Multi-input Multi-output Model Research Articles

Spiral Scanning With Improved Control for Faster Imaging of AFM

One of the key barriers to an atomic force microscope (AFM) achieving high scanning speeds is its use of the traditional zig-zag raster pattern scanning technique. In this paper, we consider the use of a high-speed spiral imaging technique with an improved multi-input multi-output (MIMO) model predictive control (MPC) scheme with a damping compensator for faster scanning by an AFM. The controller's design is based on an identified MIMO model of the AFM's piezoelectric tube scanner (PTS) and it achieves a higher closed-loop bandwidth, significant damping of the resonant mode of the PTS, and reduces the cross-coupling effect between the PTS's axes. The spirals produced have particularly narrow-band frequency measures which change slowly over time, thereby making it possible for the scanner to achieve improved tracking and continuous high-speed scanning rather than being restricted to the back and forth motion of raster scanning. To evaluate the performance improvement using this proposed control scheme for spiral scanning, an experimental comparison of its scanned images with those of the open-loop condition is performed. Experimental results show that, by using the proposed method, the AFM's scanning speed is significantly increased up to 180 Hz.

A real-time nonlinear decentralized control of multimachine power systems

This paper proposes the application of a new nonlinear decentralized control to the multi-input multi-output model of multimachine power systems. The goal is to control the terminal voltage as well as the rotor angle to obtain high-performance transient stability and good post-fault voltage regulation. The proposed stabilizing feedback laws for the power system are shown to be globally asymptotically stable in the context of Lyapunov theory. The effectiveness of the proposed control action is demonstrated through some computer simulations on a two-area benchmark power system under a large sudden fault and a wide range of operating conditions. The results are compared with those of the traditional automatic voltage regulator (IEEE type I AVR) plus the power system stabilizer control structure and speed governor.

Control of Multiple Remote Servers for Quality-Fair Delivery of Multimedia Contents

This paper proposes a control scheme for the quality-fair delivery of several encoded video streams to mobile users sharing a common wireless resource. Video quality fairness, as well as similar delivery delays are targeted among streams. The proposed controller is implemented within some aggregator located near the bottleneck of the network. The transmission rate among streams is adapted based on the quality of the already encoded and buffered packets in the aggregator. Encoding rate targets are evaluated by the aggregator and fed back to each remote video server (fully centralized solution), or directly evaluated by each server in a distributed way (partially distributed solution). Each encoding rate target is adjusted for each stream independently based on the corresponding buffer level or buffering delay in the aggregator. Communication delays between the servers and the aggregator are taken into account. The transmission and encoding rate control problems are studied with a control-theoretic perspective. The system is described with a multi-input multi-output model. Proportional Integral (PI) controllers are used to adjust the video quality and control the aggregator buffer levels. The system equilibrium and stability properties are studied. This provides guidelines for choosing the parameters of the PI controllers. Experimental results show the convergence of the proposed control system and demonstrate the improvement in video quality fairness compared to a classical transmission rate fair streaming solution and to a utility max-min fair approach.

Ecological-economic assessment of farms using multi-input multi-output models: life cycle assessment with multiple paired comparisons

A multi-input multi-output model is developed by extending the life cycle assessment framework for analysing the relationship between agricultural production and environmental impacts. The inputs include farmland and materials such as fertilisers, pesticides and animals. The outputs are of two types: one is agro-economic production, such as crop yields, and the other is environmental impacts, including greenhouse gas emissions. Additive and ratio models are defined for analysing the relationship between management intensity, land productivity and environmental impacts based on the farm model. After the framework of multiple paired comparisons is illustrated, the multi-input multi-output model is applied to rice farming in Japan. The results indicate that the additive and ratio models can be used for detecting the directions of changes. These models can be extended for analysing the land-use competition between food and energy production.

A Review of Inverse Simulation Methods and Their Application

Inverse simulation allows system inputs to be found to give model outputs that match required time histories. Examples include inputs needed for specific aircraft manoeuvres or movements of a robot arm and such cases lead to investigations that differ from conventional simulation studies. Inverse simulation has been used for aircraft and helicopter flight mechanics modelling for some time and provides alternatives to analytical methods of inverse modelling used in control applications. The approach is suitable for linear and nonlinear multi-input multi-output models as well as for single-input single-output descriptions. Methods include a so-called “differentiation” approach, techniques that are termed “integration” methods, approaches involving the solution of differential algebraic equations and a method based on feedback system principles. The feedback approach is given particular emphasis, through an example involving a dynamic model of an unmanned underwater vehicle.

