=Paper=
{{Paper
|id=Vol-1614/paper_34
|storemode=property
|title=Information, Communication, and Modeling Technologies in Prosthetic Leg and Robotics Research
at Cleveland State University
|pdfUrl=https://ceur-ws.org/Vol-1614/paper_34.pdf
|volume=Vol-1614
|authors=Yuriy Kondratenko,Gholamreza Khademi,Vahid Azimi,Donald Ebeigbe,Mohamed Abdelhady,Seyed Abolfazl Fakoorian,Taylor Barto,Arash Roshanineshat,Igor Atamanyuk,Dan Simon
|dblpUrl=https://dblp.org/rec/conf/icteri/KondratenkoKAEA16
}}
==Information, Communication, and Modeling Technologies in Prosthetic Leg and Robotics Research
at Cleveland State University==
https://ceur-ws.org/Vol-1614/paper_34.pdf
Information, Communication, and Modeling
Technologies in Prosthetic Leg and Robotics Research at
Cleveland State University
Yuriy Kondratenko1,2, Gholamreza Khademi1, Vahid Azimi1, Donald Ebeigbe1,
Mohamed Abdelhady1, Seyed Abolfazl Fakoorian1, Taylor Barto1,
Arash Roshanineshat1, Igor Atamanyuk2, and Dan Simon1
1
Department of Electrical Engineering and Computer Science
Cleveland State University, Cleveland, Ohio, USA
d.j.simon@csuohio.edu, y.kondratenko@csuohio.edu
2
Department of Intelligent Information Systems
Petro Mohyla Black Sea State University, 68-th Desantnykiv str. 10,
54003 Mykolaiv, Ukraine
y_kondrat2002@yahoo.com
Abstract. This paper analyzes the role of information and communication tech-
nology (ICT) and computer modelling in the education of engineering students.
Special attention is paid to research-based education and the implementation of
new modelling methods and advanced software in student research, including
course work, diploma projects, and theses for all student categories, including
Doctoral, Master’s, and Bachelor’s. The paper concentrates on the correlation
between student research and government priorities and research funding. Suc-
cessful cases of such correlations with specific description of computer model-
ing methods for the implementation of prosthesis and robotics research projects
are presented based on experiences in the Embedded Control Systems Research
Laboratory in the Electrical Engineering and Computer Science Department in
the Washkewicz College of Engineering, Cleveland State University, USA.
Keywords: ICT, computer modeling, research-based education, student project,
prosthesis research, governmental priority
Key Terms: Academia, Research, MathematicalModeling, ComputerSimula-
tion, Experience
1 Introduction
Information and communication technologies (ICT), mathematical modeling, and
computer simulation play a significant role in higher education. Most advanced edu-
cational systems in the world are oriented toward the implementation of educational
processes of modern ICT and software for modelling and simulation in various fields
of human activity, including science, engineering, and technology. This approach is
required for the efficient training of students at various levels: undergraduates, gradu-
ICTERI 2016, Kyiv, Ukraine, June 21-24, 2016
Copyright © 2016 by the paper authors
- 169 -
ates, and doctoral students. Many international conferences on ICT and its applica-
tions for education are devoted to the use of computer modeling, open-source soft-
ware, pedagogical e-learning, web-based e-learning, course-centered knowledge man-
agement and application in online learning based on web ontology, on-online learning
in enterprise education, simulation languages, modeling and simulation for education
and training, improving education through data mining, 3D software systems, 3D
visualization, wireless communication, experimental teaching of program design,
different approaches in teaching programming, web-based computer-assisted lan-
guage learning, and so on.
It is important that university and IT-industry participants of conferences try to
find efficient solutions for the abovementioned computer-modeling-based educational
problems. For example, participants from 178 different academic institutions, includ-
ing many from the top 50 world-ranked institutions, and from many leading IT corpo-
rations, including Microsoft, Google, Oracle, Amazon, Yahoo, Samsung, IBM, Ap-
ple, and others, attended the 12th International Conference on Modeling, Simulation
and Visualization Methods, MSV-2015, in Las Vegas, Nevada, USA.
