Abstract
Nowadays, there are several meta-heuristics algorithms which offer solutions for multi-variate optimization problems. These algorithms use a population of candidate solutions which explore the search space, where the leadership plays a big role in the exploration-exploitation equilibrium. In this work, we propose to use a Germinal Center Optimization algorithm (GCO) which implements temporal leadership through modeling a non-uniform competitive-based distribution for particle selection. GCO is used to find an optimal set of parameters for a neural inverse optimal control applied to all-terrain tracked robot. In the Neural Inverse Optimal Control (NIOC) scheme, a neural identifier, based on Recurrent High Orden Neural Network (RHONN) trained with an extended kalman filter algorithm, is used to obtain a model of the system, then, a control law is design using such model with the inverse optimal control approach. The RHONN identifier is developed without knowledge of the plant model or its parameters, on the other hand, the inverse optimal control is designed for tracking velocity references. Applicability of the proposed scheme is illustrated using simulations results as well as real-time experimental results with an all-terrain tracked robot.
Highlights
Nowadays, in computer science research is important to offer optimal techniques for a variety of problems, for most of these problems are difficult to formalize a mathematical model to optimize
For the inverse optimal control approach a stabilizing feedback control law based on a priori knowledge of a control Lyapunov function (CLF), is designed first, and it is established that this control law optimizes a cost functional, the Control Lyapunov Function (CLF) is modified in order to achieve asymptotic tracking for given trajectory references [15]
Germinal Center Optimization algorithm (GCO) is a hybridization between Evolutionary Computing and Artificial Immune System based on the Germinal Center reaction which is a biological process in vertebrates immune system that maturates affinity of antibodies
Summary
In computer science research is important to offer optimal techniques for a variety of problems, for most of these problems are difficult to formalize a mathematical model to optimize. EC algorithms offer an analogy of the competitive process in natural selection applied to multi-agent search for multi-variate problems, in the same way, AIS are based on the adaptive properties of the vertebrates immune system. The vertebrates immune system has been developed through time by natural selection to overcome many diseases, some of this protection mechanisms are inheritable, the immune system is capable of adapting to a new variety of Antigens (AGs) (foreign particles) in order to acquire specific protection [6] This specific protection is given by Antibodies (ABs) that attach to AGs with certain affinity in the so-called humoral immunity. Optimal Control (NIOC) scheme for this work, where Section 3.1 presents the RHONN identifier and the extended kalman filter (EKF) training, and in Section 3.1.2 the design of the inverse optimal control law is discussed.
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