Robust microscale grasping through a multimodel design: synthesis and real time implementation

This paper deals with robust force control at the microscale for safe manipulation of deformable soft materials. Since mechanical properties of micrometer sized objects are highly uncertain, instability often occurs during a gripping task. This leads to object damage or destruction due to excessive gripping force. In this paper we propose the design of a robust dynamic output feedback controller that is able to insure desired performances for a set of 65 soft and resilient microspheres whose diameter ranges from 40 μm to 80 μm and stiffness varies from 2.8 N/m to 15.7 N/m. The degrees of freedom of the controller are managed by the use of a set of elementary observers. Robustness with respect to parametric uncertainties is satisfied thanks to an iterative procedure alternating between multimodel closed loop eigenstructure assignment and worst case analysis. The developed controller is of low order and can be implemented in real time. Robust gripping force control is for the first time demonstrated experimentally when dealing with the manipulation of a large number of variable deformable soft materials at the microscale. Both simulations and experimental results validate the interest of such control design approach.     

Predictive functional control of an axis positioning system with an estimator-based internal model

This paper deals with an application of the predictive functional control with a state estimator-based internal model (PFC_ EBIM). The PFC_ EBIM has been shown to be effective in simulation. However, neither detailed experimental validation nor comparison with other controllers has been reported thus far. Here, the PFC_ EBIM is implemented in a single-axis positioning system, and a few experimental tests are conducted. Tracking performance of the PFC_ EBIM, standard PFC, and P – PI control for both smooth and non-smooth reference signals are compared. The experimental results prove the effectiveness of the PFC_ EBIM.     

On active acceleration control of vibration isolation systems

Active vibration isolation systems (VIS) have been widely used from the space shuttle applications to the ground vehicle suspensions. The main control objective is to achieve the minimum vibrations at the flotor for given vibrations at the stator. With respect to a fundamental limitation of using the PD type flotor acceleration controller, an I (integral) and II (double integral) type flotor acceleration controller is proposed in this paper. By incorporating the feedforward compensation of the umbilical dynamics, the proposed acceleration controller is able to experimentally push down the lowest isolation frequency from 1.4 Hz (when PID control is used) to 0.03 Hz with a sufficiently improved vibration isolation performance up to 10 Hz, with respect to a MIM (Microgravity Vibration Isolation Mount) system tested on the ground. A unique frequency selective filter (FSF) is also proposed, which experimentally suppresses a fixed-frequency umbilical resonant mode at 22.2 Hz with an attenuation of 20 dB.     

A moving horizon approach to input design for closed loop identification

The identification of high fidelity models is a critical element in the implementation of high performance model predictive control (MPC) applications in the industry. These controllers can vary in size with input–ouput dimensions ranging from 5 × 10 to 50 × 100. Identifying models of this scale accurately is a time consuming and demanding exercise. We present a novel approach wherein an information rich test signal is generated in closed loop by maximizing the MPC objective, as opposed to minimization that is done in the standard controller. We show that the proposed input design approach is similar to T-optimal (trace optimal) experiment design method. Our approach automatically accounts for the input and output constraints and is implemented in a moving horizon manner. It is demonstrated through simulation examples on both well and ill-conditioned processes.     

The effect of sinusoidal rolling ground motion on lifting biomechanics

The objective of this study was to quantify the effects of ground surface motion on the biomechanical responses of a person performing a lifting task. A boat motion simulator (BMS) was built to provide a sinusoidal ground motion (simultaneous vertical linear translation and a roll angular displacement) that simulates the deck motion on a small fishing boat. Sixteen participants performed lifting, lowering and static holding tasks under conditions of two levels of mass (5 and 10 kg) and five ground moving conditions. Each ground moving condition was specified by its ground angular displacement and instantaneous vertical acceleration: A): +6°, −0.54 m/s²; B): +3°, −0.27 m/s²; C): 0°, 0 m/s²; D): −3°, 0.27 m/s²; and E): −6°, 0.54 m/s². As they performed these tasks, trunk kinematics were captured using the lumbar motion monitor and trunk muscle activities were evaluated through surface electromyography. The results showed that peak sagittal plane angular acceleration was significantly higher in Condition A than in Conditions C, D and E (698°/s² vs. 612–617°/s²) while peak sagittal plane angular deceleration during lowering was significantly higher in moving conditions (conditions A and E) than in the stationary condition C (538–542°/s² vs. 487°/s²). The EMG results indicate that the boat motions tend to amplify the effects of the slant of the lifting surface and the external oblique musculature plays an important role in stabilizing the torso during these dynamic lifting tasks.     