If IT industry today supports higher education, then tomorrow’s IT-based compa-
nies, government research agencies, and national laboratories will obtain the high-
quality graduates that they need. New achievements in ICT require continuous track-
ing by educators, and implementation in education.
Successful introduction of ICT to higher education based on research-oriented ed-
ucation and training is considered and analyzed in this paper. The focus is on the role
of computer modeling and simulation in prosthesis and robotics research for increas-
ing student quality, including grading their practical skills, and including efficient
professor-student interactions.
2 Literature Analysis and Problem Statement
Many publications are devoted to teaching methods and approaches based on ICT
and computer modelling, for increasing the efficiency of their interrelation: qualitative
modeling in education [3], computer simulation technologies and their effect on learn-
ing [21], opportunities and challenges for computer modeling and simulation in sci-
ence education [31], web-based curricula [4] and remote access laboratories, comput-
er‐based programming environments as modelling tools in education and the peculiar-
ities of textual and graphical programming languages [15], interrelations between
computer modeling tools, expert models, and modeling processes [41], efficient sci-
ence education based on models and modelling [9], educational software for collec-
tive thinking and testing hypotheses in computer science [23], and others.
Many publications are devoted to improving teaching efficiency for specific cours-
es by introducing modern ICT and computer modelling technologies. In particular,
modelling supported course programs, computer-based modelling (AutoCAD, Excel,
VBA, etc.) and computer system support for higher education in engineering [8];
software to enhance power engineering education [32]; computer modeling for en-
hancing instruction in electric machinery [20]; computer modelling in mathematics
- 170 -
education [37]; GUI-based computer modelling and design platforms to promote in-
teractive learning in fiber optic communications [42]; RP-aided computer modelling
for architectural education [33]; teaching environmental modelling; computer model-
ling and simulation in power electronics education [24]; and a virtual laboratory for a
communication and computer networking course [19].
Special attention in the literature [5] is paid to the role of ICT and modeling tech-
nology in education and training in the framework of research-based curricula. This
educational approach deals first with educational directions such as robotics, mecha-
tronics, and biomechanics (RMBM) [12, 30, 38]. The correlation of RMBM with ICT
and modeling are underlined by results such as: a multidisciplinary model for robotics
in engineering education; integration of mechatronics design into the teaching of
modeling; modelling of physical systems for the design and control of mechatronic
systems [38]; biomechanical applications of computers in engineering education [30];
computerized bio-skills system for surgical skills training in knee replacement [6];
computer modelling and simulation of human movement [22]; computer modelling of
the human hand [17]; and design and control of a prosthesis test robot [26, 27].
The main aims of this paper are given as follows.
(a) Description and analysis of research-based education based on the experi-
ence in the Embedded Control Systems Research Laboratory at the Electrical Engi-
neering and Computer Science Department at the Washkewicz College of Engineer-
ing at Cleveland State University (CSU), USA, with a focus on undergraduate, gradu-
ate, and doctoral student participation in prosthesis and robotics research, which is
funded by the US National Science Foundation (NSF);
(b) Analysis of applied ICT and modeling technologies and advanced software,
as well as their implementation in student research, including course work, diploma
projects, and Doctoral, Master’s, and Bachelor’s theses;
(c) Focus on the correlation between student research and government science
priorities based on successful cases of ICT and advanced modelling implementation in
US government-funded prosthesis research, with particular focus on undergraduate,
graduate, and doctoral student participation in prosthesis and robotics research.
The rest of this paper is organized as follows. Section 3 presents a general descrip-
tion of the prosthesis research project granted by the US NSF. In Section 4 the authors
consider the implementation of ICT in prosthesis and robotics research at CSU. The
paper ends with a conclusion in Section 5.
