Abstract: A Variable Structure Model Reference Adaptive Controller using state variables is proposed for a class of multi input-multi output systems. Adaptation law is of variable structure type and switching functions is designed based on stability requirements. Global exponential stability is proved based on Lyapunov criterion. Transient behavior is analyzed using sliding mode control and shows perfect model following at a finite time.
Abstract: This paper features the proposed modeling and design
of a Robust Decentralized Periodic Output Feedback (RDPOF)
control technique for the active vibration control of smart flexible
multimodel Euler-Bernoulli cantilever beams for a multivariable
(MIMO) case by retaining the first 6 vibratory modes. The beam
structure is modeled in state space form using the concept of
piezoelectric theory, the Euler-Bernoulli beam theory and the Finite
Element Method (FEM) technique by dividing the beam into 4 finite
elements and placing the piezoelectric sensor / actuator at two finite
element locations (positions 2 and 4) as collocated pairs, i.e., as
surface mounted sensor / actuator, thus giving rise to a multivariable
model of the smart structure plant with two inputs and two outputs.
Five such multivariable models are obtained by varying the
dimensions (aspect ratios) of the aluminum beam, thus giving rise to
a multimodel of the smart structure system. Using model order
reduction technique, the reduced order model of the higher order
system is obtained based on dominant eigen value retention and the
method of Davison. RDPOF controllers are designed for the above 5
multivariable-multimodel plant. The closed loop responses with the
RDPOF feedback gain and the magnitudes of the control input are
observed and the performance of the proposed multimodel smart
structure system with the controller is evaluated for vibration control.
Abstract: In this paper, we evaluate the performance of the
Hybrid-MIMO Receiver Scheme (HMRS) in Cognitive Radio
network (CR-network). We investigate the efficiency of the proposed
scheme which the energy level and user number of primary user are
varied according to the characteristic of CR-network. HMRS can
allow users to transmit either Space-Time Block Code (STBC) or
Spatial-Multiplexing (SM) streams simultaneously by using
Successive Interference Cancellation (SIC) and Maximum Likelihood
Detection (MLD). From simulation, the results indicate that the
interference level effects to the performance of HMRS. Moreover,
the exact closed-form capacity of the proposed scheme is derived and
compared with STBC scheme.
Abstract: This report aims to utilize existing and future Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing Wireless Local Area Network (MIMO-OFDM WLAN) systems characteristics–such as multiple subcarriers, multiple antennas, and channel estimation characteristics–for indoor location estimation systems based on the Direction of Arrival (DOA) and Radio Signal Strength Indication (RSSI) methods. Hybrid of DOA-RSSI methods also evaluated. In the experimental data result, we show that location estimation accuracy performances can be increased by minimizing the multipath fading effect. This is done using multiple subcarrier frequencies over wideband frequencies to estimate one location. The proposed methods are analyzed in both a wide indoor environment and a typical room-sized office. In the experiments, WLAN terminal locations are estimated by measuring multiple subcarriers from arrays of three dipole antennas of access points (AP). This research demonstrates highly accurate, robust and hardware-free add-on software for indoor location estimations based on a MIMO-OFDM WLAN system.
Abstract: We propose a downlink multiple-input multipleoutput
(MIMO) multi-carrier code division multiple access (MCCDMA)
system with adaptive beamforming algorithm for smart
antennas. The algorithm used in this paper is based on the Least
Mean Square (LMS), with pilot channel estimation (PCE) and the
zero forcing equalizer (ZFE) in the receiver, requiring reference
signal and no knowledge channel. MC-CDMA is studied in a
multiple antenna context in order to efficiently exploit robustness
against multipath effects and multi-user flexibility of MC-CDMA and
channel diversity offered by MIMO systems for radio mobile
channels. Computer simulations, considering multi-path Rayleigh
Fading Channel, interference inter symbol and interference are
presented to verify the performance. Simulation results show that the
scheme achieves good performance in a multi-user system.
Abstract: In this paper, Neuro-Fuzzy based Fuzzy Subtractive
Clustering Method (FSCM) and Self Tuning Fuzzy PD-like
Controller (STFPDC) were used to solve non-linearity and trajectory
problems of pitch AND yaw angles of Twin Rotor MIMO system
(TRMS). The control objective is to make the beams of TRMS reach
a desired position quickly and accurately. The proposed method
could achieve control objectives with simpler controller. To simplify
the complexity of STFPDC, ANFIS based FSCM was used to
simplify the controller and improve the response. The proposed
controllers could achieve satisfactory objectives under different input
signals. Simulation results under MATLAB/Simulink® proved the
improvement of response and superiority of simplified STFPDC on
Fuzzy Logic Controller (FLC).
