Dan Simon

• Professor (Full) at Cleveland State University

A complex system is made up of multiple related and coupled subsystems. Each subsystem has its own set of multiple constraints and objectives, and is commonly found in real-world applications. Biogeography-based complex system optimization (BBO/Complex) is a population-based evolutionary intelligence paradigm that has been developed to solve comple...

This work proposes the design of an optimization method for high-power LED luminaires with the introduction of new evaluation metrics. A luminaire geometry computational method is deployed to conduct thermal and optical analysis. This current effort novels by designing a tool that enables the analysis of uniformity for individual luminaire over the...

Complex system optimization is an emerging research topic in the field of evolutionary computation, whose goal is to handle complex systems with multiple coupled subsystems, each including multiple objectives and multiple constraints in real-world applications. This paper proposes a multi-system genetic algorithm (MSGA), stemming from implicit para...

Individuals with an above-knee (AK) amputation typically use passive prostheses, whether reactive (microprocessor) or purely mechanical. Though sufficient for walking, these solutions lack the positive power generation observed in able-bodied individuals. Active (powered) prostheses can provide positive power but suffer complex control and limited...

This paper develops a robust regressor-free controller for n-link robots with state constraints. We use the function approximation technique to represent the uncertain robot dynamics. Our controller, which uses a state-dependent barrier Lyapunov function, prevents the states from violating their constraints by guaranteeing uniform ultimate boundedn...

During the COVID-19 pandemic and similar outbreaks in the future, drones can be set up to reduce human interaction for medical supplies delivery, which is crucial in times of pandemic. In this short paper, we introduce the use of two evolutionary algorithms for multi-objective optimization (MOO) and tuning the parameters of the PD controller of a d...

During the COVID-19 pandemic and similar outbreaks in the future, drones can be set up to reduce human interaction for medical supplies delivery, which is crucial in times of pandemic. In this short paper, we introduce the use of two evolutionary algorithms for multi-objective optimization (MOO) and tuning the parameters of the PD controller of a d...

Drones are effective for reducing human activity and interactions by performing tasks such as exploring and inspecting new environments, monitoring resources and delivering packages. Drones need a controller to maintain stability and to reach their goal. The most well-known drone controllers are proportional-integral-derivative (PID) and proportion...

This work proposes a resilient and adaptive state estimation framework for robots operating in perceptually-degraded environments. The approach, called Adaptive Maximum Correntropy Criterion Kalman Filtering (AMCCKF), is inherently robust to corrupted measurements, such as those containing jumps or general non-Gaussian noise, and is able to modify...

The unscented transform uses a weighted set of samples called sigma points to propagate the means and covariances of nonlinear transformations of random variables. However, unscented transforms developed using either the Gaussian assumption or a minimum set of sigma points typically fall short when the random variable is not Gaussian distributed an...

The unscented transform uses a weighted set of samples called sigma points to propagate the means and covariances of nonlinear transformations of random variables. However, unscented transforms developed using either the Gaussian assumption or a minimum set of sigma points typically fall short when the random variable is not Gaussian distributed an...

This work proposes a resilient and adaptive state estimation framework for robots operating in perceptually-degraded environments. The approach, called Adaptive Maximum Correntropy Criterion Kalman Filtering (AMCCKF), is inherently robust to corrupted measurements, such as those containing jumps or general non-Gaussian noise, and is able to modify...

The ultimate goal of ridesharing systems is to match travelers who do not have a vehicle with those travelers who want to share their vehicle. A good match can be found among those who have similar itineraries and time schedules. In this way each rider can be served without any delay and also each driver can earn as much as possible without having...

This study synthesises modelling techniques and dynamic state estimation techniques for the simultaneous estimation of the muscle states, muscle forces, and joint motion states of a dynamic human arm model. The estimator considers both muscle dynamics and motion dynamics. The arm model has two joints and six muscles and contains dynamics both of th...

This paper demonstrates the use of hardware-in-the-loop (HIL) simulation to mimic control action scenarios in an active prosthetic leg. The simulation is used to carry out a comparative assessment for model-based controllers such as computed torque, the Slotine and Li approach, and a regressor free approach using adaptive control based on the funct...

The ultimate goal of ridesharing systems is to match travelers who do not have a vehicle with those travelers who want to share their vehicle. A good match can be found among those who have similar itineraries and time schedules. In this way each rider can be served without any delay and also each driver can earn as much as possible without having...