A traffic cellular automata model based on road network grids and its spatial and temporal resolution’s influences on simulation

Microscopic Traffic Simulation Model based on Cellular Automata (CA) has become more and more popular since it was firstly introduced by Creamer and Ludwig in 1986. Cellular automata are simpler to implement on computers, provide a simple physical picture of the system and can be easily modified to deal with different aspects of traffic. However, in a traditional traffic CA model, the spatial resolution of CA and temporal resolution of simulation are low. Take TRANSIMS for example. The size of cellular automata is 7.5 m and the time step equals 1 s. In such a case, if a vehicle drives at a speed of 4 cells per s, the speed difference between 95 km/h (3.5 1 7.5 m/s) and 121 km/h (4.4999 1 7.5 m/s) will not be distinguished by simulation models. And the temporal resolution of 1 s makes the system hard to model different drivers’ reaction time, which plays a very important role in vehicular movement models. In this paper, a microscopic traffic cellular automata model based on road network grids is proposed to overcome the low spatial and temporal resolutions of traditional traffic CA models. In our model, spatial resolution can be changed by setting different grid size for lanes and intersections before or during simulation and temporal resolution can be defined according to simulation needs to model different drivers’ reaction time, whereas the vehicular movement models are still traditional CA models. By doing so, the low spatial and temporal resolution of CA model can be overcome and the advantages of using CA to simulate traffic are preserved. The paper also presents analyses of the influences on simulation of different 1D lane grid size, 2D intersection grid size and different combinations of temporal resolution and mean drivers’ reaction time. The analysis results prove the existence of spatial and temporal resolution thresholds in traffic CA models. They also reveal that the size of grids, the combinations of different temporal resolutions and mean drivers’ reaction time do pose influences on the speed of vehicles and lane/intersection occupancy, but do not affect the volume of traffic greatly.     

Electromagnetic characterization of chiral auxetic metamaterials for EMC applications

A new class of auxetic materials, a hexachiral honeycomb structure with good mechanical properties, is investigated through computer simulation and measurement. The electromagnetic properties for shielding applications are taken into account. This new material shows some interesting EMC properties (e.g. − 40 dB transmittance at 2.4 GHz) and promises better performance using different insertion techniques.     

Development of a navigation and control system for an autonomous outdoor vehicle in a steel plant

This paper describes the navigation and control system for an autonomous guided outdoor vehicle (AGV) which is used to transport heavy steel slabs in a steel plant area. The vehicle has unconventional kinematics. It has six axles all pair wise steerable by three bogie structures. The weight of the machine is 17,000 kg and its load may weigh up to 95,000 kg. The navigation is based on a fusion of dead reckoning and transponder positioning. The transponders are passive and are buried in the ground every 5–10 m along the routes of the AGV. A wireless communication has been built to connect the vehicle control system and a remote control station. This article concentrates mainly on describing the navigation system including the kinematics and position control as well as the safety system and the remote control system.     

Ultra high performance planar InGaAs PIN photodiodes for high speed optical fiber communication

This paper reports a front-illuminated planar InGaAs PIN photodiode with very low dark current, very low capacitance and very high responsivity on S-doped InP substrate. The presented device which has a thick absorption layer of 2.92 μm and a photosensitive area 73 μm in diameter exhibited the high performance of a very low capacitance of 0.47 pF, a very low dark current of 0.041 nA, a very high responsivity of 0.99 A/W (79% quantum efficiency) at λ = 1.55 μm, the 3 dB bandwidths of 6.89 GHz (−5 V), 7.48 GHz (−12 V) for bare chips and 4.48 GHz (−5 V), 5.02 GHz (−12 V) for the devices packaged in TO can, respectively. Furthermore, the developed PIN photodiodes possess high breakdown voltage of less than −25 V.     

Glucose concentration control of a fed-batch mammalian cell bioprocess using a nonlinear model predictive controller

A non-linear model predictive controller (NMPC) was investigated as a route to delivering improved product quality, batch to batch reproducibility and significant cost reductions by providing a means for better controlling the bioreactor environment in a Chinese hamster ovary (CHO) mammalian cell fed-batch process.A nonlinear fundamental bioprocess model was developed to represent the CHO mammalian cell fed-batch bioprocess under study. This developed nonlinear model aided in the configuration and tuning of a NMPC through off-line simulation. The tuned NMPC was applied to a 15 L pilot-plant bioreactor for glucose concentration fixed set-point control. Traditionally, bioprocesses are characterized by long critical process parameter (CPP) measurement intervals (24 h). However, advances in PAT have helped increase CPP measurement frequency. An in situ Kaiser RXN2 Raman spectroscopy instrument was used to monitor the glucose concentration at 6 min intervals.Glucose concentration control of a bioreactor is not a trivial task due to high process variability, measurement noise and long measurement intervals. Nevertheless, NMPC proved successful in achieving closed loop fixed set-point control in the presence of these common bioprocess operation attributes.     