Abstract: We study the performance of compressed beamforming
weights feedback technique in generalized triangular decomposition
(GTD) based MIMO system. GTD is a beamforming technique that
enjoys QoS flexibility. The technique, however, will perform at its
optimum only when the full knowledge of channel state information
(CSI) is available at the transmitter. This would be impossible in
the real system, where there are channel estimation error and limited
feedback. We suggest a way to implement the quantized beamforming
weights feedback, which can significantly reduce the feedback data,
on GTD-based MIMO system and investigate the performance of
the system. Interestingly, we found that compressed beamforming
weights feedback does not degrade the BER performance of the
system at low input power, while the channel estimation error
and quantization do. For comparison, GTD is more sensitive to
compression and quantization, while SVD is more sensitive to the
channel estimation error. We also explore the performance of GTDbased
MU-MIMO system, and find that the BER performance starts
to degrade largely at around -20 dB channel estimation error.
Abstract: The highly nonlinear characteristics of drying
processes have prompted researchers to seek new nonlinear control
solutions. However, the relation between the implementation
complexity, on-line processing complexity, reliability control
structure and controller-s performance is not well established. The
present paper proposes high performance nonlinear fuzzy controllers
for a real-time operation of a drying machine, being developed under
a consistent match between those issues. A PCI-6025E data
acquisition device from National Instruments® was used, and the
control system was fully designed with MATLAB® / SIMULINK
language. Drying parameters, namely relative humidity and
temperature, were controlled through MIMOs Hybrid Bang-bang+PI
(BPI) and Four-dimensional Fuzzy Logic (FLC) real-time-based
controllers to perform drying tests on biological materials. The
performance of the drying strategies was compared through several
criteria, which are reported without controllers- retuning. Controllers-
performance analysis has showed much better performance of FLC
than BPI controller. The absolute errors were lower than 8,85 % for
Fuzzy Logic Controller, about three times lower than the
experimental results with BPI control.
Abstract: The problem of robust disturbance rejection (RDR) using a proportional state feedback controller is studied for the case of Left Invertible MIMO generalized state space linear systems with nonlinear uncertain structure. Sufficient conditions for the problem to have a solution are established. The set of all proportional feedback controllers solving the problem subject to these conditions is analytically determined.
Abstract: SDMA (Space-Division Multiple Access) is a MIMO
(Multiple-Input and Multiple-Output) based wireless communication
network architecture which has the potential to significantly increase
the spectral efficiency and the system performance. The maximum
likelihood (ML) detection provides the optimal performance, but its
complexity increases exponentially with the constellation size of
modulation and number of users. The QR decomposition (QRD)
MUD can be a substitute to ML detection due its low complexity and
near optimal performance. The minimum mean-squared-error
(MMSE) multiuser detection (MUD) minimises the mean square
error (MSE), which may not give guarantee that the BER of the
system is also minimum. But the minimum bit error rate (MBER)
MUD performs better than the classic MMSE MUD in term of
minimum probability of error by directly minimising the BER cost
function. Also the MBER MUD is able to support more users than
the number of receiving antennas, whereas the rest of MUDs fail in
this scenario. In this paper the performance of various MUD
techniques is verified for the correlated MIMO channel models based
on IEEE 802.16n standard.
Abstract: In this paper, we consider a multi user multiple input
multiple output (MU-MIMO) based cooperative reporting system for
cognitive radio network. In the reporting network, the secondary
users forward the primary user data to the common fusion center
(FC). The FC is equipped with linear equalizers and an energy
detector to make the decision about the spectrum. The primary user
data are considered to be a digital video broadcasting - terrestrial
(DVB-T) signal. The sensing channel and the reporting channel are
assumed to be an additive white Gaussian noise and an independent
identically distributed Raleigh fading respectively. We analyzed the
detection probability of MU-MIMO system with linear equalizers and
arrived at the closed form expression for average detection
probability. Also the system performance is investigated under
various MIMO scenarios through Monte Carlo simulations.
Abstract: In this paper, a TSK-type Neuro-fuzzy Inference
System that combines the features of fuzzy sets and neural networks
has been applied for the identification of MIMO systems. The procedure of adapting parameters in TSK model employs a Shuffled
Frog Leaping Algorithm (SFLA) which is inspired from the memetic evolution of a group of frogs when seeking for food. To demonstrate
the accuracy and effectiveness of the proposed controller, two nonlinear systems have been considered as the MIMO plant, and results have been compared with other learning methods based on
Particle Swarm Optimization algorithm (PSO) and Genetic
Algorithm (GA).
Abstract: In this paper, the problem of estimating the optimal
radio capacity of a single-cell spread spectrum (SS) multiple-inputmultiple-
output (MIMO) system operating in a Rayleigh fading environment
is examined. The optimisation between the radio capacity
and the theoretically achievable average channel capacity (in the
sense of information theory) per user of a MIMO single-cell SS system
operating in a Rayleigh fading environment is presented. Then,
the spectral efficiency is estimated in terms of the achievable average
channel capacity per user, during the operation over a broadcast
time-varying link, and leads to a simple novel-closed form expression
for the optimal radio capacity value based on the maximization
of the achieved spectral efficiency. Numerical results are presented to
illustrate the proposed analysis.
