Robots that can adapt like animals

As robots leave the controlled environments of factories to autonomously function in more complex, natural environments, they will have to respond to the inevitable fact that they will become damaged. However, while animals can quickly adapt to a wide variety of injuries, current robots cannot "think outside the box" to find a compensatory behavior when damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots. Here we introduce an intelligent trial and error algorithm that allows robots to adapt to damage in less than two minutes, without requiring self-diagnosis or pre-specified contingency plans. Before deployment, a robot exploits a novel algorithm to create a detailed map of the space of high-performing behaviors: This map represents the robot's intuitions about what behaviors it can perform and their value. If the robot is damaged, it uses these intuitions to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a compensatory behavior that works in spite of the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new technique will enable more robust, effective, autonomous robots, and suggests principles that animals may use to adapt to injury

Pushing towards the Limit of Sampling Rate: Adaptive Chasing Sampling

Measurement samples are often taken in various monitoring applications. To reduce the sensing cost, it is desirable to achieve better sensing quality while using fewer samples. Compressive Sensing (CS) technique finds its role when the signal to be sampled meets certain sparsity requirements. In this paper we investigate the possibility and basic techniques that could further reduce the number of samples involved in conventional CS theory by exploiting learning-based non-uniform adaptive sampling. Based on a typical signal sensing application, we illustrate and evaluate the performance of two of our algorithms, Individual Chasing and Centroid Chasing, for signals of different distribution features. Our proposed learning-based adaptive sampling schemes complement existing efforts in CS fields and do not depend on any specific signal reconstruction technique. Compared to conventional sparse sampling methods, the simulation results demonstrate that our algorithms allow $46\%$ less number of samples for accurate signal reconstruction and achieve up to $57\%$ smaller signal reconstruction error under the same noise condition.Comment: 9 pages, IEEE MASS 201

System identification and adaptive current balancing ON/OFF control of DC-DC switch mode power converter

PhD ThesisReliability becomes more and more important in industrial application of Switch Mode Power Converters (SMPCs). A poorly performing power supply in a power system can influence its operation and potentially compromise the entire system performance in terms of efficiency. To maintain a high reliability, high performance SMPC effective control is necessary for regulating the output of the SMPC system. However, an uncertainty is a key factor in SMPC operation. For example, parameter variations can be caused by environmental effects such as temperature, pressure and humidity. Usually, fixed controllers cannot respond optimally and generate an effective signal to compensate the output error caused by time varying parameter changes. Therefore, the stability is potentially compromised in this case. To resolve this problem, increasing interest has been shown in employing online system identification techniques to estimate the parameter values in real time. Moreover, the control scheme applied after system identification is often called “adaptive control” due to the control signal selfadapting to the parameter variation by receiving the information from the system identification process. In system identification, the Recursive Least Square (RLS) algorithm has been widely used because it is well understood and easy to implement. However, despite the popularity of RLS, the high computational cost and slow convergence speed are the main restrictions for use in SMPC applications. For this reason, this research presents an alternative algorithm to RLS; Fast Affline Projection (FAP). Detailed mathematical analysis proves the superior computational efficiency of this algorithm. Moreover, simulation and experiment result verify this unique adaptive algorithm has improved performance in terms of computational cost and convergence speed compared with the conventional RLS methods. Finally, a novel adaptive control scheme is designed for optimal control of a DC-DC buck converter during transient periods. By applying the proposed adaptive algorithm, the control signal can be successfully employed to change the ON/OFF state of the power transistor in the DC-DC buck converter to improve the dynamic behaviour. Simulation and experiment result show the proposed adaptive control scheme significantly improves the transient response of the buck converter, particularly during an abrupt load change conditio

Development of Hybrid PS-FW GMPPT Algorithm for improving PV System Performance Under Partial Shading Conditions

A global maximum power point tracking (MPPT) algorithm hybrid based on Particle Swarm Fireworks (PS-FW) algorithm is proposed which is formed with Particle Swarm Optimization and Fireworks Algorithm. The algorithm tracks the global maximum power point (MPP) when conventional MPPT methods fail due to occurrence of partial shading conditions. With the applied strategies and operators, PS-FW algorithm obtains superior performances verified under simulation and experimental setup with multiple cases of shading patterns