Analog Devices Circuit Design tools are web based or downloadable but always free to use. The tool is easy to use and features an interactive user interface to quickly get you up and running.
Use the Analog Filter Wizard to design low-pass, high-pass, or band-pass filters with actual op amps in minutes. As you progress through the design process, you can observe the characteristics of your filter design from ideal specifications to real world circuit behavior. Quickly evaluate the tradeoffs in op amp specifications - including gain-bandwidth, noise, and supply current — to determine the best filter design for your requirements.
Watch Video. Use Photodiode Wizard to design a transimpedance amplifier circuit to interface with a photodiode. Select a photodiode from the library included in the tool, or enter custom photodiode specifications. Modify circuit parameters, and immediately see results in plots for pulse response, frequency response, and noise gain.
Instrumentation amplifier datasheets typically show a graph or several variations of the Output Swing vs Input Common-Mode Voltage, also known as the Diamond Plot, which is a comprehensive graph of all external and internal headroom limits. To speed and simplify your design process, TimerBlox Designer is an Excel based selection and synthesis tool that allows you to choose and configure the TimerBlox part best suited for your application.
Control, 43 4 , R7 Sanner, R. Neural Networks, 3 6 , Slotine Lectures on Nonlinear Systems. Lecture Duration Textbook and References 1.
Introduction Example 1. Basic Lyapunov Theory Sections 3. Lyapunov Stability Analysis Section 3. Convergence to Invariant Sets Sections 3. Stability of Time-Varying Systems Sections 4. Sliding Variables Section 7. Robust Control Sections 7. Adaptive Control Chapter 8 9. Robust Adaptive Control Exercise 8. Adaptive Robot Control Chapter 9 Feedback Linearization Section 6. A genetic algorithm GA [56, 57] is a search algorithm that is based on natural genetics.
A given problem is encoded as an array pop- ulation of artificial strings chromosomes. In the cases considered here, where an optimization problem has to be solved, the guesses for possible solutions are encoded. The GA is split into two parts: the first one is devoted to estimation of the time delay set and the second part is used for estimating the ODE model. In the model-selection part, different guesses for models are encoded, while in the delay-selection part, possible delay-combinations are encoded into binary strings.
The GA will then manipulate this representation of the solution, but not the solution itself. A GA also must have a criterion for discriminating good from bad solutions according to the fitness measure of these solutions. This criterion is used to guide the evo- lution towards future generations. In the case considered here, we use a complex criterion composed of different objectives that include stability of the model, topology, and, of course, similarity of the original and the generated time series.
After encoding the problem in a chromosomal manner and finding a discrim- ination strategy for good solutions, an initial population of encoded solutions is created. This is done by using a random number generator without any prior knowledge of possibly good solutions.
Time series a and DFT spectra b of the original vowel signal top bar and the signal generated by the estimated differential model in 30 bottom bar , and c embedding of the signal generated by the estimated differential model. The evolution of this initial population towards later generations is done by applying genetic operators in an iterative process. The most common genetic op- erators are a selection, b recombination, and c mutation [56, 57]. Selection allocates greater survival to better individuals.
Better solutions are preferred to worse ones. Additional new, possibly better, individuals not present in the origi- nal population have to be created. This is done via recombination and mutation. Recombination combines bits of parental solutions to form a better offspring. It combines parental traits in a novel manner. Mutation, on the other hand, mod- ifies a single individual. It is a random walk in the neighborhood of a particular solution.
The GA proposed here is implemented in two parts to solve the given opti- mization problem. The algorithm is initialized by selecting a first set of delays. If no a priori information on the delays is available, a first set of delays can be ob- tained by visually inspecting the embedded attractors. The algorithm then uses the model-selection-GA to optimize a system of ODEs while the delays are kept fixed.
Once the modeling error is minimized for the given delay set, the found model is fixed and the second, delay-selection-GA, is used to optimize over the delays. The process is repeated until the selected model and the delays do not change over a given number of iterations.
The flexibility of GAs allows us to design a strict and, at the same time, complex fitness criterion composed of four different objectives. The modeling error in our algorithm is defined as the least squares error weighed differentially over time to penalize later observations. The penalty for later observations is included because nonlinear systems can only be predicted within the Lyapunov time limit.
Since we are working with a single time series, which is also noisy, we typically can only make predictions within the time range which are con- siderably less then the Lyapunov limit. We further take into account that the dynamics can be different for selected data segments.
Our algorithm computes the modeling error from randomly selected data segments and the corresponding segments are integrated. A good model should also be stable when numerically integrated over long time intervals. The algorithm, therefore, automatically dis- cards all models that do not fulfil a long-term stability criterion. Yet, the fourth optimization constraint used is the topological equivalence of the model to the original embedded data.
This is implemented by comparing the topology, which we define as the density of the embedded input data with the corresponding integrated data in a two dimensional projection. The nonlinear series generated by the resulting global models not only produce the smallest point-to-point error to the original process, but also recover the topological properties of the embed- ded data. The GA approach allows us to implement this complex optimization criteria in a straightforward fashion.
GA for Modeling Vowel Signals. The aim here is to find the optimal ODE model for a given time series s n and a time delay embedding, with simultaneous optimization of the embedding lags, using a GA for model selection. Note, that the GA prefers models with as many coefficients ai , bj , ck as possible to be equal to zero. The minimal delay is set to 0 and the maximal one is The initial population size was set to Our GA increases the pop- ulation size, if for 2 generations no better individual was found and decreases the population size, if evolution was successful, but never below individu- als.
