We consider robust principal component analysis (RPCA) to perform dimensionality reduction for human hand motor control based on kinematic synergies. RPCA decomposes joint angular velocity data into (i) a low-rank matrix capturing coordinated motion patterns and (ii) a sparse matrix isolating sensor artifacts. Next we apply a robust LASSO method to find synergy recruitment weights, yielding a sparse representation of hand grasping tasks. Experiments on 100 grasp trials show that RPCA maintains stable performance up to approximately 20% corruption, while classical PCA degrades quickly. Our results suggest that robustifying standard PCA and LASSO enables reliable synergy extraction even with inexpensive, low-quality sensors, supporting affordable experimentation and improved prosthetic or robotic hand control.

Networked systems that occur in various domains, such as electric networks, the brain, and opinion networks, are known to obey conservation laws. For instance, electric networks obey Kirchoff’s laws, and social networks obey opinion consensus. Conservation laws are often modeled as balance equations that relate appropriate injected flows and potentials at the nodes of the networks. A recent line of work considers the problem of estimating the unknown structure of such networked systems from observations of node potentials (and only the knowledge of the statistics of injected flows). Given the dynamic nature of the systems under consideration, an equally important task is estimating the change in the structure of the network from data – the so called differential network analysis problem. That is, given two sets of node potential observations, the goal is to estimate the structural differences between the underlying networks. We formulate this novel differential network analysis problem for systems obeying conservation laws and devise a convex estimator to learn the edge changes directly from node potentials. We derive conditions under which the estimate is unique in the highdimensional regime and devise an efficient ADMM-based approach to perform the estimation. Finally, we demonstrate the performance of our approach on synthetic and benchmark power network data.

Many networked systems such as electric networks, the brain, and social networks of opinion dynamics are known to obey conservation laws. Examples of this phenomenon include the Kirchoff laws in electric networks and opinion consensus in social networks. Conservation laws in networked systems are modeled as balance equations of the form $X = B^\ast Y$, where the sparsity pattern of $B^\ast \in \mathbb{R}^{p\times p}$ captures the connectivity of the network on $p$ nodes, and $Y, X \in \mathbb{R}^p$ are vectors of ''potentials'' and ''injected flows'' at the nodes respectively. The node potentials $Y$ cause flows across edges which aim to balance out the potential difference, and the flows $X$ injected at the nodes are extraneous to the network dynamics. In several practical systems, the network structure is often unknown and needs to be estimated from data to facilitate modeling, management, and control. To this end, one has access to samples of the node potentials $Y$, but only the statistics of the node injections $X$. Motivated by this important problem, we study the estimation of the sparsity structure of the matrix $B^\ast$ from $n$ samples of $Y$ under the assumption that the node injections $X$ follow a Gaussian distribution with a known covariance $\Sigma_X$. We propose a new $\ell_{1}$-regularized maximum likelihood estimator for tackling this problem in the high-dimensional regime where the size of the network may be vastly larger than the number of samples $n$. We show that this optimization problem is convex in the objective and admits a unique solution. Under a new mutual incoherence condition, we establish sufficient conditions on the triple $(n,p,d)$ for which exact sparsity recovery of $B^\ast$ is possible with high probability; $d$ is the degree of the underlying graph. We also establish guarantees for the recovery of $B^\ast$ in the element-wise maximum, Frobenius, and operator norms. Finally, we complement these theoretical results with experimental validation of the performance of the proposed estimator on synthetic and real-world data.

Security attacks may have disruptive consequences on cyber-physical systems, and lead to significant social and economic losses. Building secure cyber-physical systems is particularly challenging due to the variety of attack surfaces from the cyber and physical components, and often to limited computation and communication resources. In this paper, we propose a cross-layer design framework for resource-constrained cyber-physical systems. The framework combines control-theoretic methods at the functional layer and cybersecurity techniques at the embedded platform layer, and addresses security together with other design metrics such as control performance under resource and real-time constraints. We use the concept of interface variables to capture the interactions between control and platform layers, and quantitatively model the relation among system security, performance, and schedulability via interface variables. The general codesign framework is customized and refined to the automotive domain, and its effectiveness is demonstrated through an industrial case study and a set of synthetic examples.