Multivariable Adaptive Controller for the Nonlinear MIMO Model of a Container Ship

The paper presents an adaptive multivariable control system for a Multi-Input, Multi-Output (MIMO) nonlinear dynamic process. The problems under study are exemplified by a synthesis of a course angle and forward speed control system for the nonlinear four-Degrees-of-Freedom (4-DoF) mathematical model of a single-screw, high-speed container ship. The paper presents the complexity of the assumed model to be analyzed and a synthesis method for the multivariable adaptive modal controller. Due to a strongly nonlinear nature of the ship movements equations a multivariable adaptive controller is tuned in relation to changeable hydrodynamic operating conditions of the ship. In accordance with the given operating conditions controller parameters are chosen on the basis of four measured auxiliary signals. The system synthesis is carried out by linearization of the nonlinear model of the ship at its nominal operating points in the steady-state and by means of a pole placement control method. The final part of the paper includes results of simulation tests of the proposed control system carried out in the MATLAB/Simulink environment along with conclusions and final remarks.

Facilitation of memory encoding in primate hippocampus by a neuroprosthesis that promotes task-specific neural firing

Objective. Memory accuracy is a major problem in human disease and is the primary factor that defines Alzheimer’s, ageing and dementia resulting from impaired hippocampal function in the medial temporal lobe. Development of a hippocampal memory neuroprosthesis that facilitates normal memory encoding in nonhuman primates (NHPs) could provide the basis for improving memory in human disease states. Approach. NHPs trained to perform a short-term delayed match-to-sample (DMS) memory task were examined with multi-neuron recordings from synaptically connected hippocampal cell fields, CA1 and CA3. Recordings were analyzed utilizing a previously developed nonlinear multi-input multi-output (MIMO) neuroprosthetic model, capable of extracting CA3-to-CA1 spatiotemporal firing patterns during DMS performance. Main results. The MIMO model verified that specific CA3-to-CA1 firing patterns were critical for the successful encoding of sample phase information on more difficult DMS trials. This was validated by the delivery of successful MIMO-derived encoding patterns via electrical stimulation to the same CA1 recording locations during the sample phase which facilitated task performance in the subsequent, delayed match phase, on difficult trials that required more precise encoding of sample information. Significance. These findings provide the first successful application of a neuroprosthesis designed to enhance and/or repair memory encoding in primate brain.

Parameter Estimation of Reverse Osmosis Process Model for Desalination

The present work pertains to modelling and identification of seawater desalination system using reverse osmosis. Initially the manipulated variable (feed pressure and recycle ratio) and the measured variables (flowrate, concentration and pH of permeate) are identified from reverse osmosis desalination system. The model of reverse osmosis was developed from the first principle approach using the mass balance equation (taking into consideration effect of concentration polarisation) from which the transfer function model was developed. The parameters of multi-input multi-output model are identified using the autoregressive exogenous linear identification technique. The states of the process model were also estimated using Kalman filter and parameters are identified by nonlinear least square (NNLS) algorithm. The plant’s data of spiral wound model are given as input to all the identification methods. The results obtained from the predicted and the linear models are in good agreement with these obtained for the same plant data.

A fast and robust model selection algorithm for multi-input multi-output support vector machine

Multi-Input Multi-Output (MIMO) regression estimation problems widely exist in engineering fields. As an efficient approach for MIMO modeling, multi-dimensional support vector regression, named M-SVR, is generally capable of obtaining better predictions than many traditional methods. However, M-SVR is sensitive to the perturbation of hyper-parameters when facing small-scale sample problems, and most of currently used model selection methods for conventional SVR cannot be applied to M-SVR directly due to its special structure. In this paper, a fast and robust model selection algorithm for M-SVR is proposed. Firstly, a new training algorithm for M-SVR is proposed to reduce efficiently the numerical errors in training procedure. Based on this algorithm, a new leave-one-out (LOO) error estimate for M-SVR is derived through a virtual LOO cross-validation procedure. This LOO error estimate can be straightway calculated once a training process ended with less computational complexity than traditional LOO method. Furthermore, a robust implementation of this LOO estimate via Cholesky factorization is also proposed. Finally, the gradients of the LOO estimate are calculated, and the hyper-parameters with lowest LOO error can be found by means of gradient decent method. Experiments on toy data and real-life dynamical load identification problems are both conducted, demonstrating comparable results of the proposed algorithm in terms of generalization performance, numerical stability and computational cost.

