This paper introduces a novel approach for state-space representation of linear time invariant (LTI) systems, so-called Future Inputs Elimination (FIE) method. It can be applied to single-input-single-output (SISO) or multiple-input-multiple-output (MIMO) systems, continuous-time or discrete-time systems, whose dynamic equations are coupled or separated (uncoupled) in terms of their inputs and outputs. The FIE method closely parallels to the controllable canonical method when restricted to a class of SISO LTI systems. Moreover, it retains an easy implementation and effortless computation even for a class of MIMO LTI systems. The proposed approach may be used for representation of LTI systems with multiple or complex-conjugate poles. Many representative numerical examples are provided in order to illustrate the effectiveness of the elimination state-space method for representation of both SISO and MIMO LTI systems.

In this paper, we present a novel algorithm – DRGBT (Dynamic Rapidly-exploring Generalized Bur Tree), intended for motion planning in dynamic environments. The main idea behind DRGBT lies in a so-called adaptive horizon, consisting of a set of prospective target nodes that belong to a predefined $\mathcal{C}$-space path, which originates from the current node. Each node is assigned a weight that depends on relative distances and captured changes in the environment. The algorithm continuously uses a suitable horizon assessment to decide when to trigger the replanning procedure. A comprehensive simulation study is performed, covering a variety of manipulators, where DRGBT is compared to a state-of-the-art algorithm. Results indicate some promising features of the proposed method.

In this paper, a novel global optimization algorithm – Wingsuit Flying Search (WFS) is introduced. It is inspired by the popular extreme sport – wingsuit flying. The algorithm mimics the intention of a flier to land at the lowest possible point of the Earth surface within their range, i.e., a global minimum of the search space. This is achieved by probing the search space at each iteration with a carefully picked population of points. Iterative update of the population corresponds to the flier progressively getting a sharper image of the surface, thus shifting the focus to lower regions. The algorithm is described in detail, including the mathematical background and the pseudocode. It is validated using a variety of classical and CEC 2020 benchmark functions under a number of search space dimensionalities. The validation includes the comparison of WFS to several nature-inspired popular metaheuristic algorithms, including the winners of CEC 2017 competition. The numerical results indicate that WFS algorithm provides considerable performance improvements (mean solution values, standard deviation of solution values, runtime and convergence rate) with respect to other methods. The main advantages of this algorithm are that it is practically parameter-free, apart from the population size and maximal number of iterations. Moreover, it is considerably “lean” and easy to implement.