This paper suggests a novel intelligent closed loop control strategy based on fractional order (FO) PID controller for head positioning servo control system. The design of FOPID controller has been formulated as a single objective optimization framework using time domain optimality criterion and is carried out with help of Differential Search (DS) algorithm. In order to digitally realize FOPID controller, an Oust Loup 5th order approximation has been used. It is shown that the servo system optimally moves the reader head on to the desired track when actuated with FOPID controller than a normal PID controller. Validation results of DS algorithm tuned FOPID controller are compared with PID controller, which shows the superior closed-loop response and robustness of the proposed approach.

In this paper an optimally designed PID controller equipped with Power System Stabilizer (PSS) for a Single Machine Infinite Bus (SMIB) system using linearized Modified Philip-Heffron's model is presented. The PSS design based on this model utilizes signals available within the generating station and doesn't require the knowledge about external system parameters like line impedance and infinite bus voltage. A new swarm intelligent Artificial Bee Colony (ABC) algorithm has been used to tune the PSS-PID parameters to enhance the small signal stability due to small variations in generation and loads. Various simulation results and comparisons over different loading conditions on a single machine infinite bus power system using ABC tuned PID-PSS show the superiority of ABC in designing the power system stabilizer for the model considered.

This paper deals with an interesting application of designing a Fractional Order Controller for a Servo hydraulic positioning system, to compensate the negative effects of fluid leakage across a faulty actuator piston seal. A novel micro artificial bee colony algorithm (μABC) has been proposed for designing this Fractional order Controller based on minimizing Integral Time Absolute Error (ITAE) criterion. As μABC requires a less number of bees, the solution is obtained much faster than that of basic Artificial Bee Colony (ABC) method. Simulations and comparisons of proposed method along with that of GA and an adaptive variant of ABC in terms of time domain indices indicate the superiority of μABC in designing the fractional controller for a much complex plant.

In this paper, a new variant of Teaching-Learning based Optimization (TLBO), termed as Elitist Teaching-Learning Opposition based (ETLOBA) Algorithm has been proposed for numerical function optimization. The proposed method is empowered with two mechanisms to reach the accurate global optimum with less time complexity. One of them is elitism, which strengthens the capability of optimization method by retaining the best solution obtained so far, on the other hand Opposition method helps in ameliorating the capability of searching. As ETLOBA had an advantage of both Elitism and Opposition based learning, hence it tries to obtain optimum solutions with guaranteed convergence. The proposed method has been tested on several benchmark functions and the results obtained by ETLOBA are been compared with new state-of-art optimization methods like ABC, HS etc., shows the superiority of the proposed approach in solving continuous optimization problems.

Now-a-days fractional order controllers are replacing integer order controllers in various applications due to their robust mechanism in enhancing the system performance. The only complexity using fractional order controllers is to determine the gain parameters, followed by integral and derivative constant. This article describes the design of Fractional-Order Proportional-Integral-Derivative (FOPID) controller using a newly developed variant of ABC known as Sobol Mutated Artificial Bee Colony (S-ABC). The mutation component of S-ABC is based on quasi random Sobol sequence. Controller synthesis is obtained by minimizing Integral Time Absolute Error (ITAE) criterion. In order to digitally realize the FOPID controller Tustin operator based continuous fraction approximation (CFE) method is used. Simulation results for some real life plants and comparison with other state of art optimization techniques over same problems demonstrate the superiority of proposed approach.

This paper proposes an improved version of Artificial Bee Colony (ABC) algorithm with mutation based on Levy Probability Distributions. The Levy distribution has a peculiar property of generating an offspring farther away from its parent which depends on internal parameter α compared to that of Gaussian mutations, this property enables in finding out most optimal solutions to the problems than that of conventional methods. The proposed algorithm is tested on 7 standard benchmark functions and on a set of non-traditional problems suggested in the special session of CEC'2008. Analysis and comparison of results with other state of art optimization algorithms like GA and PSO, shows the superiority of improved mutation, especially on high dimensional problems. This paper finally investigates the performance of proposed algorithm on the frequency-modulated sound wave synthesis problem, a real world problem in the field on communication engineering.