This paper discusses current work on the input tracking control of single-link flexible manipulator systems. Joint-based control (JBC) is tuned using bacterial foraging algorithm (BFA). The objective of the work is to...
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This paper discusses current work on the input tracking control of single-link flexible manipulator systems. Joint-based control (JBC) is tuned using bacterial foraging algorithm (BFA). The objective of the work is to investigate the performance of the BFA with adaptable chemotactic step size in the control of a flexible manipulator in comparison to the original BFA. Lagrange equation discretised by finite difference (FD) approach is used to represent the dynamics of the flexible manipulator, incorporating payload with flexible mode associated damping and hub inertia. The FD method is chosen because it can represent flexible manipulator dynamics adequately well. The impact of two different cost functions is examined and discussed. The controller performance is evaluated based on the hub-angle output in time-domain.
The increased utilization of flexible structure systems, such as flexible manipulators and flexible aircraft in various applications, has been motivated by the requirements of industrial automation in recent years. Ro...
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ISBN:
(纸本)9781424466146;9780769540160
The increased utilization of flexible structure systems, such as flexible manipulators and flexible aircraft in various applications, has been motivated by the requirements of industrial automation in recent years. Robust optimal control of flexible structures with active feedback techniques requires accurate models of the base structure, and knowledge of uncertainties of these models. Such information may not be easy to acquire for certain systems. An adaptive Neuro-Fuzzy inference systems (ANFIS) use the learning ability of neural networks to adjust the membership function parameters in a fuzzy inference system. Hence, modelling using ANFIS is preferred in such applications. This paper discusses modelling of a nonlinear flexible system namely a twin rotor multi-input multi-output system using ANFIS techniques. Pitch and yaw motions are modelled and tested by model validation techniques. The obtained results indicate that ANFIS modelling is powerful to facilitate modelling of complex systems associated with nonlinearity and uncertainty.
This paper presents an active vibration control (AVC) scheme based on artificial immune system (AIS) algorithm with application to a thin plate system. A model-based control design approach, intergrating identificatio...
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This paper presents an active vibration control (AVC) scheme based on artificial immune system (AIS) algorithm with application to a thin plate system. A model-based control design approach, intergrating identification of the system model based on AIS is carried out to design the controller. The investigations concentrate on the first five resonance modes of the structure, and assesses the ability of developed control approach in attenuating the deflection of the plate structure. The performance of the AVC system is presented in both time and frequency domains with random, pseudo random binary sequence (PRBS) and finite duration step disturbance signals. Simulation results reveal that good performance is achieved with this approach.
control of vibration suppressions is crucial for applications in engineering particularly in the area of aircraft system. A hybrid control approach encompasses of using a feedforward intelligent command shaping techni...
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ISBN:
(纸本)9781424466146;9780769540160
control of vibration suppressions is crucial for applications in engineering particularly in the area of aircraft system. A hybrid control approach encompasses of using a feedforward intelligent command shaping technique with combination of intelligent PID feedback control is presented in this paper. The advantage of using command shaping is to reduce system vibration. However, it can cause delay in system response resulting to a conflict between vibration suppression and rise time response. Multi objective genetic algorithm is employ in this work to determine a set of solutions for the amplitudes and corresponding time locations of impulses on an extra sensitive (EI) command shaping as well as gain parameters for the PID controller. The effectiveness of the proposed technique is assessed both in the time domain and the frequency domain. Moreover, a comparative assessment of the performance of the technique with the system response and unshaped finite step input is presented.
This paper presents a recursive least-squares (RLS)-model based active vibration control (AVC) system with genetic algorithm (GA) self tuning. Tuning parameters include initial regression vectors, gain of RLS-model an...
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This paper presents a recursive least-squares (RLS)-model based active vibration control (AVC) system with genetic algorithm (GA) self tuning. Tuning parameters include initial regression vectors, gain of RLS-model and gain of controller. The system employs a single-input single-output (SISO) control configuration, and is realised within the Matlab/Simulink environment. In the paper, the parameters of RLS-AVC system are tuned so as to achieve effective vibration suppression. The approach is tested with pseudo random binary sequence (PRBS) disturbance signal acting on a plate to access the effectiveness of the approach in reducing the vibration of the plate.
In this paper, iterative learning control （ILC） design is studied for an iteration-varying tracking problem in which reference trajectories are generated by high-order internal models （HOLM）. An HOlM formulated as...
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In this paper, iterative learning control （ILC） design is studied for an iteration-varying tracking problem in which reference trajectories are generated by high-order internal models （HOLM）. An HOlM formulated as a polynomial operator between consecutive iterations describes the changes of desired trajectories in the iteration domain and makes the iterative learning problem become iteration varying. The classical ILC for tracking iteration-invariant reference trajectories, on the other hand, is a special case of HOlM where the polynomial renders to a unity coefficient or a special first-order internal model. By inserting the HOlM into P-type ILC, the tracking performance along the iteration axis is investigated for a class of continuous-time nonlinear systems. Time-weighted norm method is utilized to guarantee validity of proposed algorithm in a sense of data-driven control.
This work aims to develop and build a well designed two wheeled vehicle with specific features. Certain mechanical issues are discussed for a well designed mechanical prototype. The prototype is designed so as to be a...
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This work aims to develop and build a well designed two wheeled vehicle with specific features. Certain mechanical issues are discussed for a well designed mechanical prototype. The prototype is designed so as to be able to carry and move a payload in a vertical direction while maintaining a balanced condition of the vehicle on two wheels. Considering the various positions, speeds and different sizes of a payload, carried by the vehicle, while maintaining the entire vehicle balanced is the main anxiety of the vehicle design. An approach for modeling the system to characterize its dynamic behavior is also presented.
This paper presents a new algorithm for approximating a nonlinear function by means of local models. It is proposed a memory-based technique for selecting the best model configuration by comparing different alternativ...
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ISBN:
(纸本)9781424474325
This paper presents a new algorithm for approximating a nonlinear function by means of local models. It is proposed a memory-based technique for selecting the best model configuration by comparing different alternatives. A recursive technique for local model identification and validation is presented, together with an enhanced statistical method for model selection. The shapes and locations of receptive fields are changed in an adaptive manner. The learning capabilities are demonstrated by means of some examples.
This paper presents a Gaussian mixture probability hypothesis density (GM-PHD) smoother for tracking multiple maneuvering targets that follow jump Markov models. Unlike the generalization of the multiple model GM-PHD ...
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This paper presents a Gaussian mixture probability hypothesis density (GM-PHD) smoother for tracking multiple maneuvering targets that follow jump Markov models. Unlike the generalization of the multiple model GM-PHD filters, our aim is to approximate the dynamics of the linear Gaussian jump Markov system (LGJMS) by a best-fitting Gaussian (BFG) distribution so that the GM-PHD smoother can be carried out with respect to an approximated linear Gaussian system. Our approach is inspired by the recognition that the BFG approximation provides an accurate performance measure for the LGJMS. Furthermore, the multiple model estimation is avoided and less computational cost is required. The effectiveness of the proposed smoother is verified with a numerical simulation.
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