3 NSF Project “Optimal Prosthesis Design with Energy
Regeneration” for Research-Based Education
CSU’s research project “Optimal prosthesis design with energy regeneration”
(OPDER) is funded by the US NSF (1.5M USD). Professors and students from the
Department of Electrical Engineering and Computer Science, and the Department of
Mechanical Engineering, are involved in research according to the project goals,
which deal with the development of: (a) new approaches for the simulation of human
limb control; (b) new approaches for optimizing prosthetic limb control, capturing
- 171 -
energy during walking, and storing that energy to lengthen useful prosthesis life;
(c) prosthesis prototype development.
The human leg transfers energy between the knee, which absorbs energy, and the
ankle, which produces energy. The prosthesis that results from this research will mim-
ic the energy transfer of the human leg. Current prostheses do not restore normal gait,
and this contributes to degenerative joint disease in amputees. This research will de-
velop new design approaches that will allow prostheses to perform more robustly,
closer to natural human gait, and last longer between battery charges.
This project forms a framework for research-based education. Doctoral, graduate,
and undergraduate students are involved in research such as: the study of able-bodied
gait and amputee gait; the development of models for human motion control to pro-
vide a foundation for artificial limb control; the development of electronic prosthesis
controls; the development of new approaches for optimizing prosthesis design param-
eters based on computer intelligence; the fabrication of a prosthesis prototype and its
test in a robotic system; the conduct of human trials of the prosthesis prototype.
The role of student participation in all aspects of the research is significant for in-
creasing their qualifications for their careers, for presentations at conferences, for
publishing in journals, and for research with professors who can help them be more
successful in building their future careers in industry or academia. In the next section
we describe the student contribution to prosthesis and robotics research at CSU.
4 Student contributions to prosthesis and robotics research
Seven cases of student research in the framework of the OPDER project are de-
scribed in this section.
Evolutionary Optimization of User Intent Recognition for Transfemoral Amputees.
Powered prostheses can help amputees handle multiple activities: standing, level
walking, stepping up and down, walking up and down a ramp, etc. For each walking
mode, a different control strategy or control gains are used to control the prosthesis. It
is important to infer the user’s intent automatically while transitioning from one walk-
ing mode to another one, and to subsequently activate the suitable controller or con-
trol gains. Pattern recognition techniques are used to address such problems.
In this research, mechanical sensor data are experimentally collected from an able-
bodied subject. Collected signals are processed and filtered to eliminate noise and to
handle missing data points. Signals reflecting the state of the prosthesis, user-
prosthesis interactions, and prosthesis-environment interactions are used for user in-
tent recognition. Principal component analysis is used to convert data to a lower di-
mension by eliminating the least relevant features. We propose the use of correlation
analysis to remove highly correlated observations from the training set.
We use K-nearest neighbor (K-NN) as a classification method. K-NN is modified
and optimized with an evolutionary algorithm for enhanced performance. In the modi-
fied K-NN, the contribution of each neighbor is weighted on the basis of its distance
to the test point, and the history of previously classified test points is considered for
classification of the current test point. This modification leads to better performance
- 172 -
than standard K-NN. Optimization techniques can be used to tune the parameters and
obtain a classification system with the highest possible accuracy. We choose biogeog-
raphy-based optimization (BBO) as the evolutionary optimization algorithm for this
purpose. The optimization problem is to minimize the classification error.
We use MATLAB to implement user intent recognition. BBO is a stochastic algo-
rithm, so it requires several runs to optimize the parameters. The optimization process
may take multiple days, so we use parallel computing to reduce the optimization time
from 7.77 days to about 20 hours [11]. To test the proposed method, multiple sets of
experimental data were collected for various gait modes: standing (ST), slow walking
(SW), normal walking (NW), and fast walking (FW). Fig. 1 illustrates the experi-
mental setup for able-bodied subjects. Hip and ankle angles, ground reaction force
(GRF) along three axes, and hip moment, comprise the six input signals which were
used for user intent recognition. Fig. 2 shows an example of test data for a walking
trial lasting approximately 18 seconds, which included different walking modes.
