UAV Model-based Flight Control with Artificial Neural Networks: A Survey

Model-Based Control (MBC) techniques have dominated flight controller designs for Unmanned Aerial Vehicles (UAVs). Despite their success, MBC-based designs rely heavily on the accuracy of the mathematical model of the real plant and they suffer from the explosion of complexity problem. These two challenges may be mitigated by Artificial Neural Networks (ANNs) that have been widely studied due to their unique features and advantages in system identification and controller design. Viewed from this perspective, this survey provides a comprehensive literature review on combined MBC-ANN techniques that are suitable for UAV flight control, i.e., low-level control. The objective is to pave the way and establish a foundation for efficient controller designs with performance guarantees. A reference template is used throughout the survey as a common basis for comparative studies to fairly determine capabilities and limitations of existing research. The end-result offers supported information for advantages, disadvantages and applicability of a family of relevant controllers to UAV prototypes

A brief review of neural networks based learning and control and their applications for robots

As an imitation of the biological nervous systems, neural networks (NN), which are characterized with powerful learning ability, have been employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification and patterns recognition etc. This article aims to bring a brief review of the state-of-art NN for the complex nonlinear systems. Recent progresses of NNs in both theoretical developments and practical applications are investigated and surveyed. Specifically, NN based robot learning and control applications were further reviewed, including NN based robot manipulator control, NN based human robot interaction and NN based behavior recognition and generation

Development of advanced autonomous learning algorithms for nonlinear system identification and control

Identification of nonlinear dynamical systems, data stream analysis, etc. is usually handled by autonomous learning algorithms like evolving fuzzy and evolving neuro-fuzzy systems (ENFSs). They are characterized by the single-pass learning mode and open structure-property. Such features enable their effective handling of fast and rapidly changing natures of data streams. The underlying bottleneck of ENFSs lies in its design principle, which involves a high number of free parameters (rule premise and rule consequent) to be adapted in the training process. This figure can even double in the case of the type-2 fuzzy system. From this literature gap, a novel ENFS, namely Parsimonious Learning Machine (PALM) is proposed in this thesis. To reduce the number of network parameters significantly, PALM features utilization of a new type of fuzzy rule based on the concept of hyperplane clustering, where it has no rule premise parameters. PALM is proposed in both type-1 and type-2 fuzzy systems where all of them characterize a fully dynamic rule-based system. Thus, it is capable of automatically generating, merging, and tuning the hyperplane-based fuzzy rule in a single-pass manner. Moreover, an extension of PALM, namely recurrent PALM (rPALM), is proposed and adopts the concept of teacher-forcing mechanism in the deep learning literature. The efficacy of both PALM and rPALM have been evaluated through numerical study with data streams and to identify nonlinear unmanned aerial vehicle system. The proposed models showcase significant improvements in terms of computational complexity and the number of required parameters against several renowned ENFSs while attaining comparable and often better predictive accuracy. The ENFSs have also been utilized to develop three autonomous intelligent controllers (AICons) in this thesis. They are namely Generic (G) controller, Parsimonious controller (PAC), and Reduced Parsimonious Controller (RedPAC). All these controllers start operating from scratch with an empty set of fuzzy rules, and no offline training is required. To cope with the dynamic behavior of the plant, these controllers can add, merge or prune the rules on demand. Among three AICons, the G-controller is built by utilizing an advanced incremental learning machine, namely Generic Evolving Neuro-Fuzzy Inference System. The integration of generalized adaptive resonance theory provides a compact structure of the G-controller. Consequently, the faster evolution of structure is witnessed, which lowers its computational cost. Another AICon namely, PAC is rooted with PALM's architecture. Since PALM has a dependency on user-defined thresholds to adapt the structure, these thresholds are replaced with the concept of bias- variance trade-off in PAC. In RedPAC, the network parameters have further reduced in contrast with PALM-based PAC, where the number of consequent parameters has reduced to one parameter per rule. These AICons work with very minor expert domain knowledge and developed by incorporating the sliding mode control technique. In G-controller and RedPAC, the control law and adaptation laws for the consequent parameters are derived from the SMC algorithm to establish a stable closed-loop system, where the stability of these controllers are guaranteed by using the Lyapunov function and the uniform asymptotic convergence of tracking error to zero is witnessed through the implication of an auxiliary robustifying control term. While using PAC, the boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Their efficacy is evaluated by observing various trajectory tracking performance of unmanned aerial vehicles. The accuracy of these controllers is comparable or better than the benchmark controllers where the proposed controllers incur significantly fewer parameters to attain similar or better tracking performance