This paper introduces an extensive human motion data set for typical activities of daily living. These data are crucial for the design and control of prosthetic devices for transfemoral prosthesis users. This data set was collected from seven individuals, including five individuals with intact limbs and two transfemoral prosthesis users. These data...

Despite the popularity of drones and their relatively simple operation, the underlying control algorithms can be difficult to design due to the drones’ underactuation and highly nonlinear properties. This paper focuses on position and orientation control of drones to address challenges such as path and edge tracking, and disturbance rejection. The...

Accurate attitude estimation using low-cost sensors is an important capability to enable many robotic applications. In this paper, we present a method based on the concept of corren-tropy in Kalman filtering to estimate the 3D orientation of a rigid body using a low-cost inertial measurement unit (IMU). We then leverage the proposed attitude estima...

Objective: Locomotion mode recognition (LMR) enables seamless and natural transitions between low-level control systems in a powered prosthesis. We present a new optimization framework for LMR that eliminates irrelevant or redundant features and measurement signals while still maintaining performance. Methods: We use multi-objective biogeography...

Kernel size plays a significant role in the performance of the maximum correntropy Kalman filter (MCC-KF). Kernel size is usually chosen by trail and error. If the kernel size is large, the MCC-KF reduces to the Kalman filter (KF). However, if the kernel size is small, the MCC-KF may diverge, or converge slowly. We propose a novel method for adapti...

Nonlinear control stability of a heart beat tracking system is investigated in this paper based on second-order original and third-order modified Zeeman’s heartbeat models. Stability analysis shows that the third-order model is less sensitive to model parameter variations and thus shows long range stable performance.

There are many important challenges in gait analysis, which has many applications in healthcare, rehabilitation, therapy, and exercise training. However, gait analysis is typically performed in a gait laboratory, which is inaccessible to the general population and is not available in natural gait environments (e.g., outdoors). In this paper, we dis...

Powered lower-limb prostheses feature a high-level intelligent control system, referred to as locomotion mode recognition (LMR), which enables seamless amputee-prosthesis interactions through activation of appropriate low-level controllers depending on the user's gait intent and environment. Environmental and terrain conditions provide valuable sub...

This paper investigates the fuzzy real-time multi-objective optimization of a combined test robot/transfemoral prosthesis system with three degrees of freedom. Impedance control parameters are optimized with respect to the two objectives of ground force and vertical hip position tracking. Control parameters are first optimized off-line with an evol...

This paper develops a novel regressor-free robust controller for rigid robots whose dynamics can be described using the Euler-Lagrange equations of motion. The function approximation technique (FAT) is used to represent the robot's inertia matrix, the Coriolis matrix, and the gravity vector as finite linear combinations of orthonormal basis functio...

The Kalman filter (KF) is optimal with respect to minimum mean square error (MMSE) if the process noise and measurement noise are Gaussian. However, the KF is suboptimal in the presence of non-Gaussian noise. The maximum correntropy criterion Kalman filter (MCC-KF) is a Kalman-type filter that uses the correntropy measure as its optimality criterio...

This paper presents and experimentally implements three different adaptive and robust adaptive controllers as the first steps toward using model-based controllers for transfemoral prostheses. The goal of this paper is to translate these control methods to the robotic domain, from bipedal robotic walking to prosthesis walking, including a rigorous s...

One control challenge in prosthetic legs is seamless transition from one gait mode to another. User intent recognition (UIR) is a high-level controller that tells a low-level controller to switch to the identified activity mode, depending on the user’s intent and environment. We propose a new framework to design an optimal UIR system with simultane...

Research on assistive technology, rehabilitation, and prosthesis requires the understanding of human machine interaction, in which human muscular properties play a pivotal role. This paper studies a nonlinear agonistic-antagonistic muscle system based on the Hill muscle model. To investigate the characteristics of the muscle model, the problem of e...

Existence of disturbances in unknown environments is a pervasive challenge in robotic locomotion control. Disturbance observers are a class of unknown input observers that have been extensively used for disturbance rejection in numerous robotics applications. In this paper, we extend a class of widely-used nonlinear disturbance observers to unde-ra...

One control challenge in prosthetic legs is seamless transition from one gait mode to another. User intent recognition (UIR) is a high-level controller that tells a low-level controller to switch to the identified activity mode, depending on the user’s intent and environment. We propose a new framework to design an optimal UIR system with simultane...