Flexion
-80
Ankle
(Nm) Flexion (deg)
-100
40
(deg)
20
Moment Hip
0
100
Hip
0
-100
100
50
(N)
Fx
0
1000
(N)
Fy
500
0
200
(N)
0
Fz
-200
FW
Walking
Mode
NW
SW
ST
0 2 4 6 8 time (s) 12 14 16 18
Fig. 1. Experimental Fig. 2. Sample test data showing four different gait
setup: data collection for modes and transitions: ST (standing), SW (slow walk), NW
able-bodied subjects (normal walk), and FW (fast walk)
Fig. 3 shows the performance of the classifier using both simple K-NN and opti-
mized K-NN. Classification error for optimized K-NN is 3.6% which is improved
from 12.9% with standard K-NN.
In conclusion, K-NN was modified to enhance the performance of a user intent
recognition system. An evolutionary algorithm was applied to optimize the classifier
parameters. Experimental data was used for training and testing the system. It was
shown that the optimized system can classify four different walking modes with an
accuracy of 96%. The code used to generate these results is available at
http://embeddedlab.csuohio.edu/prosthetics/research/user-intent-recognition.html.
Further details about this research can be found in [11].
- 173 -
FW FW
Actual
Actual Classified
Classified
NW
NW
Walking
Mode
Walking
Mode
SW
SW
ST
ST
0 2 4 6 8 10 12 14 16 18
0 2 4 6 8 10 12 14 16 18 Time (s)
Time (s)
Fig. 3. Classifier results for optimized K-NN 3.6% error (right), improved from 12.9% with
standard K-NN (left)
Stable Robust Adaptive Impedance Control of a Prosthetic Leg. We propose a
nonlinear robust model reference adaptive impedance controller for a prosthetic leg.
We use an adaptive control term to compensate for the uncertain parameters of the
system, and a robust control term so the system trajectories exhibit robustness to
variations of ground reaction force (GRF). The algorithm not only compromises
between control chattering and tracking performance, but also bounds parameter
adaptation to prevent unfavorable drift. The acceleration-free regressor form of the
system removes the need to measure joint accelerations, which would otherwise
introduce noise in the system. We use particle swarm optimization (PSO) to optimize
the design parameters of the controller and the adaptation law. The PSO cost function
is comprised of control signal magnitudes and tracking errors.
The prosthetic component is modeled as an active transfemoral (above-knee)
prosthesis. This model has a prismatic-revolute-revolute (PRR) joint structure. Human
hip and thigh motion are emulated by a prosthesis test robot. The vertical degree of
freedom represents human vertical hip motion, the first rotational axis represents an-
gular thigh motion, and the second rotational axis represents prosthetic angular knee
motion [26, 27]. The three degree-of-freedom model can be written as follows [36]:
𝑀𝑞̈ + 𝐶𝑞̇ + 𝑔 + 𝑅 = 𝑢 − 𝑇𝑒 (1)
where 𝑞 𝑇 = [𝑞1 𝑞2 𝑞3 ] is the vector of generalized joint displacements (𝑞1 is the
vertical displacement, 𝑞2 is the thigh angle, and 𝑞3 is the knee angle); u is the control
signal that comprises the active control force at the hip and the active control torques
at the thigh and knee; and 𝑇𝑒 is the effect of the GRF on the three joints.
