Browsing by Subject "Optimal Control"

Now showing 1 - 3 of 3
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    Advanced Control Strategies for the Robotic Hand
    (2017-12) Baz Khallouf, Ibrahim
    The research in this master’s thesis presents a new state-space representation of the nonlinear dynamics of two-link (thumb) and three-link (index) fingers of a robotic hand and an effective online solution of finite-time, nonlinear, closed-loop optimal control regulator and tracking problems using the state-dependent Riccati equations (SDRE). The technique involves the use of the solution of the algebraic Riccati equation for the in finite-time case (hence the technique is approximate) and the change of variables that converts a state-dependent, nonlinear, differential Riccati equation (SD-DRE) to a linear differential Lyapunov equation (DLE) which can be solved in closed form. The approximate technique is demonstrated by software simulation and hardware experimentation for the two-link and three-link fingers of the robotic hand.
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    Design and Analysis of Hydraulic Hybrid Passenger Vehicles
    (2015-09) Cheong, Kai Loon
    The research described in this dissertation focuses on the development of computationally efficient design methodology to optimize the hydraulic hybrid power-split transmission for fuel efficiency, acceleration performance and robustness against powertrain uncertainties. This research also involve experimental implementation of a three-level hierarchical control approach on two test beds, requiring powertrain control design and fine-tuning. Hybrid powertrains have the potential to benefit the fuel efficiency of highway and off-highway vehicles. Hydraulic hybrid has high power density. Hydraulic power-split architecture is chosen in this study for its flexibility in operation and combined advantage of series and parallel architecture. An approach for optimizing the configuration and sizing of a hydraulic hybrid power-split transmission is proposed. Instead of considering each mechanical configuration consisting of combinations of gear ratios, a generalized kinematic relation is used to avoid redundant computation. The Lagrange multiplier method for computing the optimal energy management control is shown to be 450 times more computationally efficient for use in transmission design iterations. To exploit the benefit of high power density of hydraulics, a classical multi-objective solver is utilized to incorporate the acceleration performance criteria into the transmission design optimization. By considering worst-case uncertainty, the transmission design is optimized to be robust against powertrain uncertainties and insensitive to operating condition variations, and yet fuel efficient. The Generation I and II vehicles are experimental platforms built to implement controls and to validate the fuel efficiency gain for power-split transmission. The powertrain for the platforms are modeled to predict the potential fuel efficiency improvement by different energy management strategies. Results show maximum of 74\% fuel efficiency gain by optimizing engine management from CVT to full optimal hybrid operation. The three-level control strategy is implemented on the Generation I vehicle. This control strategy segregates the tasks of the drive-train into three layers that respectively 1) manages the accumulator energy storage (high level); 2) performs vehicle level optimization (mid-level); and 3) attains the desired vehicle operating condition (low level). Results validated the modularity and effectiveness of this control structure.
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    Optimal Estimation and Control of Large Collaborative Swarms using Random Finite Set Theory
    (2019-09) Doerr, Bryce
    Controlling large swarms of robotic agents presents many challenges including, but not limited to, computational complexity due to a large number of agents, uncertainty in the functionality of each agent in the swarm, and uncertainty in the swarm's configuration. The contributions of this work is to form the Random Finite Set (RFS) control for large collaborative swarms, decentralize RFS control for individual agents, and form RFS control using other multi-agent RFS filters. The state representation of the large swarms with an unknown number of agents is generalized as an RFS where an RFS is a collection of agent states with no ordering between individual agents that can randomly change through time. The novelty of this idea is to generalize the notion of distance using RFS-based distance measures and "close-the-loop" between an estimating and controlling a swarm RFS. Specifically, multi-target estimation is determined using the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter which processes measurements from an unknown number of agents with defined spawn, birth, and death rates. RFS control is then compared for each distributional distance-based cost studied including the Cauchy-Schwarz, L2^2, and a modified L2^2 divergence using a model predictive control (MPC) based Quasi-Newton optimization. Next, RFS control and estimation is extended to MPC via iterative linear quadratic regulator (a variant of differential dynamic programming) for spacecraft swarms. The swarm is estimated in both cardinality (number of agents) and state using the GM-PHD filter which provides the estimates for RFS control. RFS control through ILQR approximates a quadratic value function from the distributional distance-based cost (i.e. the modified L2^2 divergence) to find an optimal control solution. This results in an implicit proof for RFS control of large collaborative swarms. The RFS control formulation assumes that the topology underlying the swarm control is complete and uses the complete graph in a centralized manner. To generalize the control topology in a localized or decentralized manner, sparse LQR is used to sparsify the RFS control gain matrix obtained using ILQR. This allows agents to use information of agents near each other (localized topology) or only the agent's own information (decentralized topology) to make a control decision. Sparsity and performance for decentralized RFS control are compared for different degrees of localization in feedback control gains which show that the stability and performance compared to centralized control do not degrade significantly in providing RFS control for large collaborative swarms. The GM-PHD filter is the most basic RFS-based filters used for estimation. Other RFS-based filters can improve the estimate or provide additional tracking information for RFS control by using either the Cardinalized Probability Hypothesis Density (CPHD) filter or the Generalized labeled Multi-Bernoulli (GLMB) filter, respectively. The CPHD filter generalizes the GM-PHD filter by jointly propagating a generalized cardinality distribution as well as the RFS to produce better estimates at high cardinality. The GLMB filter incorporates labels into the RFS, thus the GLMB filter is able to track individual trajectories of agents through time. Both these filters are propagated in feedback with RFS control for the spacecraft relative motion problem. Specifically, the MPC-based ILQR is implemented to provide swarm control in a centralized manner. By using the CPHD and GLMB filters, the cardinality and state estimates become more accurate for RFS control for large collaborative swarms.