One control challenge in prosthetic legs is seamless transition from one gait mode to another. User intent recognition (UIR) is a high-level controller that tells a low-level controller to switch to the identified activity mode, depending on the user’s intent and environment. We propose a new framework to design an optimal UIR system with simultane...

The control system proposed in the paper is motivated by robotic testing of prosthetic legs, where a test robot is used to emulate the mechanics of walking. Previously, robust trajectory tracking of walking profiles was used on the test robot’s hip axes, both for swing and stance phases. For the stance phase, a tracking controller does not reproduc...

We present the design, control, and experimental evaluation of an energy regenerative powered transfemoral prosthesis. Our prosthesis prototype is comprised of a passive ankle and a powered knee joint. The knee joint is actuated by an ultracapacitor based regenerative drive mechanism. A novel varying impedance control approach controls the prosthes...

This paper develops a new function approximation technique (FAT)-based adaptive controller for the control of rigid robots called the adaptive passivity function approximation technique (APFAT) controller. This controller utilizes the passivity-based approach and simplifies the FAT controller design by eliminating the need for simultaneous estimati...

This paper presents, compares, and tests two robust model reference adaptive impedance controllers for a three degrees-of-freedom (3DOF) powered prosthesis/test robot. We first present a model for a combined system that includes a test robot and a transfemoral prosthetic leg. We design these two controllers, so the error trajectories of the system...

This paper addresses the problem of state estimation of a two-joint six-muscle linkage. It is assumed that noisy measurements of the joint angles are available. It is shown that the joint's velocities, tendon-muscle lengths and rates of tendon-muscle lengths can be estimated using a high-gain observer (HGO), sliding mode observer (SMO), and extende...

We propose a method for voltage stability assessment of power systems using a support vector machine (SVM). We input the information from measurement signals to the SVM. The objectives of this paper are twofold: (1) to select the minimum number of features for training an SVM using multi-objective optimization (MOO); (2) to find the minimum misclas...

Ground reaction force (GRF) characteristics of amputee walking are important for the analysis of clinical gait data, and also to update model reference adaptive impedance (MRAI) controllers. GRF estimation is a better alternative than direct GRF measurement because of the disadvantages of load cells, such as high cost, integration difficulties due...

This paper analyzes various methods of structural and parametric optimization for fuzzy control and decision-making systems. Special attention is paid to hierarchical structure selection, rule base reduction, and reconfiguration in the presence of incomplete data sets. In addition fuzzy system parameter optimization based on gradient descent, Kalma...

The objectives of this paper are five-fold: (1) to design an extended Kalman filter (EKF) for the single-muscle and two-muscle Hill models; (2) to design an EKF for unknown-input estimation of the single-muscle and two-muscle Hill models; (3) to investigate the detectability of the muscle models; (4) to examine the robustness of the EKF to modeling...

Research on assistive technology, rehabilitation, and prosthesis requires the understanding of human machine interaction, in which human muscular properties play a pivotal role. This paper studies a nonlinear agonistic-antagonistic muscle system based on the Hill muscle model. To investigate the characteristics of the muscle model, the problem of e...

In this paper, an agonistic-antagonistic muscle system is presented. This dual muscle system is based on the Hill muscle model. The problem of estimating the state variables and activation signals of the dual muscle system is addressed. A proposed estimation scheme which combines a super-twisting observer and an input estimator is given to provide...

Non-Gaussian noise may degrade the performance of the Kalman filter because the Kalman filter uses only second-order statistical information, so it is not optimal in non-Gaussian noise environments. Also, many systems include equality or inequality state constraints that are not directly included in the system model, and thus are not incorporated i...

This research addresses the problem of state estimation of an advanced rowing machine with energy regeneration. It is assumed that the states of the system, which are position, velocity, and capacitor charge, are measurable. The user force input to the system can be measured by load cells. It is shown that the need for load cells can be eliminated...

Biogeography-based optimization (BBO) is an evolutionary algorithm which is inspired by the migration of species between habitats. Almost 10 years have passed since the first BBO paper was published in 2008. BBO has successfully solved optimization problems in many different domains and has reached a relatively mature state. Considering the signifi...

