http://nova.newcastle.edu.au/vital/access/services/Feed ${session.getAttribute("locale")} 5 http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:10675 In this article, we consider remote-controlled systems, where the command generator and the controlled object are connected with a bandwidth-limited communication link. In the remote-controlled systems, efficient representation of control commands is one of the crucial issues because of the bandwidth limitations of the link. We propose a new representation method for control commands based on compressed sensing. In the proposed method, compressed sensing reduces the number of bits in each control signal by representing it as a sparse vector. The compressed sensing problem is solved by an ℓ1-ℓ2 optimization, which can be effectively implemented with an iterative shrinkage algorithm. A design example also shows the effectiveness of the proposed method. 2013-03-24T04:52:39.481Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:9215 We investigate a networked control architecture for LTI plant models with a scalar input. Communication from controller to actuator is over an unreliable network which introduces packet dropouts. To achieve robustness against dropouts, we adopt a packetized predictive control paradigm wherein each control packet transmitted contains tentative future plant input values. The novelty of our approach is that we seek that the control packets transmitted be sparse. For that purpose, we adapt tools from the area of compressed sensing and propose to design the control packets via on-line minimization of a suitable ℓ¹/ℓ² cost function. We then show how to choose parameters of the cost function to ensure that the resultant closed loop system be practically stable, provided the maximum number of consecutive packet dropouts is bounded. A numerical example illustrates that sparsity reduces bit-rates, thereby making our proposal suited to control over unreliable and bit-rate limited networks. 2013-03-24T04:52:05.409Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:11734 We investigate the use of compressive sampling for networked feedback control systems. The method proposed serves to compress the control vectors which are transmitted through rate-limited channels without much deterioration of control performance. The control vectors are obtained by an ℓ¹-ℓ² optimization, which can be solved very efficiently by FISTA (Fast Iterative Shrinkage-Thresholding Algorithm). Simulation results show that the proposed sparsity-promoting control scheme gives a better control performance than a conventional energy-limiting L²-optimal control. 2013-03-17T23:54:47.773Z ]]>