비대칭 가변스팬 모핑 무인 항공기의 자체스케줄 파라미터 가변 제어

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2023. 2. 김유단.In this dissertation, a novel framework for flight control of a morphing unmanned aerial vehicle (UAV) is proposed. The proposed method uses asymmetric span morphing for lateral-directional motion control considering the dynamic characteristics of the morphing actuators while exploiting the advantages of symmetric span morphing for longitudinal flight performance enhancement. The proposed control system is self-scheduled based on linear parameter-varying (LPV) methods, which guarantees stability and performance for the variations of the morphing configuration and the flight condition. Therefore, the morphing UAV is allowed to swiftly metamorphose into the optimal configuration to maximize the system-level benefit according to the maneuvering command and the flight condition. First, a high-fidelity nonlinear model of an asymmetric variable-span morphing UAV is obtained from the NASA generic transport model. The impacts of morphing on the center of mass, inertia matrix, and aerodynamic coefficients are modeled based on the asymmetrically damaged wing model. The span variation ratios of the left and right wings are decomposed into symmetric and asymmetric morphing parameters, which are considered as the scheduling parameter and the control input, respectively. The nonlinear model is decoupled and linearized to obtain point-wise linear time-invariant (LTI) models for the longitudinal and lateral-directional motions throughout the grid points over the entire rectangularized scheduling parameter domain. The LPV model of the morphing UAV is derived for the longitudinal and lateral-directional motions by associating the family of LTI models through interpolation. Second, the longitudinal and lateral-directional control augmentation systems are designed based on LPV methods to track the normal acceleration command and the angle of sideslip and the roll rate commands, respectively. The inherent dynamic characteristics of the morphing actuator, such as low bandwidth, are considered in the control design procedure through a frequency-dependent weighting filter. The span morphing strategy to assist the intended maneuver is studied considering the impacts of morphing on various aspects. Numerical simulations are performed to demonstrate the effectiveness of the proposed control scheme for pushover-pullup maneuver and high-g turn. Finally, the longitudinal and lateral-directional autopilots are designed based on LPV methods to track the airspeed and altitude commands and the angle of sideslip and roll angle commands, respectively. A nonlinear guidance law is coupled with the autopilots to enable three-dimensional trajectory tracking. Numerical simulation results for the trajectory-tracking flight show that the proposed controller shows satisfactory performance, while the closed-loop system using the conventional gain-scheduled controller may lose stability when the scheduling parameter varies rapidly or widely.본 논문에서는 모핑 무인 항공기(unmanned aerial vehicle: UAV)의 비행 제어를 위한 새로운 프레임워크가 제안된다. 제안된 기법은 모핑 구동기의 동적 특성을 고려한 횡방향축(lateral-directional) 운동 제어를 위해 비대칭 스팬 모핑을 사용하고 종축(longitudinal) 비행 성능 향상을 위해 대칭 스팬 모핑의 이점을 활용한다. 또한 설계된 제어 시스템은 선형 파라미터 가변(linear parameter-varying: LPV) 기법을 기반으로 제어기 이득이 자체적으로 스케줄링 되며 모핑 형상 및 비행 조건의 임의의 변화에 대해 안정성과 성능을 엄밀하게 보장한다. 따라서 모핑 UAV는 기동 명령과 비행 조건에 따라 안정성을 상실할 우려 없이 시스템 수준의 이점을 극대화하는 동시에 내부 루프 안정화를 위한 제어에 기여하도록 최적의 형상으로 신속하게 변형될 수 있다. 첫째, NASA GTM(generic transport model)으로부터 비대칭 가변 스팬 모핑 UAV의 고충실도(high-fidelity) 비선형 모델이 획득된다. 