We propose a new variant of the ecologically-inspired optimization method known as invasive weed optimization (IWO). The proposed algorithm features three new components that are typically not present in IWO: (1) migration; (2) gradient descent; and (3) mutation. In standard IWO, each individual uses only its own features (that is, independent solu...

It is difficult for a space robot to perform autonomous relative navigation of a non-cooperative space target using only a single line-of-sight measurement. To solve this problem, a decentralized-centralized relative navigation method based on multiple space robots in a leader-follower formation is proposed. All the leader and follower robots obser...

Objective: We design an optimal passivity-based tracking / impedance control system for a robotic manipulator with energy regenerative electronics, where the manipulator has both actively and semi-actively controlled joints. The semi-active joints are driven by a regenerative actuator that includes an energy-storing element. Method: External for...

Swarm intelligence (SI) optimization algorithms are fast and robust global optimization methods, and have attracted significant attention due to their ability to solve complex optimization problems. The underlying idea behind all SI algorithms is similar, and various SI algorithms differ only in their details. In this paper we discuss the algorithm...

This chapter concentrates on the correlation between research-based education, government priorities and research funding. Special attention is paid to an analysis of the role of modern information and communication technology (ICT) in the education of engineering students. Successful cases with specific description of computer modeling methods for...

This work presents a methodology for optimizing the layout and geometry of an m × n high power (HP) light emitting diode (LED) luminaire. Two simulators are used to analyze an LED luminaire model. The first simulator uses the finite element method (FEM) to analyze the thermal dissipation, and the second simulator uses the ray tracing method for lig...

Food-borne diseases associated with fresh produce consistently cause serious public health issues. Although sanitization measures are utilized to enhance the safety of fresh produce, strategies that neglect the dynamic nature of commercial wash processes are limited, creating the potential for pathogen cross-contamination and major disease outbreak...

This paper presents, compares, and experimentally implements two robust model-based controllers for transfemoral prosthetic walking: the robust passivity (RP) controller and the robust sliding mode (RS) controller. These findings constitute the first steps toward using model-based controllers for prosthetic devices as an alternative to commonly-use...

A method to estimate ground reaction forces (GRFs) in a robot/prosthesis system is presented. The system includes a robot that emulates human hip and thigh motion, along with a powered (active) transfemoral prosthetic leg. We design a continuous-time extended Kalman filter (EKF) and a continuous-time unscented Kalman filter (UKF) to estimate not on...

The science of biogeography can be traced to the work of 19th Century naturalists, most notably Alfred Wallace [WAL 06] and Charles Darwin [KEY 01] (see Figure 1.1). Wallace is usually considered the father of biogeography, although Darwin is much better known because of his preeminence in publishing the theory of evolution. Science views the distr...

The focus of this research is to consider control and energy regeneration for a robotic manipulator with both actively and semi-actively controlled joints. The semi-active joints are powered by a regenerative scheme. The problem of designing an impedance controller to track a desired joint trajectory and regenerate energy in the storage element is...

The paper considers the problem of controlling a novel hydraulic actuator to be used in a transfemoral (above-knee) prosthesis. The control objective is tracking of knee angle reference data corresponding to able-bodied walking. The forces and velocities appearing in a prosthesis when worn by a person are simulated with a two degree-of-freedom robo...

A method to estimate ground reaction forces (GRFs) in a robot/prosthesis system is presented. The system includes a robot that emulates human hip and thigh motion, along with a powered (active) prosthetic leg for transfemoral amputees, and includes four degrees of freedom (DOF): vertical hip displacement, thigh angle, knee angle, and ankle angle. W...

A method is proposed for online power system voltage security assessment (VSA) using decision trees (DTs). The DT inputs are the data gathered from phasor measurement units (PMUs). The dimensions of the training data are reduced in two ways. First, the number of features is decreased by principal component analysis (PCA). Second, the number of trai...

We develop a hybrid controller for an n-degree of freedom robot where one control approach is used for some joints while another control approach is used for the remaining joints. We combine Slotine and Li's regressor based control, and function approximation technique (FAT) based regressorfree control, to obtain a coupled controller. We verify the...

Conference paper accepted on proceeding IEEE System Conference 2016

This research proposes evolutionary optimisation for the improvement of atrial fibrillation (AF) detection. The basis of the AF detection algorithm is the irregularity of RR intervals (heartbeats) in the electrocardiogram (ECG) signal. We use three well-known statistical methods to detect RR interval irregularity: root mean squares of successive di...