A Multivariable Design Methodology for Voltage Control of a Single-DG-Unit Microgrid

This paper proposes a multivariable digital control design methodology for the voltage regulation of an islanded single distributed generation (DG) unit microgrid and its dedicated load. The controller design methodology is based on a family of spectral Multi-Input Multi-Output (MIMO) models of the microgrid system and performs open-loop shaping and system decoupling simultaneously by a convex optimization approach. The control design procedure includes: (i) the determination of a family of nonparametric models of the system at various operating points, (ii) the determination of the class of the controller, and (iii) system open-loop shaping by convex minimization of the summation of the square second norm of the errors between the system open-loop transfer functions and a desired open-loop transfer function. Based on the proposed design methodology, two dq -augmented voltage controllers are proposed to regulate the load voltages of a single-DG-unit microgrid. The proposed controllers guarantee the robust stability and satisfactory dynamic response of the system in spite of load parametric uncertainties and also the presence of nonlinear load. This paper describes the theoretical aspects involved in the design procedure of the controllers and evaluates the performance of the controllers based on simulation studies and experiments.

Linear Multi-controller Structure for Control of a Nonlinear MIMO Model of a Drill Ship

In the paper a linear control system structure with modal controllers for multi-input, multioutput (MIMO) nonlinear dynamic processes is presented. The problems under study are exemplified by synthesis of a system for control a position and yaw angle of drillship described by a 3DOF nonlinear mathematical model of its low-frequency (LF) motions over the drilling point. The equations of the ship motions include the impact of the sea current and wind forces on the ship's hull. In the proposed control system structure a set of (stable) linear modal controllers is used that create an adaptive linear controller with variable parameters tuned appropriately to operation conditions chosen on the basis of two measured auxiliary signals. These are: the ship's current forward speed measured with respect to the water and systematically calculated the difference between the yaw angle and the sea current angle. The system synthesis is carried out by means of Diophantine polynomial matrix equations method for system pole placement after having linearized model of low-frequency motions made by the vessel at its nominal “operating points” in steady states. Furthermore, the control system has been extended by an additional integrating path to compensate influences of environmental disturbances. The final part of the paper includes simulation results of system operation with an adaptive controller of (stepwise) varying parameters along with conclusions and final remarks.

Optimal space–time precoding of artificial sensory feedback through mutichannel microstimulation in bi-directional brain–machine interfaces

Brain–machine interfaces (BMIs) aim to restore lost sensorimotor and cognitive function in subjects with severe neurological deficits. In particular, lost somatosensory function may be restored by artificially evoking patterns of neural activity through microstimulation to induce perception of tactile and proprioceptive feedback to the brain about the state of the limb. Despite an early proof of concept that subjects could learn to discriminate a limited vocabulary of intracortical microstimulation (ICMS) patterns that instruct the subject about the state of the limb, the dynamics of a moving limb are unlikely to be perceived by an arbitrarily-selected, discrete set of static microstimulation patterns, raising questions about the generalization and the scalability of this approach. In this work, we propose a microstimulation protocol intended to activate optimally the ascending somatosensory pathway. The optimization is achieved through a space–time precoder that maximizes the mutual information between the sensory feedback indicating the limb state and the cortical neural response evoked by thalamic microstimulation. Using a simplified multi-input multi-output model of the thalamocortical pathway, we show that this optimal precoder can deliver information more efficiently in the presence of noise compared to suboptimal precoders that do not account for the afferent pathway structure and/or cortical states. These results are expected to enhance the way microstimulation is used to induce somatosensory perception during sensorimotor control of artificial devices or paralyzed limbs.