An automated signalized junction controller that learns strategies by temporal difference reinforcement learning

This paper shows how temporal difference learning can be used to build a signalized junction controller that will learn its own strategies through experience. Simulation tests detailed here show that the learned strategies can have high performance. This work builds upon previous work where a neural network based junction controller that can learn strategies from a human expert was developed (Box and Waterson, 2012). In the simulations presented, vehicles are assumed to be broadcasting their position over WiFi giving the junction controller rich information. The vehicle's position data are pre-processed to describe a simplified state. The state-space is classified into regions associated with junction control decisions using a neural network. This classification is the strategy and is parametrized by the weights of the neural network. The weights can be learned either through supervised learning with a human trainer or reinforcement learning by temporal difference (TD). Tests on a model of an isolated T junction show an average delay of 14.12 s and 14.36 s respectively for the human trained and TD trained networks. Tests on a model of a pair of closely spaced junctions show 17.44 s and 20.82 s respectively. Both methods of training produced strategies that were approximately equivalent in their equitable treatment of vehicles, defined here as the variance over the journey time distributions.     

Advanced control with parameter estimation of batch transesterification reactor

The objective of this work is to enhance the economic performance of a batch transesterification reactor producing biodiesel by implementing advanced, model based control strategies. To achieve this goal, a dynamic model of the batch reactor system is first developed by considering reaction kinetics, mass balances and heat balances. The possible plant-model mismatch due to inaccurate or uncertain model parameter values can adversely affect model based control strategies. Therefore, an evolutionary algorithm to estimate the uncertain parameters is proposed. It is shown that the system is not observable with the available measurements, and hence a closed loop model predictive control cannot be implemented on a real system. Therefore, the productivity of the reactor is increased by first solving an open-loop optimal control problem. The objective function for this purpose optimizes the concentration of biodiesel, the batch time and the heating and cooling rates to the reactor. Subsequently, a closed-loop nonlinear model predictive control strategy is presented in order to take disturbances and model uncertainties into account. The controller, designed with a reduced model, tracks an offline determined set-point reactor temperature trajectory by manipulating the heating and cooling mass flows to the reactor. Several operational scenarios are simulated and the results are discussed in view of a real application. With the proposed optimization and control strategy and no parameter mismatch, a revenue of 2.76 $ min⁻¹ can be achieved from the batch reactor. Even with a minor parameter mismatch, the revenue is still 2.01 $ min⁻¹. While these values are comparable to those reported in the literature, this work also accounts for the cost of energy. Moreover, this approach results in a control strategy that can be implemented on a real system with limited online measurements.     

Robust low power DC-type shift register circuit capable of compensating threshold voltage shift of oxide TFTs

This paper proposes an oxide TFT DC-type shift register that consists of eleven TFTs and one bootstrapping capacitor. The proposed circuit connects drain nodes of large size pull-up TFTs of output drivers to positive supply voltage instead of alternating clock signals for low power consumption. In addition, a robust internal inverter capable of maintaining the high voltage level of the output over the large positive threshold voltage shift by bootstrapping is implemented. For a 120 Hz Full-HD display, the SPICE simulation estimates the clock power consumption of the proposed DC-type circuit as 0.56 mW at 32 shift registers and ensures the robust operation over the wide range of threshold voltage shift from −4 V to 10 V.     

Feedback PID-like fuzzy controller for pH regulatory control near the equivalence point

In this research the use of a feedback PID-like fuzzy controller scheme for pH control is presented to deal with instability problems near the equivalence point in neutralization processes. State space analysis of the titration curves and a fuzzy clustering algorithm based on calculating a measure of potential derived from the square distance of the pH data are complementary applied to define the membership structure and the fuzzy sets of the controller. To test the performance of the controller, both simulated and experimental runs were used. The fuzzy controller was tested for compensating step-change perturbations of propionic acidic flow rates, propionic acid concentration, and buffering conditions. Stationary cycling behavior has been observed for large loads of acidic flow rates. It was found that though the rejection time was strongly dependent on the mean residence time of the liquid solutions, the proposed controller keep the neutralization process operating close to the specified set point of pH = 7.     

