Advanced digitally adjustable analog feedback control system and its usage in active noise insulation

This paper discusses the first step in analog (and mixed signal) abstraction utilized in large-scale Field Programmable Analog Arrays (FPAA), encoded in open-source SciLab / Xcos based toolset. Analog computation becomes relevant with the advent of FPAA devices, particularly the SoC FPAA devices and resulting design tools. Analog and digital systems have tools to model resolution and computational noise and computation energy; analog and digital approaches have their own optimal computing regions. Abstraction of Blocks in the FPAA block library makes the SoC FPAA ecosystem accessible to system-level designers while still enabling circuit designers the freedom to build at a low level. The FPAA block library provides a starting point for discussing the fundamental block concepts of analog computational approaches. These discussions begin the framework for a theory of analog algorithm complexity theory, and careful methods for comparison of techniques.

For topography measurements and faster imaging with the AFM a high control-bandwidth is required. This paper presents an analog implementation of a model-based controller for a high-speed Atomic Force Microscope (AFM) using a new type of control hardware. The vertical positioning axis of the AFM scanner is modeled, and the imaging bandwidth is improved by means of model-based control. The new feedback controller, which is designed in the H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> -framework, is implemented on a Field Programmable Analog Array (FPAA), which enables operation of the model-based controlled AFM system at a feedback bandwidth on the order of 100 kHz. Measured results demonstrate that the closed-loop system recovers from a step-like disturbance within 7 microseconds. Recorded AFM images verify a significant performance improvement of the model-based controlled system over the analog proportional-integral (PI) controlled AFM.

Discretizing the continuous-time controller of a master-slave teleoperation system can simplify control implementation. However, in teleoperation systems, discrete-time control can cause a performance degradation compared to continuous-time control. In digitally controlled bilateral teleoperation systems, there exist stability-imposed bounds on the gains of the discrete-time controller and the sampling period, and a trade-off between the two. This means that given a sampling period, it is impossible for the discretized controller to have gains above a threshold, which may well be necessary for successful performance of teleoperated tasks requiring highly accurate master-slave position tracking. In a teleoperation system with analog controller, however, stability does not impose any upper bound on the control gains, thus facilitating the performance of tasks that do require highly accurate master-slave position tracking. Inspired by this advantage of analog control, in this paper we develop a Field Programmable Analog Arrays (FPAA) based controller for bilateral teleoperation, which can achieve higher control gain than its discrete-time counterpart. We present the results of a user study measuring the human performance for a task involving flipping a stiff switch through a teleoperation system. We experimentally show that large sampling periods, necessitating low control gains for maintaining stability, lead to unacceptable task performance. We then show that humans can successfully perform the same task with the FPAA-based controller for the teleoperation system.

Analog signal processing (ASP) can be used to decrease energy consumption by several orders of magnitude over completely digital applications. Low-power field programmable analog arrays (FPAA) have been previously used by analog designers to decrease energy consumption. Combining ASP with an FPAA, energy consumption of these systems can be further reduced. For ASP to be most functional, it must achieve rail-to-rail operation to maintain a high dynamic range. This work strives to further reduce power consumption in reconfigurable analog circuitry by presenting a novel data converter that utilizes ASP and rail-to-rail operation. Rail-to-Rail operation is achieved in the data converter with the use of an operational amplifier presented in this work. This efficient yet elementary data converter has been fabricated in a 0.5$\mu$m standard CMOS process. Additionally, this work looks deeper into the challenges of students working remotely, how MATLAB can be used to create circuit design tools, and how these developmental tools can be used by circuit design students.

In some control tasks is often necessary to ask an analog or digital solutions should be used. One way or the other choice is almost always some compromise solution is reached as a result. Suppose that any embedded microcontroller, the device is already present. This extends the resources of the microcontroller in such a way that the established arrangement is suitable for both digital and analog signal processing and control functions to implement. We do this in such a way that the established system of adaptive, robust, predictive, be flexible. All this microcontroller and field programmable analog array (FPAA) is a new arrangement that has been used in the circuit from the environment can change depending on transfer characteristics of the stimulus signal.

Electrochemical micro-electro mechanical systems (MEMS) seismic sensor is limited by its nonideality of frequency dependent characteristics hence interface circuits for compensation is necessary. The conventional compensation circuits are limited by high power consumption, bulky external hardware. These digital circuits are limited by inherent analog to digital conversions which consumes significant power, acquires more size and limits processing speed. This system presents field programmable analog array (FPAA) (Anadigm AN231E04) based hardware implementation of artificial neural network (ANN) model with minimized error in frequency drift in the range of 3.68% to about 0.64% as compared to ANN simulated results in the range of 23.07% to 0.99%. Single neuron consumes power of 206.62 mW with minimum block wise resource utilization. The proposed hardware uses all analog blocks removing the requirement of analog to digital converter and digital to analog converter, reducing significant power and size of interface circuit and enhancing processing speed. It gives the reliable, SMART MEMS seismic sensor with ANN-based intelligent interface circuit implemented in FPAA hardware.

