J. B. Lewis

Bio: J. B. Lewis is an academic researcher from Massachusetts Institute of Technology. The author has an hindex of 1, co-authored 1 publications receiving 38 citations.

Optimum Sampled-Data Systems with Quantized Control Signals

Abstract: Optimum design of sampleddata systems with quantized control signals is considered. The control signals m i (k) are members of a set [- CL , ... - C2 , - C1 , 0, C 1 , C 2 , ... C L ] where c i ≫ 0, i= 1, 2,... L, and C k ≫C j for k≫j. By selecting a proper set [c i ], a design compromise between the extremes of proportional control systems and relay control systems is achieved. Dynamic programming techniques are used in carrying out the optimum design. Both minimum summed-square-error systems and minimum -N systems are discussed and illustrated for dynamic performance.

Adaptive prediction in speech differential encoding systems

Abstract: The design of speech coders that produce high-quality highly intelligible speech at 6 to 16 kb/s while retaining robustness to background and transmission impairments is an area of current research interest Differential encoding structures employing adaptive quantization and adaptive prediction constitute one of the most promising approaches to achieving these design objectives This paper focuses on the design and analysis of adaptive predictors for differential encoders Several differential encoding systems, including adaptive predictive coding, differential pulse-code modulation, noise feedback coding, direct feedback coding, and prediction error coding, are described and related Adaptive quantizers are briefly discussed and quantitative and qualitative indicators of speech coder performance are defined The channel model, the speech model, and the research problem statements used in the design of differential encoders and adaptive predictors are presented The nomenclature and theory of forward and backward adaptive prediction are developed, and several new backward adaptive algorithms based on various assumptions are presented A detailed survey of theoretical and simulation results on adaptive prediction for speech differential encoders is given, and the effects of background and transmission impairments on these systems are discussed, Finally, the impact of adaptive predictors on rate distortion theory motivated coders is indicated Numerous areas for future research are highlighted

Control and estimation under information constraints: Toward a unified theory of control, computation and communications

Abstract: An attempt is made to overview an emerging area of research devoted to analysis and design of control systems under constraints caused by limited information capacity of communication channels. The problem's prehistory dating back to the 1960s---1970s, as well as the new approaches that appeared during the last decade were analyzed. Much attention was paid to various versions of the celebrated data rate theorem. Consideration was given to the problems of control through the communication networks and some results obtained for the nonlinear systems. The basic application areas were listed in brief.

Optimum quantization in dynamic systems

Abstract: In this paper the problem of optimally designing a quantizer imbedded in a closed-loop dynamic system is considered. The criterion for the design is that the overall system performance as expressed by a variational criterion is optimized. The function of quantization is thus related to the functions of control and estimation that are performed in the system. First, a procedure is described for optimally designing a quantizer in a static open-loop system, where the design criterion is the expected value of a function of the instantaneous error between the input and output of the quantizer. This procedure reduces the search over all quantizer parameters to an iterative search over a single parameter. Next, the existing methods for finding the optimal design of a quantizer imbedded in a dynamic system are reviewed. The most general method found in the literature involves a combination of dynamic programming with an exhaustive search for all quantizer parameters. The computational requirements of this procedure are quite large even for low-order systems with few quantizer parameters. Finally, a new result is presented that leads to greatly reduced computational requirements for the dynamic system case. It is shown that under certain conditions an overall optimum system design is obtained by first optimizing the system with all quantizers removed and then applying the procedure for the static open-loop case mentioned above. This result is analogous to the separation of the functions of estimation and control that occurs under similar conditions. The computational savings over the existing procedures are very extensive, and the new procedure is computationally feasible for a large class of practical systems.

Iterative Encoder-Controller Design for Feedback Control Over Noisy Channels

Abstract: We study a closed-loop control system with state feedback transmitted over a noisy discrete memoryless channel. With the objective to minimize the expected linear quadratic cost over a finite horizon, we propose a joint design of the sensor measurement quantization, channel error protection, and controller actuation. It is argued that despite that this encoder-controller optimization problem is known to be hard in general, an iterative design procedure can be derived in which the controller is optimized for a fixed encoder, then the encoder is optimized for a fixed controller, etc. Several properties of such a scheme are discussed. For a fixed encoder, we study how to optimize the controller given that full or partial side-information is available at the encoder about the symbols received at the controller. It is shown that the certainty equivalence controller is optimal when the encoder is optimal and has full side-information. For a fixed controller, expressions for the optimal encoder are given and implications are discussed for the special cases when process, sensor, or channel noise is not present. Numerical experiments are carried out to demonstrate the performance obtained by employing the proposed iterative design procedure and to compare it with other relevant schemes.