We propose a nonlinear robust model reference adaptive impedance controller for an active prosthetic leg for transfemoral amputees. We use an adaptive control term to consider the uncertain parameters of the system, and a robust control term so the system trajectories converge to a sliding mode boundary layer and exhibit robustness to variations of...

We design a control system for a prosthesis test robot that was previously developed for transfemoral prosthesis design and test. The robot’s control system aims to mimic human walking in the sagittal plane. It has been seen in previous work that trajectory control alone fails to produce human-like forces. Therefore, we utilize an impedance control...

Lower-limb prosthetic legs help amputees regain their walking ability. User intent recognition is utilized to infer human gait mode (fast walk, slow walk, etc.) so the controller can be adjusted depending on the detected gait mode. In this paper, mechanical sensor data is collected from an able-bodied subject and used for user intent recognition. F...

Automotive simulations often prohibit the use of traditional optimization techniques because these simulations are complex and computationally expensive. These two qualities motivate the use of evolutionary algorithms and meta-modeling techniques respectively. In this work, we apply biogeography-based optimization (BBO) to optimize radial basis fun...

This paper proposes an ensemble multi-objective biogeography-based optimization (EMBBO) algorithm, which is inspired by ensemble learning, to solve the automated warehouse scheduling problem. First, a real-world automated warehouse scheduling problem is formulated as a constrained multi-objective optimization problem. Then EMBBO is formulated as a...

Evolutionary algorithms are robust optimization methods that have been used in many engineering applications. However, real-world fitness evaluations can be computationally expensive, so it may be necessary to estimate the fitness with an approximate model. This article reviews design and analysis of computer experiments (DACE) as an approximation...

Biogeography-based optimization (BBO) is an evolutionary algorithm inspired by biogeography, which is the study of the migration of species between habitats. This paper derives a mathematical description of the dynamics of BBO based on ideas from statistical mechanics. Rather than trying to exactly predict the evolution of the population, statistic...

Evolutionary algorithms (EA) excel in optimizing systems with a large number of variables. Previous mathematical and empirical studies have shown that opposition-based algorithms can improve EA performance. We review existing opposition-based algorithms and introduce a new one. The proposed algorithm is named fitness-based quasi-reflection and empl...

This paper introduces a Markov model for evolutionary algorithms (EAs) that is based on interactions among individuals in the population. This interactive Markov model has the potential to provide tractable models for optimization problems of realistic size. We propose two simple discrete optimization search strategies with population-proportion-ba...

Evolutionary algorithms (EAs) are widely employed for solving optimization problems with rugged fitness landscapes. Opposition-based learning (OBL) is a recent tool developed to improve the convergence rate of EAs. In this paper, we derive the probabilities that distances between OBL points and the optimization problem solution are less than the di...

In this paper the data fusion problem for asynchronous, multirate, multisensor linear systems is studied. The linear system is observed by multiple sensor systems, each having a different sampling rate. Under the assumption that the state space model is known at the scale of the highest time resolution sensor system, and that there is a known mathe...

Robotic testing can facilitate the development of new concepts, designs and control systems for prosthetic limbs. Human subject test clearances, safety and the lack of repeatability associated with human trials can be reduced or eliminated with automated testing, and test modalities are possible which are dangerous or inconvenient to attempt with p...

We present a differential particle swarm evolution (DPSE) algorithm which combines the basic idea of velocity and position update rules from particle swarm optimization (PSO) and the concept of differential mutation from differential evolution (DE) in a new way. With the goal of optimizing within a limited number of function evaluations, the algori...

Transfemoral amputees modify their gait in order to compensate for their prosthetic leg. This compensation causes harmful secondary physical conditions due to an over-dependence on the intact limb and deficiencies of the prosthesis. Even with more advanced prostheses, amputees still have to alter their gait to compensate for the prosthesis. We pres...

We describe the preliminary optimal design of an electromechanical above-knee active prosthesis with energy storage and regeneration. A DC motor-generator applies a positive or negative torque to the knee. The control system regulates the exchange of energy between the motor-generator and a supercapacitor. The central idea of the design is motivate...

Biogeography-based optimization (BBO) is an evolutionary algorithm inspired by biogeography, which is the study of the migration of species between habitats. A finite Markov chain model of BBO for binary problems was derived in earlier work, and some significant theoretical results were obtained. This paper analyzes the convergence properties of BB...