Qiang Ling

Robust performance of soft real-time networked control systems with data dropouts

This paper derives the power spectral density (PSD) of the output generated by a discrete-time feedback control system in which feedback measurements are dropped with a known probability, /spl epsi/. This class of systems is a model for soft real-time control systems in which the feedback path is implemented on a nondeterministic computer network. The PSD computed in this paper is a function of the dropout probability. The dropout probability is taken as a measure of the network quality of service (QoS). Based on the derived PSD, the power semi-norm of the output is predicted. So a direct way of linking control system performance(as measured by the power semi-norm of the output) to the networks QoS (as measured by the dropout probability) is provided.

Optimal dropout compensation in networked control systems

This paper examines optimal compensation for dropped feedback measurements in a networked control system. A common policy for handling such lost data is to simply use the past data. This case was treated in. This paper extends that prior work to cover a more general class of dropout compensation. The papers principal result shows that determining the optimal dropout compensator can be posed as a constrained generalized regulator problem. An example compares the performance of a networked control system using the optimal dropout compensator against more commonly used heuristic dropout policies. The comparison shows that the optimal compensator works better than these heuristic policies.

Soft real-time scheduling of networked control systems with dropouts governed by a Markov chain

This paper derives an equation for a networked control systems performance as a function of the networks dropout process. The dropout process is governed by a Markov chain. The equation computes the system output power as a function of the Markov chains transition matrix. This equation is used to pose an optimization problem whose solution yields the transition matrix the optimized closed loop output power for fixed average dropout rates.

Power spectral analysis of networked control systems with data dropouts

This note derives a closed-form expression for a control systems output power spectral density (PSD) as a function of the data dropout probability. We use the PSD to determine a dropout compensator that minimizes the regulators output power. We show, by example, that the optimal dropout compensator does not always correspond to a filter that minimizes the mean square error between the predicted and dropped feedback measurement.

Firm real-time system scheduling based on a novel QoS constraint

Many real-time systems have firm real-time requirements which allow occasional deadline violations but discard any jobs that are not finished by their deadlines. To measure the performance of such a system, a quality of service (QoS) metric is needed. Examples of often used QoS metrics for firm real-time systems are average deadline miss rates and (m, k)-firm constraints. However, for certain applications, these metrics may not be adequate measures of system performance. This paper introduces a novel QoS constraint for firm real-time systems. The new QoS constraint generalizes existing firm real-time constraints. Furthermore, using networked control system as an example, we show that this constraint can be directly related to the control systems performance. We then present three different scheduling approaches with respect to this QoS constraint. Experimental results are provided to show the effectiveness of these approaches.

A Necessary and Sufficient Feedback Dropout Condition to Stabilize Quantized Linear Control Systems With Bounded Noise

This technical note studies the almost sure input-to-state stability of quantized linear systems with bounded noise under nondeterministic feedback dropouts. It proposes a dropout condition which is both necessary and sufficient for stabilizing the quantized linear system at a finite constant bit rate. Sufficiency of that dropout condition is proven by constructing appropriate quantization policies. Note that the obtained dropout condition does not require reliable dropout acknowledgments (ACKs). Moreover, this technical note derives a lower bound on the constant bit rates under which the quantized system is stabilizable. That bound is achievable when dropout ACKs are available. When dropout ACKs are not available, the bound can be achieved in some systems. Simulations are used to verify some of the analytical results.

Control system performance under dynamic quantization: the scalar case

This paper derives an upper bound on the quantization error generated by a scalar quantized feedback control system. We assume a dynamic quantization policy in which feedback data is randomly dropped in accordance with an (m, k)-firm guarantee rule. Our main result identifies the minimum quantization level required to assure a specified signal-to-quantization ratio (SQR). We also show that these performance bounds scale in an exponential manner with k - m, thereby suggesting that real-time systems enforcing an (m, k)-firm guarantee rule should seek to keep k as small as possible.

A background modeling and foreground segmentation approach based on the feedback of moving objects in traffic surveillance systems

Background modeling and foreground segmentation are the foundation of traffic surveillance systems. The preciseness of the background model and the accuracy of the foreground segmentation will directly affect the subsequent operations, such as object detection, target classification and behavior understanding. Additionally, the processing time is limited for real applications. The background modeling and foreground segmentation approaches, unfortunately, often have to make two tough trade-offs, including the one between the robustness to background changes and the sensitivity to foreground abnormalities and the other between suppressing noise and reducing the erroneous holes and splitting in foreground segmentation. To deal with these problems, an improved background modeling and foreground segmentation approach based on the feedback of the tracking results of moving objects is proposed. According to the achieved object tracking results, a frame image is divided into four kinds of regions, then a dual-layer background updating is done for these different regions with appropriate operations, which can significantly improve the quality of the background model. Based on the spatial relationship among the tracked objects, the predicted object blocks are merged into regions, among which adaptive segmentation thresholds are used for foreground segmentation. This adaptive threshold approach can efficiently avoid the erroneous holes and splitting in foreground segmentation. Our proposed approach is validated with several public data sets, which confirm its advantages over many existing approaches.

Stability of quantized linear systems with bounded noise under dynamic bit assignment

This paper studies the stability of quantized linear control systems with external noise under the fixed-length coding policies. A vector of feedback measurements is quantized prior to being transmitted over a communication channel. This transmitted data may be dropped by the channel. This paper derives a lower bound on the number of quantization levels for closed-loop stability under fixed-length coding policies. Prior studies consider each component of the feedback vector separately, which may not achieve the above lower bound. This paper considers the feedback vector as a whole and introduces a dynamic bit assignment algorithm that is shown to achieve this lower bound.

Bit Rate Conditions to Stabilize a Continuous-Time Scalar Linear System Based on Event Triggering

This technical note is mainly concerned with bit rate conditions to stabilize a continuous-time scalar linear control system which transmits its feedback information through digital communication networks. It shows that when a packet is received, not only the bits inside that packet but also the receiving time instant of that packet carry information. The latter could carry a huge amount of information and can be obtained through simply receiving a packet without additional communication cost. Therefore it is an efficient way to encode information into the receiving time instant of a packet. The receiving time instant, however, cannot be accurately controlled by the sensor/sampler which can determine only sampling time instants. The difference between the sampling and receiving time instants of a packet is up to two delays, including the delay to process and encode the packet and the network delay to transmit that packet. These delays cannot be precisely known and have negative effects on the amount of information carried by receiving time instants. Under the boundedness assumption of these delays, this technical note provides some bit rate conditions to guarantee the systems input-to-state stability by making a good use of receiving time instants. Through event-triggering schemes, we realize these conditions which may require much lower stabilizing bit rates than periodic sampling schemes. Simulations are done to confirm the effectiveness of the obtained stabilizing bit rate conditions.