Minimization of cross-talk in a piezo inkjet printhead based on system identification and feedforward control

The printing quality delivered by a drop-on-demand inkjet printhead is severely affected by the residual oscillations in an ink channel and the cross-talk between neighboring ink channels. For a single ink channel, our earlier contribution shows that the actuation pulse can be designed, using a physical model, to effectively damp the residual oscillations. It is not always possible to obtain a good physical model for a single ink channel. A physical model for a multi-input multi-output (MIMO) inkjet printhead is made even more sophisticated by the presence of the cross-talk effect. This paper proposes a system identification-based approach to build a MIMO model for an inkjet printhead. Additionally, the identified MIMO model is used to design new actuation pulses to effectively minimize the residual oscillations and the cross-talk. Using simulation and experimental results, we demonstrate the efficacy of the proposed method.

Linear and Non-linear Multi-Input Multi-Output Model Predictive Control of Continuous Stirred Tank Reactor

In this article, multi-input multi-output (MIMO) linear model predictive controller (LMPC) based on state space model and nonlinear model predictive controller based on neural network (NNMPC) are applied on a continuous stirred tank reactor (CSTR). The idea is to have a good control system that will be able to give optimal performance, reject high load disturbance, and track set point change. In order to study the performance of the two model predictive controllers, MIMO Proportional-Integral-Derivative controller (PID) strategy is used as benchmark. The LMPC, NNMPC, and PID strategies are used for controlling the residual concentration (CA) and reactor temperature (T). NNMPC control shows a superior performance over the LMPC and PID controllers by presenting a smaller overshoot and shorter settling time.

Facilitation and restoration of cognitive function in primate prefrontal cortex by a neuroprosthesis that utilizes minicolumn-specific neural firing

Objective. Maintenance of cognitive control is a major concern for many human disease conditions; therefore, a major goal of human neuroprosthetics is to facilitate and/or recover the cognitive function when such circumstances impair appropriate decision making. Approach. Minicolumnar activity from the prefrontal cortex (PFC) was recorded from nonhuman primates trained to perform a delayed match to sample (DMS), via custom-designed conformal multielectrode arrays that provided inter-laminar recordings from neurons in the PFC layer 2/3 and layer 5. Such recordings were analyzed via a previously demonstrated nonlinear multi-input–multi-output (MIMO) neuroprosthesis in rodents, which extracted and characterized multicolumnar firing patterns during DMS performance. Main results. The MIMO model verified that the conformal recorded individual PFC minicolumns responded to entrained target selections in patterns critical for successful DMS performance. This allowed the substitution of task-related layer 5 neuron firing patterns with electrical stimulation in the same recording regions during columnar transmission from layer 2/3 at the time of target selection. Such stimulation improved normal task performance, but more importantly, recovered performance when applied as a neuroprosthesis following the pharmacological disruption of decision making in the same task. Significance. These findings provide the first successful application of neuroprosthesis in the primate brain designed specifically to restore or repair the disrupted cognitive function.

Nonlinear modeling of dynamic interactions within neuronal ensembles using Principal Dynamic Modes

A methodology for nonlinear modeling of multi-input multi-output (MIMO) neuronal systems is presented that utilizes the concept of Principal Dynamic Modes (PDM). The efficacy of this new methodology is demonstrated in the study of the dynamic interactions between neuronal ensembles in the Pre-Frontal Cortex (PFC) of a behaving non-human primate (NHP) performing a Delayed Match-to-Sample task. Recorded spike trains from Layer-2 and Layer-5 neurons were viewed as the "inputs" and "outputs", respectively, of a putative MIMO system/model that quantifies the dynamic transformation of multi-unit neuronal activity between Layer-2 and Layer-5 of the PFC. Model prediction performance was evaluated by means of computed Receiver Operating Characteristic (ROC) curves. The PDM-based approach seeks to reduce the complexity of MIMO models of neuronal ensembles in order to enable the practicable modeling of large-scale neural systems incorporating hundreds or thousands of neurons, which is emerging as a preeminent issue in the study of neural function. The "scaling-up" issue has attained critical importance as multi-electrode recordings are increasingly used to probe neural systems and advance our understanding of integrated neural function. The initial results indicate that the PDM-based modeling methodology may greatly reduce the complexity of the MIMO model without significant degradation of performance. Furthermore, the PDM-based approach offers the prospect of improved biological/physiological interpretation of the obtained MIMO models.