Abstract: The Combination of path planning and path following is the main purpose of this paper. This paper describes the developed practical approach to motion control of the MRL small size robots. An intelligent controller is applied to control omni-directional robots motion in simulation and real environment respectively. The Brain Emotional Learning Based Intelligent Controller (BELBIC), based on LQR control is adopted for the omni-directional robots. The contribution of BELBIC in improving the control system performance is shown as application of the emotional learning in a real world problem. Optimizing of the control effort can be achieved in this method too. Next the implicit communication method is used to determine the high level strategies and coordination of the robots. Some simple rules besides using the environment as a memory to improve the coordination between agents make the robots' decision making system. With this simple algorithm our team manifests a desirable cooperation.
Abstract: With the advent of DSL services, high data rates are now available over phone lines, yet higher rates are in demand. In this paper, we optimize the transmit filters that can be used over wireline channels. Results showing the bit error rates when optimized filters are used, and with a decision feedback equalizer (DFE) employed in the receiver, are given. We then show that significantly higher throughput can be achieved by modeling the channel as a multiple input multiple output (MIMO) channel. A receiver that employs a MIMO-DFE that deals jointly with several users is proposed and shown to provide significant improvement over the conventional DFE.
Abstract: In this paper, a neural network tuned fuzzy controller
is proposed for controlling Multi-Input Multi-Output (MIMO)
systems. For the convenience of analysis, the structure of MIMO
fuzzy controller is divided into single input single-output (SISO)
controllers for controlling each degree of freedom. Secondly,
according to the characteristics of the system-s dynamics coupling, an
appropriate coupling fuzzy controller is incorporated to improve the
performance. The simulation analysis on a two-level mass–spring
MIMO vibration system is carried out and results show the
effectiveness of the proposed fuzzy controller. The performance
though improved, the computational time and memory used is
comparatively higher, because it has four fuzzy reasoning blocks and
number may increase in case of other MIMO system. Then a fuzzy
neural network is designed from a set of input-output training data to
reduce the computing burden during implementation. This control
strategy can not only simplify the implementation problem of fuzzy
control, but also reduce computational time and consume less
memory.
Abstract: In the context of sensor networks, where every few
dB saving counts, the novel node cooperation schemes are reviewed
where MIMO techniques play a leading role. These methods could be
treated as joint approach for designing physical layer of their
communication scenarios. Then we analyzed the BER performance
of transmission diversity schemes under a general fading channel
model and proposed a power allocation strategy to the transmitting
sensor nodes. This approach is then compared to an equal-power
assignment method and its performance enhancement is verified by
the simulation. Another key point of the contribution lies in the
combination of optimal power allocation and sensor nodes-
cooperation in a transmission diversity regime (MISO). Numerical
results are given through figures to demonstrate the optimality and
efficiency of proposed combined approach.
Abstract: In this paper, real-coded genetic algorithm (RCGA) optimization technique has been applied for large-scale linear dynamic multi-input-multi-output (MIMO) system. The method is based on error minimization technique where the integral square error between the transient responses of original and reduced order models has been minimized by RCGA. The reduction procedure is simple computer oriented and the approach is comparable in quality with the other well-known reduction techniques. Also, the proposed method guarantees stability of the reduced model if the original high-order MIMO system is stable. The proposed approach of MIMO system order reduction is illustrated with the help of an example and the results are compared with the recently published other well-known reduction techniques to show its superiority.
Abstract: A control system design with Characteristic Ratio
Assignment (CRA) is proven that effective for SISO control design.
But the control system design for MIMO via CRA is not concrete
procedure. In this paper presents the control system design method
for quadruple-tank process via CRA. By using the decentralized
method for both minimum phase and non-minimum phase are made.
The results from PI and PID controller design via CRA can be
illustrated the validity of our approach by MATLAB.
Abstract: All the available algorithms for blind estimation namely constant modulus algorithm (CMA), Decision-Directed Algorithm (DDA/DFE) suffer from the problem of convergence to local minima. Also, if the channel drifts considerably, any DDA looses track of the channel. So, their usage is limited in varying channel conditions. The primary limitation in such cases is the requirement of certain overhead bits in the transmit framework which leads to wasteful use of the bandwidth. Also such arrangements fail to use channel state information (CSI) which is an important aid in improving the quality of reception. In this work, the main objective is to reduce the overhead imposed by the pilot symbols, which in effect reduces the system throughput. Also we formulate an arrangement based on certain dynamic Artificial Neural Network (ANN) topologies which not only contributes towards the lowering of the overhead but also facilitates the use of the CSI. A 2×2 Multiple Input Multiple Output (MIMO) system is simulated and the performance variation with different channel estimation schemes are evaluated. A new semi blind approach based on dynamic ANN is proposed for channel tracking in varying channel conditions and the performance is compared with perfectly known CSI and least square (LS) based estimation.