Transactions of the Institute of Measurement and Control

Liu, Ri; Sun, Xiuxia; Wang, Dong

doi: 10.1177/0142331215627003pmid: N/A

This paper investigates the problem of designing a novel adaptive sliding-mode controller for heavyweight airdrop operations. The design objective is to guarantee asymptotic tracking performance of the aircraft states, in the presence of bounded nonlinear uncertainties without prior knowledge of the bounds. On the basis of feedback linearization of the aircraft–cargo model, a sliding-mode control method with projection-based adaptive function approximation is proposed. This method uses an adaptation strategy to achieve robustness against model uncertainties, and a knowledge of the bounds on the complex uncertainties is not required. Notably, the adaptation law with projection can bound the estimated function, and this avoids singularity of the control signal. Simulations are conducted under the condition that one transport aircraft performs a maximum load airdrop mission at a height of 25 m, using single-row single-platform mode. The results verify the good properties of the control method, which can meet the airdrop mission performance indexes well in the presence of ±20% aerodynamic data uncertainty and 20% actuator fault.

Zhang, Youjian; Yang, Qiang; Yan, Wenjun

doi: 10.1177/0142331215579447pmid: N/A

This paper addresses the network-based leader–following consensus problem for the second-order multi-agent systems with nonlinear dynamics. Based on the Lyapunov–Krasovskii theory, a new delay-dependent sufficient condition in terms of linear matrix inequalities (LMIs) is presented to guarantee the consensus of the multi-agent system, and a sufficient condition for network-based controller design is proposed to ensure the followers reach consensus with the leader for second-order multi-agent systems with nonlinear dynamics. The effectiveness and applicability of the suggested solution is evaluated and verified through the simulation of two numerical examples.

Grozdanovic, Miroljub; Janackovic, Goran L; Stojiljkovic, Evica

doi: 10.1177/0142331215579948pmid: N/A

This paper presents an analytical–synthetic model of ergonomic research of the abilities of the dispatcher/operator in the control room for the automatic control of railway transportation. Twenty performance indicators describing the operators’ performance (work capacity and perceptive abilities) and the main characteristics of the control room (work organization and ergo-technical analysis) are identified. A group fuzzy analytic hierarchy process is applied in the process of ranking and selection of key performance indicators of railway control rooms. The selected key performance indicators (the operator’s hand movement, visual symptoms of fatigue, device error analysis, location and dimensions of the control desk) are analysed in detail.

Tutsoy, Onder; Brown, Martin

doi: 10.1177/0142331215581638pmid: N/A

Reinforcement learning was developed to solve complex learning control problems, where only a minimal amount of a priori knowledge exists about the system dynamics. It has also been used as a model of cognitive learning in humans and applied to systems, such as pole balancing and humanoid robots, to study embodied cognition. However, closed-form analysis of the value function learning based on a higher-order unstable test problem dynamics has been rarely considered. In this paper, firstly, a second-order, unstable balance test problem is used to investigate issues associated with the value function parameter convergence and rate of convergence. In particular, the convergence of the minimum time value function is analysed, where the minimum time optimal control policy is assumed known. It is shown that the temporal difference error introduces a null space associated with the experiment termination basis function during the simulation. As this effect occurs due to termination or any kind of switching in control signal, this null space appears in temporal differences (TD) error for more general higher-order systems. Secondly, the rate of parameter convergence is analysed and it is shown that residual gradient algorithm converges faster than TD(0) for this particular test problem. Thirdly, impact of the finite horizon on both the value function and control policy learning has been analysed in case of unknown control policy and added random exploration noise.

Song, Xiaona; Liu, Leipo; Tejado Balsera, Ines; Guo, Haigang

doi: 10.1177/0142331215583323pmid: N/A

In this study, the problem of fractional-order (FO) output feedback controller design for FO Takagi–Sugeno (TS) fuzzy systems with deterministic parameters and unmeasurable premise variables has been investigated, and the FO is in the range of 0 to 2. First, the FO TS fuzzy system is changed to an equivalent FO system with uncertain parameters. Then, a FO output feedback controller for the equivalent FO uncertain parameter system can be designed. In terms of linear matrix inequality, an explicit expression for the designed FO output feedback controller is found. Consequently, the FO TS fuzzy system is shown to be stabilized by the designed FO output feedback controller. Examples are included to demonstrate the effectiveness of the proposed method.