The contribution of this research is a nonlinear robust adaptive impedance con-
troller using a boundary layer and a sliding surface to track reference inputs, in the
presence of parameter uncertainties. We desire the closed-loop system to provide
near-normal gait for amputees. Therefore, we define a target impedance model with
characteristics that are similar to those of able-bodied walking:
𝑀𝑟 (𝑞̈ 𝑟 − 𝑞̈ 𝑑 ) + 𝐵𝑟 (𝑞̇ 𝑟 − 𝑞̇ 𝑑 ) + 𝐾𝑟 (𝑞𝑟 − 𝑞𝑑 ) = −𝑇𝑒 (2)
where 𝑞𝑟 and 𝑞𝑑 are the state of the reference model and the desired trajectory respec-
tively. Since the parameters of the system are unknown, we use a control law [36]
𝑢=𝑀 ̂ 𝑣̇ + 𝐶̂ 𝑣 + 𝑔̂ + 𝑅̂ + 𝑇̂𝑒 − 𝐾𝑑 sat(𝑠/diag(𝜑) ) (3)
where the diagonal elements of 𝜑 are the widths of the saturation function; 𝑠 and 𝑣
are error and signal vectors respectively; 𝑀 ̂ , ̂𝐶, ̂𝑔, 𝑅̂ , and 𝑇̂ 𝑒 are estimates of
- 174 -
𝑀, 𝐶, 𝑔, 𝑅, and 𝑇𝑒 respectively. The control law of Eq. (3) comprises two different
̂ 𝑣̇ + 𝐶̂ 𝑣 + 𝑔̂ + 𝑅̂ , is an adaptive term that handles the uncertain
parts. The first part, 𝑀
parameters. The second part, 𝑇 ̂ 𝑒 − 𝐾𝑑 sat(𝑠/diag(𝜑)), satisfyies the reaching condi-
tion and the variations of the external inputs 𝑇𝑒 .
We use PSO to tune the controller and estimator parameters. PSO decreases the
cost function (a blend of tracking and control costs) by 8%. We suppose the system
parameters can vary ±30% from their nominal values. Fig. 4 compares the states of
the closed-loop system with the desired trajectories when the system parameters vary.
The MATLAB code used to generate these results is available at
http://embeddedlab.csuohio.edu/prosthetics/research/robust-adaptive.html [2].
0.04 0.4
hip displacement(m)
hip velocity(m/s)
0.02 0.2
0 0
-0.02 -0.2
-0.04 -0.4
0 1 2 3 4 0 1 2 3 4
time(s) time(s)
2 4
thigh angular velocity(rad/s)
thigh angle(rad)
2
1.5
0
1
-2
0.5 -4
0 1 2 3 4 0 1 2 3 4
time(s) time(s)
1.5 10
knee angular velocity(rad/s)
knee angle(rad)
1 5
0.5 0
0 -5
-0.5 -10
0 1 2 3 4 0 1 2 3 4
time(s) time(s)
Fig. 4. Tracking performance for the joint displacements and velocities
Hybrid Function Approximation Based Control for Prosthetic Legs. The combina-
tion of a prosthesis test robot and a prosthesis and how their respective controllers
could be combined to yield a coupled stable controller is addressed in this research.
The prosthesis test robot was assumed to be controlled by a regressor-based controller
while the prosthesis was assumed to be controlled by a regressor-free controller. We
address this problem by first defining a framework on which two controllers could be
combined where the controllers are indirectly dependent on each other. We propose a
- 175 -
theorem that yields a stable robotic system by the combination of the prosthesis test
robot and the prosthesis leg.
The mathematical proof depends on using the open loop dynamics of the system to
develop the closed loop system dynamics using the control law developed in the theo-
rem. We then employ a Lyapunov function to verify the stability of the robotic system
with the proposed controller. We also evaluate the transient response of the system by
evaluating the upper bounds for both the Lyapunov function and the error vector.
We use MATLAB/Simulink to model the robotic system and then simulate the
system’s behavior when the proposed controller is applied; see Fig. 5 and Fig. 6.
Fig. 5. Plot of joint angle trajectories Fig. 6. Plot of control signals and vertical
ground reaction force
Results show that the controller is able to drive the system to a desired state. Fig. 5
shows good tracking of the reference trajectories which is desired. However, Fig. 6
shows that the control signals 𝑢2 and 𝑢3 are too large to be implemented on the robot-
ic system in real-time as it will lead to damage of equipment; additional research is
needed to reduce the control signal magnitudes.
In conclusion, the simulation results show that the combination of two different
robotic systems with different control schemes is possible, which is further verifica-
tion of the stability proof. The simulation results help us investigate implementation
of an environmental interaction controller to trade off tracking accuracy and reaction
force magnitudes, hence reducing the control signal magnitudes.