모핑이 질량 중심, 관성 행렬 및 공기역학 계수에 미치는 영향은 날개가 비대칭적으로 손상된 모델을 기반으로 도출된다. 좌우 날개의 스팬 변화율은 대칭 및 비대칭 모핑 파라미터로 분해되며, 두 모핑 파라미터는 각각 스케줄링 파라미터 및 제어 입력으로 간주된다. 비선형 모델을 종축 및 횡방향축 운동으로 분리하고 직사각형 형태의 스케줄링 파라미터 영역의 각 격자점에서 선형화함으로써 각 점에 대한 선형 시불변(linear time-invariant: LTI) 모델이 얻어진다. LTI 모델 집합에 보간(interpolation)을 적용하면 종축 및 횡방향축 운동에 대한 모핑 UAV의 LPV 모델이 얻어진다. 둘째, 수직 가속도(normal acceleration) 명령과 옆미끄럼각(angle of sideslip) 및 롤 각속도 명령 추종을 위해 LPV 기법을 기반으로 종축 및 횡방향축 제어 증강 시스템(control augmentation system)이 설계된다. 이때, 제어 설계 과정에서 주파수종속(frequency-dependent) 가중치 필터를 통해 낮은 대역폭(bandwidth)과 같은 모핑 구동기 고유의 동적 특성이 고려된다. 또한 비행 특성에 대한 모핑의 다양한 영향을 고려하여 실행하고자 하는 기동을 보조하기 위한 스팬 모핑 전략이 논의된다. Pushover-pullup 기동 및 high-g turn에 대한 수치 시뮬레이션 결과를 통해 제안된 기법이 타당함을 확인할 수 있다. 마지막으로, 대기속도(airspeed) 및 고도 명령과 옆미끄럼각 및 롤 각 명령을 추종하기 위해 LPV 기법을 기반으로 종축 및 횡방향축 자동 조종 장치(autopilot)가 설계된다. 이때, 3차원 경로 추종을 위해 비선형 유도 법칙이 자동 조종 장치와 결합된다. 경로 추종 비행에 대한 수치 시뮬레이션 결과를 통해 스케줄링 파라미터의 변화 속도가 빠르거나 변화의 폭이 넓은 경우 일반적인 이득스케줄 제어기는 안정성을 상실할 수 있는 반면 제안된 기법은 만족할 만한 성능을 유지함을 확인할 수 있다.1 Introduction 1 1.1 Background and Motivation 1 1.2 Literature Review 6 1.2.1 Fixed-Wing Aircraft Implementing Morphing Technologies 6 1.2.2 Flight Control of Morphing Aircraft 7 1.2.3 Gain Scheduling Approaches to Controller Design 7 1.3 Objectives and Contributions 9 1.3.1 Objectives 9 1.3.2 Contributions 9 1.4 Dissertation Outline 11 2 Mathematical Preliminaries 13 2.1 LPV Systems 15 2.1.1 Taxonomy of Dynamical Systems 15 2.1.2 Definition of LPV Systems 15 2.1.3 LPV Modeling by Linearization 20 2.2 Gain Self-Scheduled Induced L2-Norm Control of LPV Systems 25 2.2.1 Norms of Signals and Systems 25 2.2.2 Analysis of LPV Systems 26 2.2.3 LPV Controller Design 30 2.2.4 Software for Synthesis and Analysis 30 3 Asymmetric Variable-Span Morphing UAV Model 33 3.1 Nonlinear Model of a Morphing UAV 36 3.1.1 Nominal Model of a Baseline Model 36 3.1.2 Morphing UAV Model 41 3.2 Derivation of an LPV Model of a Morphing UAV 52 3.2.1 Trim Analysis and Scheduling Parameter Selection 52 3.2.2 Pointwise Linearization of a Nonlinear Model 55 3.2.3 Linear Parameter-Varying Modeling and Analysis 58 4 CAS Design Based on LPV Method for Morphing-Assisted Maneuvers 61 4.1 Longitudinal CAS Design for Normal Acceleration Control 65 4.1.1 Performance Specifications 65 4.1.2 Controller Synthesis and Analysis 68 4.2 Lateral-Directional CAS Design for Turn Coordination and Roll Rate Control 73 4.2.1 Performance Specifications 73 4.2.2 Controller Synthesis and Analysis 75 4.3 Span Morphing Strategy 83 4.3.1 Effects of Span Morphing 83 4.3.2 Criteria for Span Variation 85 4.4 Nonlinear Simulation of Morphing-Assisted Maneuvers 86 4.4.1 High-Fidelity Flight Dynamics Simulator 86 4.4.2 Push-over and Pull-up 86 4.4.3 High-g Turn 89 5 Autopilot Design Based on LPV Methods for Morphing-Assisted Flights 109 5.1 Longitudinal Autopilot Design for Airspeed and Altitude Control 111 5.1.1 Performance Specifications 111 5.1.2 Controller Synthesis and Analysis 113 5.2 Lateral-Directional Autopilot Design for Turn Coordination and Roll Angle Control 121 5.2.1 Performance Specifications 121 5.2.2 Controller Synthesis and Analysis 123 5.3 Nonlinear Guidance Law for Trajectory Tracking 131 5.4 Nonlinear Simulation of Morphing-Assisted Flights 132 5.4.1 Waypoint Following at Low Altitude 132 5.4.2 Circular Trajectory Tracking at High Altitude 132 5.4.3 Helical Ascent under Fast Morphing 132 5.4.4 Spiral Descent with Morphing Scheduling 139 6 Conclusion 147 6.1 Concluding Remarks 147 6.2 Future Work 148박

