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Angular acceleration sensors technologies are evolving and emerging in various applications. In this paper, we propose an angular accelerometer-based inertial navigation system. One application of such systems can be highly maneuvering platforms where direct measurement of angular acceleration is desired. To that end, a review of modern angular acceleration sensor technologies and their possible applications is provided. Two angular acceleration-

Redundant manipulators are mechanical systems with more degrees of freedom than required for their task. The paper considers the problem of energy minimization, given a required task, for such systems. The problem is formulated as a constrained optimal control with additional inequality constraints. A dynamic projection enables transforming the problem into an equivalent unconstrained, reduced order one. The solution scheme presented here combines the problems of path planning and tracking control. It includes decomposition of the problem into a bi-level structure. The parametric, higher-level problem is solved using a genetic algorithm and the lower level one is solved using optimal control. Comparison with full optimal control solutions shows the superiority of the combined evolutionary algorithm in terms of computational feasibility and overall energy savings.

Redundancy is a useful feature in dynamic systems which can be exploited to enhance performance in various tasks. In this work, redundancy will be utilized to minimize the energy consumption of a linear manipulator, while in some cases an additional task of end-effector tracking will also be required and achieved. Optimal control theory has been extensively used for the optimization of dynamic systems; however, complex tasks and redundancy make these problems computationally expensive, numerically difficult to solve, and in many cases, ill-defined. In this paper, evolutionary bilevel optimization for the problem is presented. This is done by setting up an upper level optimization problem for a set of decision variables and a lower level one that actually calculates the optimal inputs and trajectories. The upper level problem is solved by a genetic algorithm (GA), whereas the lower level problem uses classical optimal control. As a result, the proposed algorithm allows the optimization of complex tasks that usually cannot be solved in practice using standard optimal control tools. In addition, despite the use of penalty functions to enforce saturation constraints, the algorithm leads to global energy minimization. Illustrative examples of a redundant x-y robotic manipulator with complex overall tasks will be presented, solved, and discussed.