A XYZ scanner is designed for fast and high resolution atomic force microscopy. The objective of this paper is to achieve a large scanning bandwidth along the X and Y axis of the scanner. Finite element analysis of the designed scanner is reported along with the experimental determination of the dynamics. Both suggest the presence of first resonant modes around 8.8 kHz and 8.9 kHz along the X and Y axis respectively. Actuating the scanner at frequencies beyond 1% of the first resonance causes mechanical vibrations and hence degradation of the images generated. Controllers are designed, using the Integral Resonant Control methodology, to damp the resonant modes, to enable fast actuation. Due to the large bandwidth of the designed scanner, a Field Programmable Analog Array (FPAA) is used for analog implementation of the controllers. High resolution images are generated at faster scanning rates in closed loop.

The digital voltage controller of power converters is used because it has many advantages over its analog counterparts. Thus, in this paper, a controller of digital voltage mode is designed and analyzed for a non-isolated DC-DC buck converter in continuous conduction mode (CCM). First, an analog controller is designed using a Bode plot to achieve the desired stability margins in the frequency domain. Next, the analog controller is discretized using bilinear transformation with the pre-warping method. The characteristics of the discretized compensator can be maintained close to the corresponding analog compensator as long as a proper sampling time is selected. The digital control scheme is simulated with a nonlinear DC-DC buck converter model in MATLAB/Simulink to investigate the controller performance. The simulation results demonstrated the validation of the proposed digital voltage-mode control design approach. The developed digital controller eliminates DC error by tracking the reference voltage, achieving a fast transient response, maintaining specified stability margins, and effectively rejecting large disturbances.

Three-phase induction motors have been widely used in a variety of industrial applications. Induction motors have been able to incrementally improve energy efficiency to satisfy the requirements of reliability and efficiency, Melfi et al. (2009). There are well known advantages of using induction motors over permanent magnet DCmotors for position control tasks; thus, efforts aimed at improving or simplifying feedback controller design are well justified. There exists a variety of control strategies that depend on difficult to measuremotor parameters while their closed loop behavior is found to be sensitive to their variations. Even adaptive schemes tend to be sensitive to speed-estimation errors, yielding to a poor performance in the flux and torque estimation, especially during low-speed operation, Harnefors & Hinkkanen (2008). Generally speaking, the designed feedback control strategies have to exhibit a certain robustness level in order to guarantee an acceptable performance. It is possible to (on-line or off-line) obtain estimates of the motor parameters, Hasan H Toliyat et al. (2003), but some of them can be subject to variation when the system is undergoing actual operation. Frequent misbehavior is due to external and internal disturbances, such as generated heat, that significantly affect some of the system parameter values. An alternative to overcome this situation is to use robust feedback control techniques which take into account these variations as unknown disturbance inputs that need to be rejected. In this context, sliding mode techniques are a good alternative due to their disturbance rejection capability (see for instance, Utkin et al. (1999)). In this chapter, we consider a two stage control scheme, the first one is devoted to the control of the rotor shaft position. This analog control is performed bymeans of the stator current inputs, in a configuration of an observer based control. Themathematical model of the rotor dynamics is a simplified model including additive, completely unknown, lumping nonlinearities and external disturbances whose effect is to be determined in an on-line fashion by means of linear observers. The gathered knowledge will be used in the appropriate canceling of the assumed perturbations themselves while reducing the underlying control problem to a simple linear feedback control task. The control scheme thus requires a rather reduced set of parameters to be implemented. Sliding Mode Control Design for Induction Motors: An Input-Output Approach 7

7 - Analog Interfacing

In this paper, we present a mixed-technology micro-system for electronically manipulating and optically detecting virusscale particles in fluids that is designed using integrated circuit technology. During the fabrication process, the top-most chip tier is assembled upside down and the substrate material is removed. This places the polysilicon layer, which is used to create geometries with the process' minimum feature size, in close proximity to a fluid channel etched into the top of the stack. By taking advantage of these processing features inherent to 3D chip-stacking technology, we create electrode arrays that have a gap spacing of 270 nm. Using CMOS technology also provides the ability to densely integrate analog and digital control circuitry for the electrodes by using the additional levels of the chip stack. We show simulations of the system with a physical model of a Kaposi's sarcoma-associated herpes virus, which has a radius of approximately 125 nm, being dielectrophoretically arranged into striped patterns. We also discuss how these striped patterns of trapped nanometer scale particles create an effective diffraction grating which can then be sensed with macro-scale optical techniques.

