Transactions of the Institute of Measurement and Control

A common approach to understanding and controlling robotic legged locomotion is the construction and analysis of simplified mathematical models that capture essential features of locomotor behaviours. However, the representational power of such simple mathematical models is inevitably limited due to the non-linear and complex nature of biological locomotor systems. Attempting to identify and explicitly incorporate key non-linearities into the model is challenging, increases complexity, and decreases the analytic utility of the resulting models. In this paper, we adopt a data-driven approach, with the goal of furnishing an input–output representation of a locomotor system. Our method is based on approximating the hybrid dynamics of a legged locomotion model around its limit cycle as a Linear Time Periodic (LTP) system. Perturbing inputs to the locomotor system with small chirp signals yield the input–output data necessary for the application of LTP system identification techniques, allowing us to estimate harmonic transfer functions (HTFs) associated with the local LTP approximation to the system dynamics around the limit cycle. We compare actual system responses with responses predicted by the HTF, providing evidence that data-driven system identification methods can be used to construct models for locomotor behaviours.

In this research study, a single axis (inner azimuth gimbal) of a four-axis gyro-stabilized electro optic gimbal system is modelled through experimental investigation in the frequency domain, and the results of this investigation are presented. The dynamic behaviour of the mechanical system is obtained from input and output signals, strictly speaking, nonparametric measurements. Detecting and measuring the nonlinear distortions allows a better understanding and gives an intuitive insight into the error sources on frequency response function measurements. In this study, a nonparametric frequency response function and its uncertainty (noise) are measured; nonlinear distortions are quantified on a real-time system. The dynamic system is modelled by its parametric transfer function with various different estimation techniques and their efficiencies, convergence properties, and bias errors are compared and discussed. It turns out that, the nonparametric noise model allows the estimator to weight the cost function and reach statistically better results. As an original contribution, a common problem encountered with gimbals having small rotational movement capacity (in this case study, ±5° for the inner gimbals) is formulated as an optimization problem. An optimization procedure is studied to achieve a better signal-to-noise ratio in the frequency band of interest, while satisfying device-specific constraints.

Control of aerial robots is a popular research field as applications with different payloads lead to a variety of flight missions. Quadrotor-type unmanned systems are one such example considered in this paper. The performance in any flight experiment depends strictly on the chosen feedback control scheme, which is the core issue addressed in the paper. A number of approaches have been reported in the literature and this paper presents a survey of these schemes with an in-depth discussion of recent research outcomes. A detailed performance evaluation of the controllers, namely proportional-integral-derivative control, sliding mode control, backstepping control, feedback linearization-based control and fuzzy control schemes, are presented. Due to the popularity of the quadrotor-type aerial vehicles, the contribution of the current work is to provide an in-depth guide to the autopilot designers of quadrotor-type unmanned aerial vehicles.

This paper presents the very first field-programmable gate array (FPGA) implementation of the Runge–Kutta model predictive control (RKMPC) mechanism to the real-time experimental electromagnetic levitation system, which is an unstable nonlinear continuous-time system with a very small time constant. In the control mechanism, the so called Runge–Kutta model of the nonlinear system is employed as an approximate discrete-time model of the system and used in the model predictive control loop for prediction and derivative calculation purposes. Experimental results have shown the effectiveness of the proposed implementation under different control conditions. Moreover, the RKMPC has been compared to a conventional nonlinear model predictive control (NMPC) method to show the advantages of the RKMPC over the NMPC.

This study investigates dynamic energy price regulation by a closed-loop fractional-order PI control system and presents a possible application for the automated energy balancing in smart grid energy markets. A persistent balance of energy demand and generation is a substantial problem for future smart grids due to the uncertainty and high fluctuation in the generation of distributed renewable energy sources and elastic demand conditions. Dynamic energy pricing is an effective strategy to balance flexible energy markets and it can provide the best energy price that balances energy demand and generation. We numerically demonstrate that closed-loop generation control by using dynamic pricing can provide persistent settling to the best price point of demand and supply curves, when the energy balance error is defined as the difference between instant demand and generation potentials. We analyse energy market management performance of a fractional-order PI controller in the case of communication and operation delays in a multi-source energy market model. Market simulation results are discussed to demonstrate the possible advantages of the fractional-order PI controller for smart grid energy market managements.

This paper is an extended version of the paper presented at TOK 2014 (Turkish Automatic Control National Meeting) which examined the determination of Sugeno type fuzzy model parameters optimized by the artificial bee colony (ABC) algorithm for a microstrip antenna. This paper presents a performance comparison of the Sugeno and Mamdani type fuzzy models proposed for nonlinear system modelling. To determine the parameters of the fuzzy models, the ABC algorithm is used. For this purpose, several nonlinear system examples which given in the literature were considered, and the results obtained by the optimized fuzzy models were compared with the other modelling approaches in the literature. Simulation results demonstrate that the use of the ABC algorithm provides a remarkable contribution to the model’s performance.

This paper addresses the path planning problem of multiple unmanned aerial vehicles (UAVs). The paths are planned to maximize collected amount of information from desired regions (DRs), while avoiding forbidden regions (FRs) and reaching the destination. This study focuses on maximizing collected information instead of minimizing total mission time, as in previous studies. The problem is solved by a genetic algorithm (GA) with the proposal of novel evolutionary operators. The initial populations are generated from a seed-path for each UAV. The seed-paths are obtained both by utilizing the pattern search method and by solving the multiple-Traveling Salesman Problem (mTSP). Utilizing the mTSP solves both the visiting sequences of DRs and the assignment problem of ‘which DR should be visited by which UAV?’ All of the paths in the population in any generation of the GA are constructed using a dynamical UAV model. Simulations are realized in a MATLAB/Simulink environment for different mission scenarios and the results provide physically realizable flight paths, which visit DRs and avoid FRs. Real-world experiments are conducted by using small UAVs, which are constructed by autopilot integration on model airplanes. Flight tests performed based on simulated scenarios proved beneficial in maximizing the collected amount of information for multiple UAV missions.

This sequential paper aims to present studies on modelling and tip tracking control of a flexible single beam. It first outlines the flexible-beam robotic mechanism that was designed and built to be used for the force and torque sensory information-based modelling and control. It then details the vibration suppression controller strategy that is applied to this robotic system. The controller is designed with respect to a simple lumped model describing the dynamics of the system. Here the dynamics of the closed-loop controlled motor is inverted in order to obtain a system with unity dynamics. Further, the flexible-beam dynamics is input state linearized. Finally, a simple external feedback control, which is based on the measurements of beam deflections using a force and torque sensor, is implemented. The complete experimental setup was positioned by two servo-motors controlled by a proportional-integral-derivative controller for each axis. The proposed controllers allow the flexible beam to move continuously in a precise manner, so that it could be treated as an accurate positioning sensor. Simulation and experimental results provided at the end illustrate that the controllers designed and implemented produce a satisfactory control performance and adequate robustness to model uncertainties and system nonlinearities.