Mostafa Bagheri

Multivariable Extremum Seeking for Joint-Space Trajectory Optimization of a High-Degrees-of-Freedom Robot

In this paper, a novel analytical coupled trajectory optimization of a seven degrees-offreedom (7DOF) Baxter manipulator utilizing extremum seeking (ES) approach is presented. The robotic manipulators are used in network-based industrial units, and even homes, by expending a significant lumped amount of energy, and therefore, optimal trajectories need to be generated to address efficiency issues. These robots are typically operated for thousands of cycles resulting in a considerable cost of operation. First, coupled dynamic equations are derived using the Lagrangian method and experimentally validated to examine the accuracy of the model. Then, global design sensitivity analysis is performed to investigate the effects of changes of optimization variables on the cost function leading to select the most effective ones. We examine a discrete-time multivariable gradient-based ES scheme enforcing operational time and torque saturation constraints in order to minimize the lumped amount of energy consumed in a path given; therefore, time-energy optimization would not be the immediate focus of this research effort. The results are compared with those of a global heuristic genetic algorithm (GA) to discuss the locality/ globality of optimal solutions. Finally, the optimal trajectory is experimentally implemented to be thoroughly compared with the inefficient one. The results reveal that the proposed scheme yields the minimum energy consumption in addition to overcoming the robot’s jerky motion observed in an inefficient path. [DOI: 10.1115/1.4040752]

A Decentralized Neuro-Adaptive Control Scheme to Suppress Chaotic/Hyperchaotic Dynamics of Smart Valves Network

In this effort, we utilize a decentralized neuro-adaptive scheme in extinguishing both the chaotic and hyperchaotic dynamics of the so-called “Smart Valves” network. In particular, a network of two dynamically interconnected bidirectional solenoid actuated butterfly valves undergoes the harmful chaotic/hyperchaotic dynamics subject to some initial conditions and critical parameters. Crucial trade-offs, including robustness, computational burden, and practical feasibility of the control scheme, are thoroughly investigated. The advantages and shortcomings of the decentralized neuro-adaptive method are compared with those of the direct decentralized adaptive one to yield a computationally efficient, practically feasible, and robust scheme in the presence of the coupled harmful responses. [DOI: 10.1115/1.4039627]

Suppressing Chaotic/Hyperchaotic Dynamics of Smart Valves Network Using Decentralized and Centralized Schemes

In this effort, we present a comprehensive comparative study of decentralized and centralized adaptive schemes to control the so-called “Smart Valves” network employed in many applications including, but not limited to, Municipal Piping Systems and oil and gas fields. The network being considered here typically includes scores of coupled solenoid actuated butterfly valves. We here examine the multiphysics network of two interconnected actuated sets. The network undergoes the coupled chaotic and hyperchaotic dynamics subject to some initial conditions and critical parameters. The control schemes’ trade-offs are thoroughly investigated with respect to robustness, computational cost, and practical feasibility of control inputs in the presence of strong nonlinear interconnections and harmful chaotic and hyperchaotic responses.

Experimental and Novel Analytical Trajectory Optimization of a 7-DOF Baxter Robot: Global Design Sensitivity and Step Size Analyses

In this paper, we present a novel nonlinear analytical coupled trajectory optimization of a 7-DOF Baxter manipulator validated through experimental work utilizing global optimization tools. The robotic manipulators used in network-based applications of industrial units and even homes, for disabled patients, spend significant lumped amount of energy and therefore, optimal trajectories need to be generated to address efficiency issues. We here examine both heuristic (Genetics) and gradient based (GlobalSearch) algorithms for a novel approach of “SShaped” trajectory (unlike conventional polynomials), to avoid being trapped in several possible local minima along with yielding minimal computational cost, enforcing operational time and torque saturation constraints. The global schemes are utilized in minimizing the lumped amount of energy consumed in a nominal path given in the collision-free joint space except an impact between the robot’s end effector and a target object for the nominal operation. Note that such robots are typically operated for thousands of cycles resulting in a considerable cost of operation. Due to the expected computational cost of such global optimization algorithms, step size analysis is carried out to minimize both the computational cost (iteration) and possibly cost function by finding an optimal step size. Global design sensitivity analysis is also performed to examine the effects of changes of optimization variables on the cost function defined.

Coupled Operational Optimization of Smart Valve System Subject to Different Approach Angles of a Pipe Contraction

In this paper, we focus on interconnected trajectory optimization of two sets of solenoid actuated butterfly valves dy namically coupled in series. The system undergoes differen t approach angles of a pipe contraction as a typical profile of the so-called “Smart Valves” network containing tens of act uated valves. A high fidelity interconnected mathematical mo deling process is derived to reveal the expected complexity of s uch a multiphysics system dealing with electromagnetics, fluid mechanics, and nonlinear dynamic effects. A coupled operatio n l optimization scheme is formulated in order to seek the most e fficient trajectories of the interconnected valves minimizin g the energy consumed enforcing stability and physical constraint s. We examine various global optimization methods including Par ticle Swarm, Simulated Annealing, Genetic, and Gradient based al gorithms to avoid being trapped in several possible local mini a. The effect of the approach angles of the pipeline contractio n on the amount of energy saved is discussed in detail. The result s indicate that a substantial amount of energy can be saved by a n intelligent operation that uses flow torques to augment the c losing efforts.