The problem of reconstructing an unknown disturbance under measuring a part of phase coordinates of a system of linear differential equations is considered. Solving algorithm is designed. The algorithm is based on the combination of ideas from the theory of dynamical inversion and the theory of guaranteed control. The algorithm consists of two blocks: the block of dynamical reconstruction of unmeasured coordinates and the block of dynamical reconstruction of an input.

This paper addresses weighted L2 gain performance switching controller design of discrete-time switched linear systems with average dwell time (ADT) scheme. Two kinds of methods, so called linearizing change-of-variables based method and controller variable elimination method, are considered for the output-feedback control with a supervisor enforcing a reset rule at each switching instant are considered respectively. Furthermore, some comparison between these two methods are also given.

Researchers have paid significant attention on hyperjerk systems, especial hyperjerk ones with chaos. A new hyperjerk system with seven terms and two parameters is analyzed. Chaotic attractors as well as coexisting attractors are displayed by the hyperjerk system. Thus it is a new multi-stable chaotic hyperjerk system. Further properties of the proposed hyperjerk system such as circuit design and backstepping-based control and synchronization are reported.

This paper proposes a generalized fractional controller for integer order systems with time delay. The fractional controller structure is so adopted to have a combined effect of fractional filter and Smith predictor. Interestingly, the resulting novel controller can be decomposed into fractional filter cascaded with an integer order PID controller. The method is applied to two practical examples i.e. liquid level system and Shell control fractionator system. The closed- loop responses resulting from the proposed method are compared with that of the available methods in the literature. For quantitative evaluations of the proposed method, Integral Absolute Error (IAE) and Integral Square Control Input (ISCI) performance criteria are employed. The proposed method effectively enhances the closed-loop response by improving the IAE values, reducing the control effort inputs to achieve the desired output. The disturbance rejection and robustness tests are also carried out. The robustness test reveals a significant improvement in the maximum absolute sensitivity measure. That is displayed in numerical simulations of the paper.

In global path planning (GPP), an autonomous underwater vehicle (AUV) tracks a predefined path. The main objective of GPP is to generate a collision free sub-optimal path with minimum path cost. The path is defined as a set of segments, passing through selected nodes known as waypoints. For smooth planar motion, the path cost is a function of the path length, the threat cost and the cost of diving. Path length is the total distance travelled from start to end point, threat cost is the penalty of collision with the obstacle and cost of diving is the energy expanse for diving deeper in ocean. This paper addresses the GPP problem for multiple AUVs in formation. Here, Grey Wolf Optimization (GWO) algorithm is used to find the suboptimal path for multiple AUVs in formation. The results obtained are compared to the results of applying Genetic Algorithm (GA) to the same problem. GA concept is simple to understand, easy to implement and supports multi-objective optimization. It is robust to local minima and have wide applications in various fields of science, engineering and commerce. Hence, GA is used for this comparative study. The performance analysis is based on computational time, length of the path generated and the total path cost. The resultant path obtained using GWO is found to be better than GA in terms of path cost and processing time. Thus, GWO is used as the GPP algorithm for three AUVs in formation. The formation follows leader-follower topography. A sliding mode controller (SMC) is developed to minimize the tracking error based on local information while maintaining formation, as mild communication exists. The stability of the sliding surface is verified by Lyapunov stability analysis. With proper path planning, the path cost can be minimized as AUVs can reach their target in less time with less energy expanses. Thus, lower path cost leads to less expensive underwater missions.