Abstract Indirect methods solve optimal control problems for hybrid systems with high precision, but they are difficult to initialize. To overcome the initialization difficulties, two different concepts based on direc...
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Abstract Indirect methods solve optimal control problems for hybrid systems with high precision, but they are difficult to initialize. To overcome the initialization difficulties, two different concepts based on direct methods are presented. In the first approach, the hybrid optimal control problem is solved by a direct method until the precision is sufficient for a successful initialization of the indirect method. The second approach decomposes the hybrid optimal control problem into non-hybrid subproblems, where each subproblem can be initialized separately by a direct method. This results in a significantly higher robustness of the initialization compared to the first approach. However, the precision of the solution with the indirect method achieved in the first approach is higher. The two concepts are compared in a numerical example.
In this paper, a knowledge-based Artificial Fish-Swarm (AFA) optimization algorithm with crossover, CAFAC, is proposed to enhance the optimization efficiency and combat the blindness of the search of the AFA. In our C...
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The magnetic shape memory (MSM) alloys are well-promising for the controlled magnetic field-induced motion. This paper describes the observer-based inverse hysteresis control of a prototypic MSM based actuating elemen...
The magnetic shape memory (MSM) alloys are well-promising for the controlled magnetic field-induced motion. This paper describes the observer-based inverse hysteresis control of a prototypic MSM based actuating element The MSM induced motion is a novel and challenging topic for mechatronics due to a high integrity of the magnetic and mechanical components as well as a complex nonlinear dynamic behavior to be controlled. A highly hysteretic and thereto non-deterministic (to the certain degree) response of the field-induced strain is the main challenge when regulating the MSM generated motion. The way, how the MSM type actuators can be controlled robustly using the observer-based inverse hysteresis approach is described and shown with experiments.
This paper describes and compares different observer-based control strategies for compensating the dynamic friction in controlled motion systems. The once identified system with friction requires usually an observer d...
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This paper describes and compares different observer-based control strategies for compensating the dynamic friction in controlled motion systems. The once identified system with friction requires usually an observer due to unknown disturbances and time variant friction behavior. The recently developed two-state dynamic friction model with elasto-plasticity is applied within three different types of observer, all involved in the control loop. Each of the observer-based compensation schemes as well as the friction model in feed-forwarding augments a standard linear feedback velocity control which serves for evaluation as the reference one. The performance of the realized control approaches is evaluated on an ordinary electro-mechanical actuator system with multiple coupled sources of friction without its direct measurement.
The magnetic shape memory (MSM) alloys are interesting candidates among active materials used in the actuators, particularly due to a macroscopic relationship between the applied magnetic field and the resulting MSM s...
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The magnetic shape memory (MSM) alloys are interesting candidates among active materials used in the actuators, particularly due to a macroscopic relationship between the applied magnetic field and the resulting MSM strain. A system oriented description of MSM actuators is quite challenging caused by the inherently nonlinear hysteretic behavior of the MSM transducer. This paper describes a modeling and identification approach which combines the second-order linear actuator dynamics with the novel two-inputs nonlinear MSM model. The proposed identification strategy allows to decompose the linear and nonlinear effects observable in the actuator response under certain excitation conditions. The experimental evaluation performed on a prototypic MSM actuator reveals the model suitability, particularly by capturing the state-dependent memory phenomena of MSM hysteresis.
This paper presents a novel approach for floor obstacle segmentation in omnidirectional images which rests upon the fusion of multiple classification generated from heterogeneous segmentation schemes. The individual n...
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This paper presents a novel approach for floor obstacle segmentation in omnidirectional images which rests upon the fusion of multiple classification generated from heterogeneous segmentation schemes. The individual naive Bayes classifiers rely on different features and cues to determine a pixel’s class label. Ground truth data for training and testing the classifiers is obtained from the superposition of 3D scans captured by a photonic mixer device camera. The classification is supported by edge detection which indicate the presence of obstacles and sonar range data. The complementary expert decisions are aggregated by stacked generalization, behavior knowledge space or voting combination. The combined floor classifier achieves a classification accuracy of up to 0.96 true positive rate with only 0.03 false positive rate. A robust robot navigation is accomplished by arbitration among a reactive obstacle avoidance and a corridor following behavior using the robots local free space as perception.
control of systems where the information between the controller, actuator, and sensor can be lost or delayed can be challenging with respect to stability and performance. One way to overcome the resulting problems is ...
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A license plate recognition system based on neural networks was designed and *** system used a neural-network chip to recognize license *** chip combined video image processing module with neural network module by usi...
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ISBN:
(纸本)9781457702686
A license plate recognition system based on neural networks was designed and *** system used a neural-network chip to recognize license *** chip combined video image processing module with neural network module by using equalized image processing algorithm and network classification algorithm.A set of interface circuit was developed for implementing license-plate-number *** results show the system can guarantee a very low error rate at an acceptable recognition time.
The design of visual robotic behaviors constitutes a substantial challenge. It requires to draw meaningful relationships and constraints between the acquired visual perception and the geometry of the environment both ...
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The design of visual robotic behaviors constitutes a substantial challenge. It requires to draw meaningful relationships and constraints between the acquired visual perception and the geometry of the environment both empirically and programmatically. This contribution proposes a novel robot learning framework to classify and acquire scenario specific autonomous behaviors through demonstration. During demonstration, robocentric 3D range and omnidirectional images are recorded as training instances of typical robot navigation situations pertaining to different contexts in multiple indoor scenarios. A programming by demonstration approach generalizes the demonstrated trajectories to a general mapping between visual features extracted from the omnidirectional image onto a corresponding robot motion. The approach is able to distinguish among different traversing scenarios and further identifies the best matching context within the scenario to predict an appropriate robot motion. As a comparison to context matching, the behaviors are trained by means of an artificial neural network and its generalization ability is evaluated against the former. The experimental validation on the mobile robot indicates that the acquired visual behavior is robust and generalizes meaningful actions beyond the specific environments and scenarios presented during training.
This paper considers optimized network topology design and distributed control for linear discrete-time systems consisting of subsystems interconnected through states, inputs, and a cost function. By using a distribut...
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ISBN:
(纸本)9781612848006
This paper considers optimized network topology design and distributed control for linear discrete-time systems consisting of subsystems interconnected through states, inputs, and a cost function. By using a distributed control law, which makes use of the communicated states of other subsystems, closed-loop performance is increased at the expense of communication costs. This raises the question of how to find a topology and associated distributed control law with optimal trade-off between communication costs and closed-loop performance. As an answer to this question, we propose an approach to simultaneous optimization of network topology and control law with respect to a cost function which combines a quadratic performance criterion with costs associated to the presence of communication links. The problem is formulated as mixed-integer semi-definite problem (MISDP) where the discrete optimization of the network topology subject to communication constraints and embedded subproblems for structured controller synthesis lead to an upper bound for the combined cost. An example is used to illustrate the method.
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