsmentioning
confidence: 99%
“…How to assign bits among the elements of the state vector of the plant, while imposing a constraint on the number of bits over time, can be found in e.g., [4], [5]. In these works, it has often been assumed that bits (rates) are evenly distributed to sensor measurements.…”
Optimal rate allocation in a networked control system with limited communication resources is instrumental to achieve satisfactory overall performance. In this paper, a practical rate allocation technique for state estimation in linear dynamic systems over a noisy channel is proposed. The method consists of two steps: (i) the overall distortion is expressed as a function of rates at all time instants by means of high-rate quantization theory, and (ii) a constrained optimization problem to minimize the overall distortion is solved by using Lagrange duality. Monte Carlo simulations illustrate the proposed scheme, which is shown to have good performance when compared to arbitrarily selected rate allocations.
“…Especially, x t is not affected by the rate allocation, and the instantaneous distortion functions are separable. Hence, the major challenge lies in deriving a useful expression of the mean-squared error (MSE) for the instantaneous distortion (5). In general, it is hard to formulate closed-form expressions, even in the case of simple uniform quantizers.…”
Section: High-rate Approximation Of the Msementioning
confidence: 99%
“…Performance in Fig. 1 is measured by the distortion (5). The distortion is obtained by averaging over 50 IA's and each IA 150 000 samples.…”
Section: Numerical Experimentsmentioning
confidence: 99%
“…How to assign bits among the elements of the state vector of the plant, while imposing a constraint on the number of bits over time, can be found in e.g., [4], [5]. In these works, it has often been assumed that bits (rates) are evenly distributed to sensor measurements.…”
Optimal rate allocation in a networked control system with limited communication resources is instrumental to achieve satisfactory overall performance. In this paper, a practical rate allocation technique for state estimation in linear dynamic systems over a noisy channel is proposed. The method consists of two steps: (i) the overall distortion is expressed as a function of rates at all time instants by means of high-rate quantization theory, and (ii) a constrained optimization problem to minimize the overall distortion is solved by using Lagrange duality. Monte Carlo simulations illustrate the proposed scheme, which is shown to have good performance when compared to arbitrarily selected rate allocations.
Abstract-With the advent of industrial standards such as WirelessHART, process industries are now gravitating towards wireless control systems. Due to limited bandwidth in a wireless network shared by multiple control loops, it is critical to optimize the overall control performance. In this paper, we address the scheduling-control co-design problem of determining the optimal sampling rates of feedback control loops sharing a WirelessHART network. The objective is to minimize the overall control cost while ensuring that all data flows meet their end-toend deadlines. The resulting constrained optimization based on existing delay bounds for WirelessHART networks is challenging since it is non-differentiable, non-linear, and not in closed-form. We propose four methods to solve this problem. First, we present a subgradient method for rate selection. Second, we propose a greedy heuristic that usually achieves low control cost while significantly reducing the execution time. Third, we propose a global constrained optimization algorithm using a simulated annealing (SA) based penalty method. Finally, we formulate rate selection as a differentiable convex optimization problem that provides a closed-form solution through a gradient descent method. This is based on a new delay bound that is convex and differentiable, and hence simplifies the optimization problem. We evaluate all methods through simulations based on topologies of a 74-node wireless sensor network testbed. Surprisingly, the subgradient method is disposed to incur the longest execution time as well as the highest control cost among all methods. SA and the greedy heuristic represent the opposite ends of the tradeoff between control cost and execution time, while the gradient descent method hits the balance between the two.
“…For the works that improve the NCS performance [3], [4], [5], [6], [7], it is not clear whether they can achieve stability in wireless environment. The other approach is to perform a co-design of the control layer and the communication layer (e.g., network protocols) [8], [9], [10], [11], [12]. While this approach can achieve both stability and optimal performance of NCS, its design inevitably involves too many interactions between the control and the communication layers, which prevents efficient layer abstraction and encapsulation and also hinders broader adoption.…”
Abstract-Building networked control systems over wireless networks is an extremely challenging task, as the wireless communication characteristics such as random packet losses and delay, significantly affect the stability and the performance of the control systems. We present a novel approach to the design of wireless networked control system. This approach decomposes the design concerns into two factors and addresses them separately in two design spaces -stability of the system is ensured using a passivity-based architecture at the control layer, while the performance of the system is optimized at the communication layer by adjusting the network operation parameters. This paper focuses on the design of IEEE 802.11-based wireless network. In particular, we present a MAC controller that dynamically adjusts the retransmission limit to track the optimal trade-off between packet losses and transmission delays and thus optimizes the overall control system performance. Simulation results show that our approach significantly improves the performance of the networked control systems.
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