Identification of nonlinear MIMO block-oriented systems with moving average noises using gradient based and least squares based iterative algorithms

Nonlinear Multi-Input Multi-Output (MIMO) models seem quite suitable to represent most industrial systems and many control problems. Furthermore, the outputs of the real systems are usually corrupted with noises which might not satisfy the assumption of white noises. This paper presents simple and efficient identification methods for nonlinear MIMO systems in the presence of colored noises. In the proposed approaches, three classes of MIMO block-oriented structures including Hammerstein, Wiener, and Hammerstein–Wiener models are studied. Appropriate and flexible representations of these models lead to a pseudo-linear-in-the-parameter problem that contains some terms related to the colored noises which are not known a priori. In this paper, gradient based and least squares based iterative learning algorithms are invoked which can successfully estimate the matrix of unknown parameters as well as the colored noises. The efficiency of the proposed identification schemes is investigated through two simulated and a real process as case studies. As the results show, these approaches are quite efficient for identification of nonlinear colored MIMO systems.

Interaction analysis and loop pairing for MIMO processes described by T–S fuzzy models

This paper presents a loop pairing method for determining the control configuration for multi-input–multi-output (MIMO) processes represented by Takagi–Sugeno (T–S) fuzzy models. The method is simple with straightforward calculation and it provides more accurate results compared with existing fuzzy pairing approaches, since both steady-state and dynamic information for the system is utilized. Each individual loop in the MIMO process is represented by a T–S fuzzy model based on the data and the models are then assembled to form the MIMO model. Simple formulae are derived to calculate the steady-state and dynamic information for the loops. In this way, interactions among the loops can be assessed and loop pairing can be determined according to the relative normalized gain array (RNGA) criterion. Two examples are provided to show that loop pairing decisions obtained from T–S fuzzy models are the same as those obtained from precise mathematical models. This demonstrates the effectiveness of the proposed interaction measure and the loop pairing method.

Closing the Loop for Memory Prosthesis: Detecting the Role of Hippocampal Neural Ensembles Using Nonlinear Models

A major factor involved in providing closed loop feedback for control of neural function is to understand how neural ensembles encode online information critical to the final behavioral endpoint. This issue was directly assessed in rats performing a short-term delay memory task in which successful encoding of task information is dependent upon specific spatio-temporal firing patterns recorded from ensembles of CA3 and CA1 hippocampal neurons. Such patterns, extracted by a specially designed nonlinear multi-input multi-output (MIMO) nonlinear mathematical model, were used to predict successful performance online via a closed loop paradigm which regulated trial difficulty (time of retention) as a function of the "strength" of stimulus encoding. The significance of the MIMO model as a neural prosthesis has been demonstrated by substituting trains of electrical stimulation pulses to mimic these same ensemble firing patterns. This feature was used repeatedly to vary "normal" encoding as a means of understanding how neural ensembles can be "tuned" to mimic the inherent process of selecting codes of different strength and functional specificity. The capacity to enhance and tune hippocampal encoding via MIMO model detection and insertion of critical ensemble firing patterns shown here provides the basis for possible extension to other disrupted brain circuitry.

Feedback methods for inverse simulation of dynamic models for engineering systems applications

Inverse simulation is a form of inverse modelling in which computer simulation methods are used to find the time histories of input variables that, for a given model, match a set of required output responses. Conventional inverse simulation methods for dynamic models are computationally intensive and can present difficulties for high-speed applications. This article includes a review of established methods of inverse simulation, giving some emphasis on iterative techniques that were first developed for aeronautical applications. It goes on to discuss the application of a different approach that is based on feedback principles. This feedback method is suitable for a wide range of linear and non-linear dynamic models and involves two distinct stages. The first stage involves design of a feedback loop around the given simulation model, and in the second stage, that closed-loop system is used for inversion of the model. Issues of robustness within closed-loop systems used in inverse simulation are not significant as there are no plant uncertainties or external disturbances. Thus, the process is simpler than that required for the development of a control system of equivalent complexity. Engineering applications of this feedback approach to inverse simulation are described through case studies that put particular emphasis on non-linear and multi-input multi-output models.