The MATLAB code that was used to generate these results can be downloaded
from http://embeddedlab.csuohio.edu/prosthetics/research/hybrid-fat.html [7].
System Identification and Control Optimization of a Prosthetic Knee. A Mauch SNS
knee has been attached to an EMG-30 geared DC motor as our active leg prosthesis.
The Mauch SNS knee is a widely-used passive prosthesis; we have modified it by
removing the damper connection and driving it with our DC motor. Our work pro-
vides a conceptual approach for the system identification, control optimization, and
implementation of an active prosthetic knee during swing phase.
To apply velocity control to the system, Proportional-Integral-Derivative control
(PID) is used due its effectiveness in a wide range of operating conditions, its func-
- 176 -
tional simplicity, and its ease of use with embedded systems technology. The goal is
to investigate the behavior of PID parameters with respect to shank length. To achieve
this goal we have to find a model for the prosthetic leg. We use heuristic algorithms
and gradient algorithms to identify model parameters and tune the PID controller.
Particle Swarm Optimization (PSO), BBO, and Sequential Quadratic Optimization
(SQP) [16, 18, 29, 34] are used for identification and tuning.
Hardware setup includes a PC connected to a Quanser© DAQ card. MATLAB
with Quanser Quarc software for real-time connectivity, and DAQ hardware; see Fig.
7. The DAQ system delivers an analog control signal to a servo amplifier to drive the
EMG30 DC motor. The encoder sends signals through two digital channels. We use a
quadrature encoder which has the ability to sense rotational direction.
Encoder Data
Active Prosthetic
Servo Amplifier DAQ System
Leg
Fig. 7. Hardware Setup
Numerical differentiation is usually used to obtain angular velocity by differentiat-
ing the encoder signal [39]. This technique leads to a distorted signal due to encoder
resolution. So a Kalman filter is instead designed to estimate the angular velocity.
The DC geared motor and the Mauch SNS joint are described mathematically [10].
Simulink is used to implement the models. In order to find model parameters, each
optimization algorithm executes 20 times. The DC motor mode and Mauch knee joint
model are combined to form the active prosthetic leg model. We also conducted a
sensitivity analysis test for PSO and BBO.
The active prosthetic knee model and PID are used to build a closed-loop feedback
system. To investigate PID controller parameter behavior with respect to shank
length, we use optimization algorithms to tune controller parameters (𝐾𝑝 , 𝐾𝑖 and 𝐾𝑑 ).
Results show that for model parameter identification, PSO gives the best optimiza-
tion results, and BBO gives better average overall performance than SQP. For PID
tuning, BBO achieves the best average overall performance, but PSO shows the fast-
est average convergence. Finally, we see that increasing shank length results in an
increase in the optimal proportional gain, and a decrease in the optimal differential
and integral gains as shown in Fig. 8.
- 177 -
20
15
Gains Kp
10
Ki
5 Kd
0
1 2 3 4 5 6 7 8 9
Extension (cm)
Fig. 8. PID parameter changes with respect to shank length
Ground Reaction Force Estimation with an Extended Kalman Filter. A method to
estimate GRF in a robot/prosthesis system is presented. The system includes a robot
that emulates human hip and thigh motion, and a powered prosthesis for transfemoral
amputees, and includes four degrees of freedom: vertical hip displacement, thigh an-
gle, knee angle, and ankle angle. A continuous-time extended Kalman filter (EKF)
[35] estimates the states of the system and the GRFs that act on the prosthetic foot.
The system includes eight states: 𝑞1 is vertical hip displacement, 𝑞2 is thigh angle,
𝑞3 is knee angle, 𝑞4 is ankle angle, and their derivatives. Horizontal and vertical GRF
is applied to the toe and heel of a triangular foot. The ground stiffness is modeled to
calculate GRF. The initial state 𝑥(0) is obtained from reference data, and we ran-
domly initialize the estimated state 𝑥 ̂(0) to include estimation error. The diagonal
covariance matrices of the continuous-time process noise and measurement noise are
tuned to obtain good performance.
