Combining Homotopy Methods and Numerical Optimal Control to Solve Motion Planning Problems

This paper presents a systematic approach for computing local solutions to motion planning problems in non-convex environments using numerical optimal control techniques. It extends the range of use of state-of-the-art numerical optimal control tools to problem classes where these tools have previously not been applicable. Today these problems are typically solved using motion planners based on randomized or graph search. The general principle is to define a homotopy that perturbs, or preferably relaxes, the original problem to an easily solved problem. By combining a Sequential Quadratic Programming (SQP) method with a homotopy approach that gradually transforms the problem from a relaxed one to the original one, practically relevant locally optimal solutions to the motion planning problem can be computed. The approach is demonstrated in motion planning problems in challenging 2D and 3D environments, where the presented method significantly outperforms a state-of-the-art open-source optimizing sampled-based planner commonly used as benchmark

Generalized predictive control based on particle swarm optimization for linear/nonlinear process with constraints

Abstract: This paper presents an intelligent generalized predictive controller (GPC) based on particle swarm optimization (PSO) for linear or nonlinear process with constraints. We propose several constraints for the plants from the engineering point of view and the cost function is also simplified. No complicated mathematics is used which originated from the characteristics ofPSO. This method is easy to be used to control the plants with linear or/and nonlinear constraints. Numerical simulations are used to show the performance of this control technique for linear and nonlinear processes, respectively. In the first simulation, the control signal is computed based on an adaptive linear model. In the second simulation, the proposed method is based on a fixed neural network model for a nonlinear plant. Both of them show that the proposed control scheme can guarantee a good control performance

Automatic Construction of Predictive Neuron Models through Large Scale Assimilation of Electrophysiological Data.

We report on the construction of neuron models by assimilating electrophysiological data with large-scale constrained nonlinear optimization. The method implements interior point line parameter search to determine parameters from the responses to intracellular current injections of zebra finch HVC neurons. We incorporated these parameters into a nine ionic channel conductance model to obtain completed models which we then use to predict the state of the neuron under arbitrary current stimulation. Each model was validated by successfully predicting the dynamics of the membrane potential induced by 20-50 different current protocols. The dispersion of parameters extracted from different assimilation windows was studied. Differences in constraints from current protocols, stochastic variability in neuron output, and noise behave as a residual temperature which broadens the global minimum of the objective function to an ellipsoid domain whose principal axes follow an exponentially decaying distribution. The maximum likelihood expectation of extracted parameters was found to provide an excellent approximation of the global minimum and yields highly consistent kinetics for both neurons studied. Large scale assimilation absorbs the intrinsic variability of electrophysiological data over wide assimilation windows. It builds models in an automatic manner treating all data as equal quantities and requiring minimal additional insight

BIBO stabilisation of continuous time takagi sugeno systems under persistent perturbations and input saturation

[EN] This paper presents a novel approach to the design of fuzzy state feedback controllers for continuous-time non-linear systems with input saturation under persistent perturbations. It is assumed that all the states of the Takagi¿Sugeno (TS) fuzzy model representing a non-linear system are measurable. Such controllers achieve bounded input bounded output (BIBO) stabilisation in closed loop based on the computation of inescapable ellipsoids. These ellipsoids are computed with linear matrix inequalities (LMIs) that guarantee stabilisation with input saturation and persistent perturbations. In particular, two kinds of inescapable ellipsoids are computed when solving a multiobjective optimization problem: the maximum volume inescapable ellipsoids contained inside the validity domain of the TS fuzzy model and the smallest inescapable ellipsoids which guarantee a minimum *-norm (upper bound of the 1-norm) of the perturbed system. For every initial point contained in the maximum volume ellipsoid, the closed loop will enter the minimum *-norm ellipsoid after a finite time, and it will remain inside afterwards. Consequently, the designed controllers have a large domain of validity and ensure a small value for the 1-norm of closed loop.The authors wish to thank the Editor-in-Chief and the anonymous reviewers for their valuable comments and suggestions. This work has been funded by Ministerio de Economia y Competitividad (Spain) through the research project DPI2015-71443-R and by Generalitat Valenciana (Valencia, Spain) through the research project GV/2017/029.Salcedo-Romero-De-Ávila, J.; Martínez Iranzo, MA.; Garcia-Nieto, S.; Hilario Caballero, A. (2018). BIBO stabilisation of continuous time takagi sugeno systems under persistent perturbations and input saturation. International Journal of Applied Mathematics and Computer Science (Online). 28(3):457-472. https://doi.org/10.2478/amcs-2018-0035S45747228

Autonomous Rendezvous with a non-cooperative satellite: trajectory planning and control

Con la nascita di nuove problematiche e nuove esigenze in ambito spaziale, le più importanti riguardanti il tema della mitigazione dei detriti spaziali o dell’assistenza e del servizio dei satelliti in orbita, lo scenario di rendez-vous autonomo tra un satellite inseguitore e un satellite target non cooperativo sta diventando sempre più centrale, ambizioso e accattivante. Il grande scoglio da superare, tuttavia, consiste nell’individuazione di una strategia di approccio robusta e vincente: mentre l’esecuzione di una manovra di rendez-vous e docking o cattura con satellite cooperativo è già stata collaudata e possiede una consolidata eredità di volo, il rendez-vous autonomo con satellite non cooperativo ed in stato di tombolamento è uno scenario agli albori, con pochi studi al riguardo. Lo scopo di questa tesi consiste nell’identificazione di una strategia di approccio che consideri le principali problematiche legate al tema in questione, ovvero la non-cooperazione e le scarse informazioni sullo stato di moto del target da raggiungere. Queste due complicazioni portano alla necessità di eseguire un moto di ispezione del satellite target e alla considerazione di numerosi vincoli nella progettazione della traiettoria di ispezione e di approccio. Un controllore adatto a trattare questo problema complesso e multi-vincolato è il Model Predictive Controller, in forma lineare o non lineare, abbinato ad un filtro di Kalman. La capacità di questo controllore di previsione e pianificazione di una traiettoria d’approccio, a partire da stime di posizione relativa tra target e inseguitore, permette di portare a termine la manovra in modo sicuro e robusto.According to the rise of new problems and new demands in the space field, the most important concerning the mitigation of space debris or the spacecraft on-orbit servicing and assistance themes, the Autonomous Rendezvous scenario between a chase satellite and a non-cooperative target satellite is becoming increasingly significant, ambitious, and attractive. The main issue to overcome, however, consists in the identification of a robust and successful approach strategy: while the execution of a rendezvous and docking or capture maneuver with a cooperative satellite has already been tested and holds a solid flight heritage, the autonomous rendezvous with a non-cooperative satellite in a state of tumbling motion is a scenario in the early days, with few studies about it and a not yet mature technology. The aim of this thesis consists in the identification of an approach strategy that deals with the main challenges related to the considered problem, namely non-cooperativeness and exiguous information about the target to be reached. These two issues lead to the need of performing an inspection motion and considering several constraints in the trajectory design. A controller suitable to handle this complex and multi-constrained problem is the Model Predictive Controller, in a linear or non-linear form, paired with a Kalman filter. The ability of this controller to predict and plan an approaching trajectory, starting from estimates of the relative position between the target and the chaser, allows to complete the approaching maneuver safely and in a robust way

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era