Repetitive control (RC) algorithms for a plant, which contain pairs of complex conjugate poles at low frequency, resulting in a resonant system, is the subject area of this paper where the experimental results given are for a gantry robot and conveyor system in which the gantry is required to transfer payloads to a constant velocity conveyor by performing a repeating ‘pick and place’ operation. Initially, the gantry robot is controlled by means of a PID feedback controller in parallel with a proportional (P‐type) repetitive feed‐forward loop, while the conveyor operates under proportional feedback control. It is found that the RC system is unable to achieve long‐term performance. The performance degrades within a relatively small number of repetitions due to the build up of resonant frequencies in the learning loop. To prevent this, a batch aliasing technique, originally developed for iterative learning control, is modified to work in the RC framework, and is implemented in real‐time. The superior performance potential of the aliasing system is demonstrated experimentally. In the second part of this paper, multi‐machine systems, are considered where the critical new factor is the relative error between the conveyor and the robot. Here a second supervisory learning loop is proposed for use to shift the reference trajectory of one machine so that the relative placement error is also reduced. Again, supporting experimental results are given. Copyright © 2006 John Wiley &amp; Sons, Ltd.

The problem of understanding and simulating human learning and inferencing procedures is faced from a hardware point of view. A fuzzy modeling method called active learning (ALM) has been developed by the authors. ALM is an adaptive recursive algorithm which tries to express a multi-input multi-output system as a fuzzy combination of some single-input single-output systems. It uses a fuzzy curve fitting technique for behavior extraction or finding the input-output transformation of each of the single-input single-output systems. The mentioned curve fitting technique is called ink drop spread (IDS) which also serves as processing engine for the fuzzy rule of composition. In this paper first we try to develop a digital hardware for executing the IDS. Then, we show that the natural difference between the fuzzy calculating procedure of IDS and the precise nature of digital hardware appears as a serious problem for achieving very high processing speed comparable to that of the brain. As the next step in the hardware development procedure an analog merged digital hardware approach is introduced. It is shown that when preserving the necessary calculating resolution for ALM, the analog merged digital approach reduces the number of circuit elements and increases the processing speed by at least two orders of magnitude. Then it is discussed how a non-precise version of the technology for binary-multi valued-analog-merged operations called Neuron-MOSFET (neuMOS) may suit the non-precise calculating nature of IDS, which appears very promising for achieving the speed of brain learning process. Finally based on the advantages in circuit integration scale and hardware speed obtained through utilizing the binary-multi valued-analog-merged operations for the hardware implementation of IDS a primary concept for a new generation of artificial neural networks structure is developed.

This paper describes the architecture and implementations of an automatic digital tuning circuit for a complex bandpass filter (BPF) in a low-power and low-cost transceiver for applications such as personal authentication and wireless sensor network systems. The architectural design analysis demonstrates that an active RC filter in a low-IF architecture can be at least 47.7% smaller in area than a conventional gm-C filter; in addition, it features a simple implementation of an associated tuning circuit. The principle of simultaneous tuning of both the center frequency and bandwidth through calibration of a capacitor array is illustrated as based on an analysis of filter characteristics, and a scalable automatic digital tuning circuit with simple analog blocks and control logic having only 835 gates is introduced. The developed capacitor tuning technique can achieve a tuning error of less than ±3.5% and lower a peaking in the passband filter characteristics. An experimental complex BPF using 0.18µm CMOS technology can successfully reduce the tuning error from an initial value of -20% to less than ±2.5% after tuning. The filter block dimensions are 1.22mm × 1.01mm; and in measurement results of the developed complex BPF with the automatic digital tuning circuit, current consumption is 705µA and the image rejection ratio is 40.3dB. Complete evaluation of the BPF indicates that this technique can be applied to low-power, low-cost transceivers.

A current fed push pull converters cascaded with a buck pre-regulator is proposed. Voltage stress of the push pull switches is reduced by buck pre regulation. Pulse width modulation is done only to the switch in buck stage. Output voltage and the buck inductor current is controlled using average current mode control. The closed loop simulation of a 20W,100V,0.2A cascaded buck push pull converter with analog control circuit is done using PSIM. Closed loop simulation using digital control circuit is done by using a SIMCOUPLER module which couples power electronic circuit in PSIM and digital control circuit in MATLAB. Further, hardware implementation of digital controllers on two platforms is explained. Switching pulses are generated using digital circuit are fed to the converter switches and the converter is tested for the open loop operation.