Thrust Vector Controller Comparison for a Finless Rocket

The paper focuses on comparing applicability, tuning, and performance of different controllers implemented and tested on a finless rocket during its boost phase. The objective was to evaluate the advantages and disadvantages of each controller, such that the most appropriate one would then be developed and implemented in real-time in the finless rocket. The compared controllers were Linear Quadratic Regulator (LQR), Linear Quadratic Gaussian (LQG), and Proportional Integral Derivative (PID). To control the attitude of the rocket, emphasis is given to the Thrust Vector Control (TVC) component (sub-system) through the gimballing of the rocket engine. The launcher is commanded through the control input thrust gimbal angle δ , while the output parameter is expressed in terms of the pitch angle θ . After deriving a linearized state–space model, rocket stability is addressed before controller implementation and testing. The comparative study showed that both LQR and LQG track pitch angle changes rapidly, thus providing efficient closed-loop dynamic tracking. Tuning of the LQR controller, through the Q and R weighting matrices, illustrates how variations directly affect performance of the closed-loop system by varying the values of the feedback gain (K). The LQG controller provides a more realistic profile because, in general, not all variables are measurable and available for feedback. However, disturbances affecting the system are better handled and reduced with the PID controller, thus overcoming steady-state errors due to aerodynamic and model uncertainty. Overall controller performance is evaluated in terms of overshoot, settling and rise time, and steady-state error

Evolino for recurrent support vector machines

Traditional Support Vector Machines (SVMs) need pre-wired finite time windows to predict and classify time series. They do not have an internal state necessary to deal with sequences involving arbitrary long-term dependencies. Here we introduce a new class of recurrent, truly sequential SVM-like devices with internal adaptive states, trained by a novel method called EVOlution of systems with KErnel-based outputs (Evoke), an instance of the recent Evolino class of methods. Evoke evolves recurrent neural networks to detect and represent temporal dependencies while using quadratic programming/support vector regression to produce precise outputs. Evoke is the first SVM-based mechanism learning to classify a context-sensitive language. It also outperforms recent state-of-the-art gradient-based recurrent neural networks (RNNs) on various time series prediction tasks.Comment: 10 pages, 2 figure

Learning basic navigation for personal satellite assistant using neuroevolution

The Personal Satellite Assistant (PSA) is a small robot proposed by NASA to assist astronauts who are living and working aboard the space shuttle or space station. To help the astronaut, it has to move around safely. Navigation is made difficult by the arrangement of thrusters. Only forward and leftward thrust is available and rotation will introduce translation. This paper shows how stable navigation can be achieved through neuroevolution in three basic navigation tasks: (1) Stopping autorotation, (2) Turning 90 degrees, and (3) Moving forward to a position. The results show that it is possible to learn to control the PSA stably and efficiently through neuroevolution

Evolving symmetric and modular neural networks for distributed control

Problems such as the design of distributed controllers are character-ized by modularity and symmetry. However, the symmetries use-ful for solving them are often difficult to determine analytically. This paper presents a nature-inspired approach called Evolution of Network Symmetry and mOdularity (ENSO) to solve such prob-lems. It abstracts properties of generative and developmental sys-tems, and utilizes group theory to represent symmetry and search for it systematically, making it more evolvable than randomly mu-tating symmetry. This approach is evaluated by evolving controllers for a quadruped robot in physically realistic simulations. On flat ground, the resulting controllers are as effective as those having hand-designed symmetries. However, they are significantly faster when evolved on inclined ground, where the appropriate symmetries are difficult to determine manually. The group-theoretic symmetry mutations of ENSO were also significantly more effective at evolv-ing such controllers than random symmetry mutations. Thus, ENSO is a promising approach for evolving modular and symmetric solu-tions to distributed control problems, as well as multiagent systems in general

Evolutionary bilevel optimization for complex control problems and blackbox function optimization

textMost optimization algorithms must undergo time consuming parameter tuning in order to solve complex, real-world control tasks. Parameter tuning is inherently a bilevel optimization problem: The lower level objective function is the performance of the control parameters discovered by an optimization algorithm and the upper level objective function is the performance of the algorithm given its parameterization. In the first part of this thesis, a new bilevel optimization method called MetaEvolutionary Algorithm (MEA) is developed to discover optimal parameters for neuroevolution to solve control problems. In two challenging benchmarks, double pole balancing and helicopter hovering, MEA discovers parameters that result in better performance than hand tuning and other automatic methods. In the second part, MEA tunes an adaptive genetic algorithm (AGA) that uses the state of the population every generation to adjust parameters on the fly. Promising experimental results are shown for standard blackbox benchmark functions. Thus, bilevel optimization in general and MEA in particular are promising approaches for solving difficult optimization tasks.Computer Science

Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes

I argue that data becomes temporarily interesting by itself to some self-improving, but computationally limited, subjective observer once he learns to predict or compress the data in a better way, thus making it subjectively simpler and more beautiful. Curiosity is the desire to create or discover more non-random, non-arbitrary, regular data that is novel and surprising not in the traditional sense of Boltzmann and Shannon but in the sense that it allows for compression progress because its regularity was not yet known. This drive maximizes interestingness, the first derivative of subjective beauty or compressibility, that is, the steepness of the learning curve. It motivates exploring infants, pure mathematicians, composers, artists, dancers, comedians, yourself, and (since 1990) artificial systems.Comment: 35 pages, 3 figures, based on KES 2008 keynote and ALT 2007 / DS 2007 joint invited lectur

Understanding Evolutionary Potential in Virtual CPU Instruction Set Architectures

We investigate fundamental decisions in the design of instruction set architectures for linear genetic programs that are used as both model systems in evolutionary biology and underlying solution representations in evolutionary computation. We subjected digital organisms with each tested architecture to seven different computational environments designed to present a range of evolutionary challenges. Our goal was to engineer a general purpose architecture that would be effective under a broad range of evolutionary conditions. We evaluated six different types of architectural features for the virtual CPUs: (1) genetic flexibility: we allowed digital organisms to more precisely modify the function of genetic instructions, (2) memory: we provided an increased number of registers in the virtual CPUs, (3) decoupled sensors and actuators: we separated input and output operations to enable greater control over data flow. We also tested a variety of methods to regulate expression: (4) explicit labels that allow programs to dynamically refer to specific genome positions, (5) position-relative search instructions, and (6) multiple new flow control instructions, including conditionals and jumps. Each of these features also adds complication to the instruction set and risks slowing evolution due to epistatic interactions. Two features (multiple argument specification and separated I/O) demonstrated substantial improvements int the majority of test environments. Some of the remaining tested modifications were detrimental, thought most exhibit no systematic effects on evolutionary potential, highlighting the robustness of digital evolution. Combined, these observations enhance our understanding of how instruction architecture impacts evolutionary potential, enabling the creation of architectures that support more rapid evolution of complex solutions to a broad range of challenges