Since the number of possible models, together with the number of possible delay combinations is huge, no absolute convergence in reasonable time can be expected. To find a reasonable model, we start the GA a couple of times and then compare the resulting models. All these models are then fed into a new GA. During numerical integration the number of used digits was fixed to 6. A nonlinear dynamical system is very sensitive to small changes in the initial conditions.
Trajectories with slightly different initial conditions can exponentially diverge after a few cycles. Fixing the number of used digits during numerical integration adds a random component to the system. The reconstructed time-series as shown in Fig. When looking at the magnitude spectra in Fig.
The embeddings in Fig. To improve this model, a more general Ansatz in 31 could be used or the GA could be restricted to only models that are also part of the Ansatz library. The discrimination strategy for better models are obtained by minimizing the error of the model, i. The coefficients of the models are numerically estimated by a least square algorithm, which is in our case a singular value decomposition SVD [53]. The principal idea of minimizing a function using a GA can be found in [56].
Then a first set of delay s and the initial population of models are generated with a random number generator. The model-selection GA is applied and is stopped when the modeling error does not change for 5 iteration steps. The best model is selected and, starting from the initial population of delays, the delay-selection GA is applied and is stopped when the modeling error does not change for 5 iteration steps. Then the model-selection GA is applied again starting from the best models of former runs.
Differential embeddings of the signals in Fig. This alternative run of the two codes is stopped, when the modeling error remains constant for both parts of the GAs. The choice of the population-size is a critical point for a fast convergence to the global minimum of the solution space and should be related to the number of possible combinations of solutions. After some runs of our code, we found empirically that 0. Furthermore, we do not keep the population size constant, but change it dynamically during a run.
For instance, when the new generation has a better winner which is the same as in the former generation, the population size can be reduced. This could mean that the solution is possibly trapped in a local minimum. With a larger population size the escape from local minima towards the global one is accelerated. To find a good DDE model to characterize speech signals we first run this GA on a set of randomly chosen speech signal segments of data points each.
Here we do not aim to find a model that can be used for synthesis of speech, but for characterizing different features of the data. We therefore restrict our search to models with up to five terms and up to three delays where smaller models are preferred in the algorithm. Our finding is that three-delay models have on average about the same mod- eling error as two-delay models and therefore we choose to use only two delays for our analysis.
Furthermore a three-term model seems to characterize as many features as more term models. Therefore we use the three-term model that is the statistical winner of this run.
Note that this model was good for all different kinds of sounds and sound combinations since the signals for this run were randomly chosen from a set of speech signals from different speakers and sentences. The choice of LW tunes if we want to look at more or less global effects. For example emotional expressions in speech can be better seen if LW is larger and on the other hand the segmentations into phonemes requires a smaller LW.
For the optimal delays of each window the coefficients a1,2,3 are computed directly using SVD singular value decomposition. They are extremely good friends. The window length LW was points which corresponds to about 6 characteristic cycles.
Several things can be seen immediately: — delays M1,2 : The bigger one of the two delays, M2 has regions where it is somehow con- stant for some time.
The mean value and its deviation of these regions is characteristic for the speaker. Female speakers have a lower mean value than male speakers. The variance around the mean is characteristic for the speaker. It expresses the melody of speech. There is a direct connection to the fundamental frequency F0. The smaller one of the two delays, M1 can sometimes jump up to the second delay. This is a characteristics for emotions. This is the case for vowels, nasals and approximants. In some regions of the signal one of the coefficients, a1 has significantly smaller values, the second linear coefficient, a2 is not correlated to a2 , and the nonlinear coefficient a3 has nonzero values.
This is the characteristics for fricatives and affricates. Figure 19 shows the speech signal, the delays, the coefficients a1,2,3 , and the least square error. In the signal plot top bar of Fig. Figure 20 shows the part of Fig. The previously discussed characteristics are very clear in these plots.
This technique can also be used to segment speech signals into phonemes. For our modeling techniques different samples of certain vowels could be selected by such a DDE model and then fed into our modeling algorithms of Secs.
This could yield more realistic models than starting from recordings of sustained speech sounds. Part of Fig. For the oscillator model based on a time delay embedding some form of regu- larization of the nonlinear function model used in the oscillator has to be applied. Experiments with multiple objects using a prototype gripper with embedded sensing show that the proposed scheme is able to effectively immobilize novel objects within the gripper fingers.
Furthermore, it is seen that the adaptation allows for close estimation of the minimum grasp force required for safe grasping which results in minimal deformation of the grasped object.
In Chapter 4, we present the design and implementation of the motion controller and adaptive interface for the second generation of the UCF-MANUS intelligent assistive robotic manipulator system. Based on usability testing for the system, several features were implemented in the interface that could reduce the complexity of the human-robot interaction while also compensating for the deficits in different human factors, such as Working Memory, Response Inhibition, Processing Speed; , Depth Perception, Spatial Ability, Contrast Sensitivity.
For the controller part, we designed several new features to provide the user has a less complex and safer interaction with the robot, such as 'One-click mode', 'Move suggestion mode' and 'Gripper Control Assistant'. As for the adaptive interface design, we designed and implemented compensators such as 'Contrast Enhancement', 'Object Proximity Velocity Reduction' and 'Orientation Indicator'.
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