Results are shown in Fig. 9. Although significant initial estimation errors are pre-
sent for displacements and velocities, the EKF converges to the true states quickly.
external force in x-direction acting on heel(N)
external force in x-direction acting on toe(N)
200 150
actual actual
estimated estimated
150
100
100
Midstance
50
50
Toe-off
Heel
strike
0
0
-50 -50
0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1
time(sec) time(sec)
(a) (b)
external force in z-direction acting on heel(N)
external force in z-direction acting on toe(N)
1000 800
actual actual
800 estimated estimated
600
600
400
400 Midstance
200
200 Toe-off
Heel
strike
0
0
-200 -200
0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1
time(sec) time(sec)
(c) (d)
Fig. 9. Horizontal and vertical ground force (GRF) estimation
- 178 -
Electronic Energy Converter Design for a Regenerative Prosthetics. Prosthetic
models use ideal electromechanical actuators for knee joints, which do not include
energy regeneration. In order to focus on energy regeneration, a voltage source con-
verter is designed to interface an electric motor to a supercapacitor.
A converter was designed to resemble a typical H-bridge motor driver. The voltage
converter control system allows power to flow from the motor to the capacitor (motor
mode) and from the capacitor to the motor (generator mode). During motor mode, the
voltage converter's control system modulates the voltage applied to the motor using
two circuits; one with the capacitor connected (powering the motor from the capaci-
tor) and one with the capacitor disconnected (shorting the motor connection through
the H-bridge). During generator mode, the voltage converter control system changes
the impedance connected to the motor using two circuits; one with the capacitor con-
nected (charging the capacitor ) and one with the capacitor disconnected (allowing the
motor to move with less resistance from the electronics). The circuit and motor were
modeled with state space equations using MATLAB and Simulink software.
Two controllers were designed for the voltage converter. Both controllers use ref-
erence knee torque from control signals in the mechanical model with an ideal actua-
tor at the knee. The first controller, a PD (proportional-derivative) controller, com-
pares reference torque to the torque generated by the motor and voltage converter.
The controller uses the comparison between reference and simulation data to deter-
mine switching between connecting and disconnecting the capacitor and motor. The
switches use measured velocity to determine the direction of motor rotation. The con-
troller uses direction, mode, and torque error to provide correct modulation. The sec-
ond controller, an artificial neural network, follows the same logic as the PD control-
ler. The controller gains were optimized with BBO. The optimized controller was able
to track the reference torque with root mean square (RMS) error of 1.35 Amps as
shown in Fig. 10. As can be seen in Fig. 11, the system was able to store 17.6 Joules
in the capacitor bank. The results from the motor and voltage converter simulation
show that it may be possible to gain energy through a normal stride. The energy
gained would allow a prosthesis to operate longer than current powered prostheses.
Fig. 10. Tracking a reference current for the knee joint with a motor and voltage converter
- 179 -
Fig. 11. The energy gained during one stride of gait with a motor and voltage converter
Fuzzy Logic for Robot Path Finding. This research deals with fuzzy logic to find a
path for mobile robots that move in environments with obstacles, when the robot does
not have prior information about the obstacles.
The radar of the robot returns a fuzzy set based on the distance Li from obstacle i
𝜑 𝐿𝑖
(see Fig. 12): 𝜇𝑖 (𝜑𝑖 ) = . The robot finds the angle between its position and the
𝐿𝑚𝑎𝑥
target position, which we call 𝛼. If the robot moved in the 𝛼 direction in an obstacle-
free environment it would follow a direct line to the target. However, there are obsta-
cles in the path. To find a safe path around the obstacles, we introduce a Gaussian
fuzzy set [13, 14, 25, 40] which has a maximum value at 𝛼 as follows:
(𝜑 −𝛼)2
−( 𝑖 )
𝜇𝑖𝛼 (𝜑𝑖 ) = 𝑒 2𝜎 2
𝜑 𝛼 𝜓
We combine 𝜇𝑖 (𝜑𝑖 ) and 𝜇𝑖 (𝜑𝑖 ) to obtain a new fuzzy set, 𝜇𝑖 (𝜓𝑖 ), shown in Fig. 13.
