Gholamreza Khademi

Evolutionary optimization of user intent recognition for transfemoral amputees

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. Feature extraction, principal component analysis, correlation analysis, and K-nearest neighbor methods are used, modified, and optimized with an evolutionary algorithm for improved performance. The optimized system successfully classifies four different walking modes with an accuracy of 96%.

Hybrid invasive weed/biogeography-based optimization

Abstract 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 solution variables) to randomly distribute new seeds over the search space. In other words, there is no sharing of features among individuals. We propose the application of the migration operator from biogeography-based optimization (BBO) to include the feature-sharing capability in IWO. This modification improves the quality of the distributed seeds (that is, new candidate solutions) in the population. To further improve the local search ability of IWO, we propose the use of gradient descent. Mutation is activated under certain conditions to increase the diversity of the population, and escape local optima. We demonstrate the performance of this new hybrid IWO/BBO on a set of single-objective benchmarks, and on a real-world cyber–physical system problem to optimize a user intent recognition system for transfemoral amputees. Hybrid IWO/BBO is compared to standard IWO, BBO, and 10 other optimization algorithms. The Kruskal–Wallis and Wilcoxon signed-rank tests are used to statistically compare the algorithms. The results for hybrid IWO/BBO present promising improvements over standard IWO. For instance, out of 25 benchmarks, hybrid IWO/BBO performs better than IWO on 18 problems with dimension 30. Hybrid IWO/BBO shows competitive performance with comparison to the 10 other state-of-the-art optimization algorithms.

Multi-objective optimization of tracking/impedance control for a prosthetic leg with energy regeneration

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 considered here as a multi-objective optimization problem. Nondominated sorting biogeography-based optimization is used for this purpose. To validate the performance of system, a prosthetic leg which imitates able-bodied gait is considered. A Pareto front is obtained where a pseudo-weight scheme is used to select among solutions. A solution with minimum tracking error (0.0009 rad) fails to regenerate energy (loses 21.56 J), while a solution with poor tracking (0.0288 rad) regenerates energy (gains 167.3 J). A tradeoff results in fair tracking (0.0157 rad) and fair energy regeneration (52.9 J). Results verify that it is possible to regenerate energy at the semi-active joint while still obtaining acceptable tracking. The results indicate that ultracapacitor systems and advanced controls/optimization have the potential to significantly reduce external power requirements in powered prostheses.

Robotics and Prosthetics at Cleveland State University: Modern Information, Communication, and Modeling Technologies

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 the implementation of prosthesis and robotics research projects are presented based on experiences in the Embedded Control Systems Research Laboratory of Cleveland State University.

Multi-objective optimization of decision trees for power system voltage security assessment

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 training cases is decreased by correlation analysis. Biogeography-based optimization (BBO) and invasive weed optimization (IWO) are combined with four multi-objective (MO) optimization methods to find the optimum dimensions of the PMU data while minimizing the misclassification rate of the security test. The four MO methods include vector evaluation (VE), nondominated sorting (NS), niched Pareto (NP), and strength Pareto (SP). A systematic comparison of MOIWO and MOBBO is conducted using Pareto front hypervolume and relative coverage. The method is applied to a 66-bus power grid in Iran. The results show that the training data size is reduced by about 98%, and the training time is approximately 200 times faster because of the dimension reduction. The misclassification rates of the DTs are in the range of 4–9%. Hypervolume and relative coverage indicate that VEBBO performs better than the other methods.

Hybrid FDG optimization method and kriging interpolator to optimize well locations

As the number of new significant oilfields discoveries are reduced and as production operations become more challenging and expensive, the efficient development of oil reservoirs in order to satisfy increasing worldwide demand for oil and gas becomes crucial. A key decision engineers must make is where to drill wells in the reservoir to maximize net present value or some other objectives. Since the number of possible solutions that depend on the size of reservoir can be very large, the use of an optimization algorithm is necessary. Optimization methods are divided into two main categories: non-gradient-based and gradient-based algorithms. In the former, the search strategy is to find global optimum while they need a great number of reservoir simulation runs. On the other hand, gradient-based optimization algorithms search locally but require fewer reservoir simulations. The computational cost of optimization method in the optimal well placement problem is substantial. Thus, in practical problems with large models, implying the gradient-based method is preferable. In the present paper, finite difference gradient (FDG) algorithm as one of the easy implemented gradient-based family is used. The main disadvantage of the mentioned technique is its dependency on the number of decision variables. The major contribution of this paper is to hybrid the FDG method and kriging interpolator. This interpolator is used as a proxy to decrease the required number of function evaluations and estimate the direction of movements in the FDG algorithm. Moreover, the idea of local grid refinement is proposed to eliminate the mixed integer problem of well placement. Then, the method is applied to some sample reservoirs and the simulation results verify the performance of the proposed method.

LMI based model order reduction considering the minimum phase characteristic of the system

One usual method to solve the model order reduction problem is to minimize the H∞-norm of the difference between the transfer function of the original system and the reduced one. In many papers, the minimization problem is solved using the Linear Matrix Inequality (LMI) approach. This paper deals with defining an extra matrix inequality constraint to guaranty that the minimum phase characteristic of the system preserves after order reduction. To overcome this, poles and zeros of the reduced system transfer function must be at Left-Half Plane (LHP). It is very easy to apply a LMI condition to force the poles of the system to be at LHP. However, the same cannot be applied to zeroes easily. Thus, a special state-space realization of the system is introduced in a way to apply conditions on zeros of the reduced system. The method is applied to some sample example and the simulation results verify the performance of the proposed method.

State Estimation of an Advanced Rowing Machine Using Optimized Kalman Filtering

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 by estimating the force with an unknown-input Kalman filter. The estimated states and the unknown user force input are passed to the controller of the system, which is either an inversion-based controller or a semi-active impedance controller. Two friction models are considered for this system: Coulomb friction, and LuGre friction. The Kalman gains are tuned using an evolutionary algorithm to minimize the standard deviation of the estimation error. The results verify the effectiveness of the proposed approach for simultaneous estimation of the states and the input force. The standard deviation of the state estimation errors are only 10% of their measurement noise. The standard deviation of the input force estimation error is 0.1 N when using an optimized Kalman gain, which is only 25% of the value obtained when using manually tuned gains.