Cao, Quan; Zhong, Maiying

doi: 10.1177/0142331215584419pmid: N/A

A position and orientation systems (POS) plays an important role in aerial mapping applications. It integrates the inertial navigation system and global positioning system to provide high-precision position, velocity and attitude for various aerial mapping sensors. However, in severe environment of temperature, magnetic field and vibration in the application of aerial mapping, the precision of gyroscopes and accelerometers may degrade. The traditional Kalman filter may perform poorly when the model of gyroscope and accelerometer errors is uncertain. This paper highlights the use of multiple fading factors for a strong tracking Kalman filter (STKF) to accommodate the model uncertainty of gyroscope and accelerometer errors. Through utilizing the information of the sensitivity matrix of a two-stage Kalman filter, the multiple fading factors are obtained adaptively. Therefore, a more accurate covariance matrix is obtained in the proposed algorithm, and a better state tracking ability is achieved than with the Kalman filter and the STKF. Finally, a flight experiment is demonstrated to validate the effectiveness of the proposed algorithm. It is shown from the experimental results that the proposed algorithm can more accurately estimate the time-varying errors of gyroscopes and accelerometers than Kalman filters or the STKF; the accuracy of position, velocity and attitude of POS is also improved correspondingly.

Kumar, M. Siva; Anand, N. Vijaya; Rao, R. Srinivasa

doi: 10.1177/0142331215583326pmid: N/A

This research work proposes a method to obtain a stable reduced-order interval model from its stable higher-order interval plant. The reduced-order interval numerator and denominator polynomials are determined by using Kharitonov’s polynomials and a general form of the Routh approximation method. The proposed reduction algorithm retains stability and full impulse response energy of the higher-order interval system in its reduced-order interval model. In addition to this, the proposed method has useful features like matching of time moments and mathematical simplicity. Moreover, a few numerical examples in the literature are taken into consideration and simulated through MATLAB to illustrate the effectiveness of the proposed method.

Haj Salah, Ali Ameur; Garna, Tarek; Ragot, José; Messaoud, Hassani

doi: 10.1177/0142331215583101pmid: N/A

In this paper, we present the synthesis of a robust controller for uncertain discrete systems. The synthesis method of such a robust controller is the generalization of the Loop Shaping Design Procedure (LSDP) approach of McFarlane and Glover in the discrete case based on the work of Gu et al. We exploit the bilinear transform known as Tustin’s method in order to formulate the discrete loop shaping technique. A discrete weighting filter and a shaped discrete plant result from this technique. By taking into account the coprime factor uncertainty representation for the resulting shaped plant and by applying the small gain theorem, we define the concept of the robust stabilization of the discrete LSDP approach. This concept is based on the resolution of an optimization problem characterized by the maximum stability margin for the synthesis of the robust controller. To calculate the robust controller we transform this problem to a standard robust H∞ controller design based on the resolution of the Riccati equations. Also, we present the gap metric theory to characterize the controller’s robustness. We note that the resulting final controller is the combination of the discrete weighting filter and the robust controller. We propose then to exploit the recent work of Bouzrara et al. in order to develop a reduced robust controller by expanding the final controller on two independent Laguerre orthonormal bases. The discrete LSDP and the reduced controller approaches were validated on a Continuous Stirred Tank Reactor chemical reactor for a set of different equilibrium points in order to take into account the nonlinearities.

Du, Tao; Guo, Lei; Yang, Jian

doi: 10.1177/0142331216649019pmid: N/A

Initial alignment for a strap-down inertial navigation system (SINS) plays an important role in the following navigation and positioning operation. Initial alignment incorporates two stages: coarse and fine. This paper mainly investigates fine alignment for SINS under static base. A new fast SINS initial alignment scheme, a disturbance observer-based Kalman filter (DOBKF), is proposed to estimate the misalignment angles. As the name implies, the DOBKF is composed of a Kalman filter and a disturbance observer (DO). The Kalman filter is used to estimate horizontal misalignment angles, and the DO is applied to estimate the azimuth misalignment angle. In addition, when the estimations from the Kalman filter reach a steady state, they will be used as input for designing the DO. Compared with traditional filters, such as a Kalman filter used in initial alignment, the filter proposed by this paper not only greatly hastens the overall initial alignment process, but has comparable accuracy. Comparing simulation results shows that the proposed filter satisfies the requirement of SINS alignment.

Lei, Hang; Xing, Keyi; Gao, Zhenxin; Xiong, Fuli

doi: 10.1177/0142331215618445pmid: N/A

This paper proposes an effective hybrid discrete differential evolution (DDE) algorithm for solving a scheduling problem of flexible manufacturing systems (FMSs), where sequence-dependent setup times are considered. The objective is to find a deadlock-free schedule that minimizes the makespan. Based on the timed Petri net models of FMSs, a possible solution of the scheduling problem is represented as an individual that is a permutation with repetition of jobs. For the existence of deadlocks, most of the individuals cannot be directly decoded into feasible (live) schedules. Therefore, a deadlock controller is applied in the decoding scheme, and infeasible individuals are amended into feasible ones. Moreover, in order to overcome the premature convergence of DDE algorithm and improve solution quality, a variable neighbourhood search algorithm, which performs a systematic change of neighbourhood in solution searching, is adopted. Then a hybrid scheduling algorithm that combines a DDE with a variable neighbourhood search is presented. Computational results and comparison based on a variety of instances show the feasibility and superiority of the proposed algorithm.