This paper presents a numerically optimized linear-quadratic-regulator-based tuning mechanism for a ubiquitous proportional-integral-derivative controller to improve the output-voltage regulation capability of a direct-current (DC)-DC buck converter. The linear-quadratic-regulator minimizes the quadratic cost of variations in the control signal and error-dynamics of output-voltage to provide a trivial set of optimized proportional-integral-derivative controller gains in the form of the state-feedback gain vector. In order to further improve the controller's time-domain performance and its disturbance-rejection capability against load-transients and input-fluctuations, an iterative-learningtuning mechanism is adopted to optimize the state-weighting matrix of the linear-quadratic cost function. The proposed optimization mechanism iteratively converges in the direction of the steepest gradient-descent of another performance index that directly captures the transient response characteristics, and thus, optimally selects the weighting matrix to achieve the desired natural frequency and damping ratio of the closed-loop system. Credible hardware-inthe-loop experiments are conducted on a low-power DC-DC buck converter circuit to validate the aforementioned propositions.A lot of research has been done to devise robust and optimal voltage controllers for effectively regulating the v o of the buck converter, despite the exogenous disturbances in load impedance and input voltage. A detailed comparative performance analysis of the conventional and contemporary control techniques has been presented in previous tudies. 4-6 These controllers are divided in two categories, namely, model-free controllers and model-based controllers. The model-free controllers do not depend on the mathematical model of the converter. Hence, they are relatively simpler to construct. 7 Although they can significantly improve the transient and steady-state response of the system, they lack optimality in minimizing the deviations in state-trajectories or energy consumption. The proportional-integralderivative (PID) controllers, and their variants, are the most widely used model-free controllers in the industry. 8,9 However, evaluating a trivial set of controller gains to obtain optimal control effort is a cumbersome process. Several autotuning techniques for PID gains have been proposed in the literature to enhance the robustness of the controller design. [10][11][12][13][14] The fuzzy-logic controllers infer the control decision based on a heuristically synthesized set of logical rules. 15,16 However, these rules are empirically defined and hence cannot completely compensate all the nonlinearities associated with the system's dynamics. Other widely used control schemes include the sliding-mode controller, 17,18 fractional-order PID controller, 19 and adaptive controller. [20][21][22] The model-based controllers depend on the mathematical model of the system and thus use the complete knowledge of system dynamics to provide optimal control effort. 23 Se...

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This manuscript investigates the motion of a micropart on a dry nonlubricated controlled deformable surface considering the dynamically changing microforces while in contact with the surface. The motion analysis of a micropart on a flexible surface under controlled deformation is the first step to initiate feasibility of a micromanipulation device. At the micro/nanoscale, the surface force of attraction becomes more significant than the inertia force; thus motion analysis requires estimating and accommodating these forces in a dynamic model. The model considers microscale forces and surface roughness conditions (asperity deformation), while dynamically evaluating the friction coefficient and attraction force due to the dynamic asperity deformation as the micropart moves on a controlled deformation active surface. The parameters considered in the model include the micropart mass and size, the relative roughness between the micropart and surface, the surface and micropart material, and input actuator frequency, stroke, and deformation profile. The simulation results indicate that predictable micropart motion could be achieved but only within a certain range of input actuator frequencies. At lower frequencies no motion is possible while at higher frequencies the micropart detaches from the surface. The understanding of the effects of the microforces on the dynamic model and micropart motion would pave the way towards controlled micropart translocation and manipulation employing a flexible surface for microassembly or for processes requiring controlled micropart handling for heterogeneous microdevice mass production.