𝜓
The movement direction then is 𝜑, which is the maximum point in 𝜇𝑖 (𝜓𝑖 ), which we
call 𝐴. If the robot moves in 𝜑𝐴 , it will touch the obstacles. To solve this problem we
introduce a new fuzzy set that has the value 1 in a range of 180 degrees around 𝜑𝐴 :
(a) (b)
Fig. 12. (a) A polar radar map in the presence of an obstacle, and (b) its transformation to
Cartesian coordinates
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In the next step we defuzzify 𝜇 𝜓 (𝜑𝑖 ) ∗ 𝜇1𝜃 (𝜑𝑖 ) using center of mass [28], which is
shown in Fig. 13.
𝜓
Fig. 13. Highlighted area is 𝜇𝑖 (𝜓𝑖 )
Simulations confirm that the proposed approach provides reliable output. In dif-
ferent layouts and robot positions and target positions, the robot was able to find a
path to the target point without touching any obstacles; see Fig. 14.
Fig. 14. Fuzzy path planning results: the red line is the robot path from start to target.
5 Conclusions
The authors have described university student training. The description has focused
on student participation in the US NSF project “Optimal prosthesis design with ener-
gy regeneration” and the application of ICT and modelling technologies.
Several factors play an important role in the results of this paper. Student research
requires skill in programming and software, and a broad theoretical knowledge in
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computer science, and mechanical, electrical, and control engineering. Students used
MATLAB, Simulink, and toolboxes (Optimization, Fuzzy Logic, etc.), and program-
ming in C and C++. The software used for robot trajectory planning research was
designed and written by students in C++, and the GUI was designed using Qt and
OpenGL. Standard libraries were used to make the software cross-platform.
The most important foundation for student research is theoretical knowledge in
fundamental and elective disciplines such as Circuits, Linear Systems, Control Sys-
tems, Nonlinear Control, Machine Learning, Artificial Intelligence, Intelligent Con-
trols, Optimal State Estimation, Optimal Control, Embedded Systems, Robot Model-
ing and Control, Probability and Stochastic Processes, Population-Based Optimiza-
tion, and Prosthesis Design and Control, which provides a basic understanding of
human biomechanics and lower-limb prosthesis design and control. These courses
played a vital role in the proper grounding of basic and advanced ICT and control
theory for robotic and prosthetic leg research. The facilities at CSU and funding from
the NSF significantly helped in furthering student research-based education.
Finally, student participation in government-sponsored research, student exchanges
of research experiences with each other, and publication of research results in high-
caliber journals and conferences [1, 2, 7, 11, 16, 26], provide students with effective
training and self-confidence in their higher education. Research-based education also
allows students to obtain practical experience as research assistants, with correspond-
ing responsibilities in the development and implementation of research projects.
Student participation in real-world research significantly influences their engineer-
ing and research qualifications by: (a) giving them a strong understanding of ICT and
engineering concepts that are covered in corresponding courses; (b) giving them prac-
tical experience and the ability to apply theoretical knowledge; (c) giving them the
opportunity to learn technical material independently; (d) helping them improve fun-
damental skills to apply in other research in their future; (e) providing them with a
rich interdisciplinary research environment; and (f) providing them with an under-
standing of concepts both familiar and unfamiliar. Through extensive literature review
and actively seeking ways to solve research problems, students are prepared to make
meaningful future contributions to the field of ICT and control engineering.
Acknowledgements. The authors thank the Fulbright Program (USA) for supporting
Prof. Y. P. Kondratenko with a Fulbright scholarship and for making it possible for
this team to conduct research in together in the USA. This research was partially sup-
ported by US NSF Grant 1344954.
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