High-speed and precise positioning of an X–Y table

Positioning plays an important role in manufacturing machines. This work presents a novel two-step controller strategy to achieve fast and precise positioning at the same time on an X–Y table that moves across the macro-dynamic range and settles to within the micro-dynamic resolution. The model dynamics in the macro- and the micro-region are both discussed and the controller design is separated into two parts to satisfy the requirements in each region. The switching condition of the controller is well defined. Experimental results indicate that the proposed two-step controller can move the system from the macro-region into the micro-region in 0.2 s with an accuracy of 1 μm.     

An intelligent navigator for seamless INS/GPS integrated land vehicle navigation applications

The Kalman filter (KF) has been implemented as the primary integration scheme of the global positioning system (GPS) and inertial navigation systems (INS) for many land vehicle navigation and positioning applications. However, it has been reported that KF-based techniques have certain limitations, which reflect on the position error accumulation during GPS signal outages. Therefore, this article exploits the idea of incorporating artificial neural networks to develop an alternative INS/GPS integration scheme, the intelligent navigator, for next generation land vehicle navigation and positioning applications. Real land vehicle test results demonstrated the capability of using stored navigation knowledge to provide real-time reliable positioning information for stand-alone INS-based navigation for up to 20 min with errors less than 16 m (as compared to 2.6 km in the case of the KF). For relatively short GPS outages, the KF was superior to the intelligent navigator for up to 30 s outages. In contrast, the intelligent navigator was superior to the KF when the length of GPS outages was extended to 90 s. The average improvement of the intelligent navigator reached 60% in the latter scenario. The results presented in this article strongly indicate the potential of including the intelligent navigator as the core algorithm for INS/GPS integrated land vehicle navigation systems.     

Fuzzy modeling and stable model predictive tracking control of large-scale power plants

This paper develops a stable model predictive tracking controller (SMPTC) for coordinated control of a large-scale power plant. First, a Takagi–Sugeno (TS) fuzzy model is established to approximate the behavior of the boiler–turbine coordinated control system (CCS) using fuzzy clustering and subspace identification (SID). Then, an SMPTC is designed based on the fuzzy model to track the power and pressure set-points while guaranteeing the input-to-state stability and the input constraints of the system. An output-based objective function is adopted for the proposed SMPTC so that the controller could be directly applicable for the data-driven model. Moreover, the effect of modeling mismatches and unknown plant variations has been overcome by the use of a disturbance term and steady-state target calculator (SSTC). Simulation results for a 600 MW power plant show that an off-set free tracking performance can be achieved over a wide range load variation.     

Design of embedded TCAM based longest prefix match search engine

The content addressable memory (CAM) based solutions are very useful in network applications due to its high speed parallel search mechanism. This paper presents a novel Ternary CAM (TCAM) based NAND Pseudo CMOS–Longest Prefix Match (NPC–LPM) search engine. The proposed system provides a simple hardware based solution using novel 11T TCAM cell structures and NPC word line technique, for network routers. The experiments were performed on 256 × 128 NPC–LPM system under 0.13 μm technology. The simulation result shows that the proposed design provides low power dissipation of 5.78 mW and high search speed of 315 MSearches/s under 1.3 V supply voltage. The presented NPC–LPM system meets the speed requirement of Optical Carrier (OC) 3072 with line-rate of 160 Gb/s in Ethernet networking and IPv6 protocol. The experimental results also show that the proposed system improves power-performance by 65%.     

Bivariate quality control using two-stage intelligent monitoring scheme

In manufacturing industries, it is well known that process variation is a major source of poor quality products. As such, monitoring and diagnosis of variation is essential towards continuous quality improvement. This becomes more challenging when involving two correlated variables (bivariate), whereby selection of statistical process control (SPC) scheme becomes more critical. Nevertheless, the existing traditional SPC schemes for bivariate quality control (BQC) were mainly designed for rapid detection of unnatural variation with limited capability in avoiding false alarm, that is, imbalanced monitoring performance. Another issue is the difficulty in identifying the source of unnatural variation, that is, lack of diagnosis, especially when dealing with small shifts. In this research, a scheme to address balanced monitoring and accurate diagnosis was investigated. Design consideration involved extensive simulation experiments to select input representation based on raw data and statistical features, artificial neural network recognizer design based on synergistic model, and monitoring–diagnosis approach based on two-stage technique. The study focused on bivariate process for cross correlation function, ρ = 0.1–0.9 and mean shifts, μ = ±0.75–3.00 standard deviations. The proposed two-stage intelligent monitoring scheme (2S-IMS) gave superior performance, namely, average run length, ARL₁ = 3.18–16.75 (for out-of-control process), ARL₀ = 335.01–543.93 (for in-control process) and recognition accuracy, RA = 89.5–98.5%. This scheme was validated in manufacturing of audio video device component. This research has provided a new perspective in realizing balanced monitoring and accurate diagnosis in BQC.