Modeling and identification of dynamic systems often is a prerequisite for the engineering of technical solutions, for example control system design. This paper presents an multi objective evolutionary approach for id...
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Modeling and identification of dynamic systems often is a prerequisite for the engineering of technical solutions, for example control system design. This paper presents an multi objective evolutionary approach for identification of dynamic systems of variable structure. The evolutionary algorithm employs domain specific operators in order to evolve the block oriented structure of the model and simultaneously optimize its parameters. Based on the observed inputs and outputs the multi objective method identifies an entire set of optimal compromise models which contrast model accuracy against complexity. The models are constructed from a set of basic blocks that capture phenomenons such as linear transfer functions, nonlinear gains and hysteresis that typically occur in mechanical, hydraulic and electrical systems. This representation enables the incorporation of domain knowledge in terms of building blocks and the interpretation of the identified model for further analysis and design. The feasibility of the proposed method is validated in the identification of an artificial dynamic system as well as a hydraulic proportional valve.
Abstract In this paper, the average consensus problem for multi-agent systems is addressed. A novel event-based control strategy is proposed which renders both control signals and state measurements, which are broadca...
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Abstract In this paper, the average consensus problem for multi-agent systems is addressed. A novel event-based control strategy is proposed which renders both control signals and state measurements, which are broadcast over the network, piecewise constant. This enables implementation on digital platforms such as microprocessors. Different triggering conditions guaranteeing convergence to an adjustable region around the average consensus point or asymptotic convergence to this point, respectively, are discussed. Numerical simulations show the effectiveness of this approach, outperforming traditional time-scheduled control in terms of load on the communication medium. Both single- and double-integrator agents are covered.
Abstract We consider an optimal control problem for networked controlsystems, where the loop is closed via a lossy, distributed network with an acknowledgment mechanism. The network is distributed in the sense that t...
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Abstract We consider an optimal control problem for networked controlsystems, where the loop is closed via a lossy, distributed network with an acknowledgment mechanism. The network is distributed in the sense that there are different sets of sensors and actuators that each communicate individually with the controller. We assume that all packets, i.e., the measurement packets, the control packets and the acknowledgment packets are sent over the lossy network and thus are subject to loss. We derive suboptimal controllers with respect to a quadratic cost criterion for the general case and optimal controllers for the case that all states are perfectly measured over a single link. Additionally, we present stability criteria for both cases.
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.
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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Abstract We consider inherent robustness properties of model predictive control (MPC) for continuous-time nonlinear systems with input constraints and terminal constraints. We show that when the linear quadratic contr...
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Abstract We consider inherent robustness properties of model predictive control (MPC) for continuous-time nonlinear systems with input constraints and terminal constraints. We show that when the linear quadratic control law is chosen as the terminal control law, and the related Lyapunov matrix is chosen as the terminal penalty matrix, MPC with nominal prediction model and bounded disturbances has some degree of inherent robustness. We emphasize that the input constraint sets can be any compact set rather than convex sets, and our results do not rely on the continuity of the optimal cost functional or control law in the interior of the feasible region.
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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