M. S. Alam

Input-Shaping with GA-Tuned PID for Target Tracking and Vibration Reduction

This paper presents an investigation into the development of an augmented control scheme for vibration suppression and rigid body motion of a twin rotor multi-input multi-output system (TRMS) in hovering mode. The augmented control scheme comprises feedforward and feedback control methods. A 4-impulse input shaper is used as a feedforward control method to pre-process the command signal applied to the system, based on the identified modes of vibration. Two closed loop compensators based on PID and PID with acceleration feedback (PIDA) are designed and used as feedback controllers. Genetic algorithm (GA) optimization is also used in this work to tune the parameters of feedback compensators. A multi objective function is formulated within the augmented control scheme to improve the systems time domain response. Simulation results of the response of the TRMS with the controllers are presented in time and frequency domains. The performance of the proposed control scheme is assessed in terms of input tracking and level of vibration reduction. This is accomplished by comparing the system response to open loop system performance without the feedforward components (i.e. open loop system response to the unshaped input). The approach has shown to result in satisfactory vibration reduction. The GA-tuned controllers have shown a significant improvement in time domain specifications

Design of hybrid learning control for flexible manipulators: A multi-objective optimisation approach

This paper presents investigations at development of a design approach of a hybrid iterative learning control scheme for flexible robot manipulators using the multi-objective genetic algorithm (MOGA) approach. A single-link flexible manipulator system is considered in this work. This is a high order, nonlinear and single-input multi-output system with infinite number of modes each with associated damping ratios. Moreover, rise time, overshoot, settling time and end-point vibration are always in conflict in the flexible manipulator since the faster the motion, the larger the level of vibration. A collocated proportional-derivative (PD) controller utilising hub-angle and hub-velocity feedback is developed to control rigid-body motion of the system. This is then extended to incorporate iterative learning control with acceleration feedback to reduce the end-point acceleration of the system. The system performance largely depends on suitable selection of controller parameters. Single objective optimisation techniques can hardly provide good solution in such cases. Multi-objective GAs with fitness sharing technique is used to find optimal set of solutions for iterative learning control parameters, which trade off between these conflicting objectives. The performance of the hybrid learning control scheme is assessed in terms of time-domain specifications and level of vibration reduction at resonance modes.

Design Issues of Spring Brake Orthosis: Evolutionary Algorithm Approach

Spring Brake Orthosis (SBO) generates the swing phase of gait by employing a spring at the knee joint to store energy during the knee extension through quadriceps stimulation, which is then released to produce knee flexion. Spring parameters (for the knee flexion part) and the stimulus signal parameters (for the knee extension part) are the only optimizable quantities amongst the factors that determine the SBO generated knee joint trajectory. In this work, subject specific optimum spring parameters (spring constant, spring rest angle) for SBO purposes are obtained using genetic algorithms (GA). The integral of time-weighted absolute error (ITAE) between the reference and actual trajectory is defined as the cost function.

Genetic Algorithm Optimization and Control System Design of Flexible Structures

This paper presents an investigation into the deployment of genetic algorithm (GA)-based controller design and optimization for vibration suppression in flexible structures. The potential of GA is explored in three case studies. In the first case study, the potential of GA is demonstrated in the development and optimization of a hybrid learning control scheme for vibration control of flexible manipulators. In the second case study, an active control mechanism for vibration suppression of flexible beam structures using GA optimization technique is proposed. The third case study presents the development of an effective adaptive command shaping control scheme for vibration control of a twin rotor system, where GA is employed to optimize the amplitudes and time locations of the impulses in the proposed control algorithm. The effectiveness of the proposed control schemes is verified in both an experimental and a simulation environment, and their performances are assessed in both the time and frequency domains.

Design Constraints in Implementing Real-time Algorithms for a Flexible Manipulator System

This paper presents an investigation into the design constraints of the algorithm of a flexible manipulator system for real-time implementation. A dynamic simulation algorithm of a single link manipulator system using finite difference (FD) method is considered to demonstrate the critical real-time design and implementation issues. The simulation algorithm is analyzed, designed in various forms and implemented to explore the impact. Finally, a comparative real-time computing performance of various forms of the algorithms is presented and discussed to demonstrate the merits of different design mechanisms through a set of experiments.

Simulation and Experimental Studies of Hybrid Learning Control with Acceleration Feedback for Flexible Manipulators

This paper presents investigations at developing a hybrid iterative learning control scheme with acceleration feedback (PDILCAF) for flexible robot manipulators. An experimental flexible manipulator rig and corresponding simulation environment are used to demonstrate the effectiveness of the proposed control strategy. In this work the dynamic model of the flexible manipulator is derived using the finite element (FE) method. A collocated proportional-derivative (PD) controller utilizing hub-angle and hub-velocity feedback is developed for control of rigid-body motion of the system. This is then extended to incorporate iterative learning control with acceleration feedback and genetic algorithms (GAs) for optimization of the learning parameters for control of vibration (flexible motion) of the system. The system performance with the controllers is presented and analysed in the time and frequency domains. The performance of the hybrid learning control scheme without and with acceleration feedback is assessed in terms of input tracking, level of vibration reduction at resonance modes and robustness with various.

Adaptive IIR Filtering Techniques for Dynamic Modeling of a Twin Rotor System

This paper investigates the development of a parametric model to characterise pitch movement in a twin rotor multi-input multi-output system (TRMS) using adaptive infinite impulse response (IIR) models. The TRMS is a laboratory platform designed for control experiments. In certain aspects, its behaviour resembles that of a helicopter. It typifies a high-order nonlinear system with significant cross coupling between its two channels. It also simulates similar problems and challenges encountered in real systems. These include complex dynamics that lead to both parametric and dynamic uncertainty, unmeasurable states and sensor and actuator noise. In this work, adaptive IIR filtering techniques using least mean square (LMS) and recursive least square (RLS) algorithms are investigated for dynamic modelling of the system. The system is initially excited with random gaussian sequence input signal of sufficient bandwidth (0–10Hz) to ensure that all resonance modes of interest are captured. The magnitude of the input signal is selected so that it does not drive the system out of its linear operating range. Good excitation is achieved from 0–2.5 Hz, which includes all the important rigid body and flexible modes. Then, adaptive IIR filters based on equation error formulation are used for modelling the system. Three standard algorithms; namely, LMS, normalized LMS and RLS are utilized as learning algorithms, to update the parameters of the filter during the modelling process. A comparative assessment of the three learning algorithms, in characterising the system, is conducted. The performance of each model is assessed in terms of output tracking, minimization of the mean-square error, stability and algorithm convergence.Copyright