NDI-based neurocontroller for unmanned combat aerial vehicles during aerial refuelling.

The success of Unmanned Combat Aerial Vehicles (UCAVs) requires further developments in the field of automated aerial refuelling (AAR) and control systems. AAR aircraft models identified thus far do not take the centre of gravity (cg) position movement into account during refuelling. A six-degree-of-freedom aircraft model was combined with a moving cg model for refuelling. The equations of motion for the aircraft in flight refuelling showed the aircraft dynamics to be coupled in the longitudinal and lateral-directional planes when the cg had moved away from the reference point. Applying assumptions specific to the flight conditions, simplified equations of motion were derived. Modal analysis of four cases for the linearised aircraft model during aerial refuelling was conducted. This revealed that the increase in mass was favourable to the stability of the Dutch Roll mode, but the mode did become more oscillatory initially as mass was increased, but as the cg moved forward, the mode became less oscillatory. The opposite was observed with the Phugoid mode. The Short Period Oscillation (SPO) decomposed into two first order modes during refuelling and these remained unchanged during the refuelling process. Three radial basis function (RBF) neural networks (RBFNN) were developed and trained to approximate the inverse plant dynamics and predicted commanded deflections of the elevator, aileron and rudder. Training data required for the network was randomly generated and the desired rates and commanded control surface deflections were computed. The training error was the smallest in the elevator deflection required during refuelling. A basic nonlinear dynamic inversion (NDI) controller without a neural network (NN) was designed for the aircraft. The performance of this controller was not satisfactory. The RBF was combined with the NDI to form a RBFNN-based controller. The longitudinal NDI RBFNN-based controller was less sensitive to modelling errors than the base NDI controller. The lateral NDI RBFNN-based controller’s performance was worse than the longitudinal controller, but showed potential as a technique for future consideration. Including the variation of aircraft inertia in the model has been recommended as further work, as well as exploring other neural network topologies in the NDI NN controller

Online parameter estimation of a miniature unmanned helicopter using neural network techniques

The online aerodynamic parameter estimation of a miniature unmanned helicopter using Neural Network techniques has been presented. The simulation model for the miniature helicopter was developed using the MATLAB/ SIMULINK software tool. Three trim conditions were analyzed: hover flight, 10m/s forward flight and 20m/s forward flight. Radial Basis Function (RBF) online learning was achieved using a moving window algorithm which generated an input-output data set at each time step. RBF network online identification was achieved with good robustness to noise for all flight conditions. However, the presence of atmospheric turbulence and sensor noise had an adverse effect on network size and memory usage. The Delta Method (DM) and the Modified Delta Method (MDM) was investigated for the NN-based online estimation of aerodynamic parameters. An increasing number high confidence estimated parameters could be extracted using the MDM as the helicopter transitioned from hover to forward flight

INTELLIGENT ESTIMATION IN DYNAMIC POSITIONING SYSTEMS OF MARINE VESSELS

Sustavi za dinamičko pozicioniranje plovnih objekata koriste se za održavanje njihove pozicije, smjera napredovanja i brzine, održavanje unaprijed definirane putanje gibanja, potpomognuto sidrenje i sl. Da bi se ove operacije uopće mogle provoditi, nužno je, između ostalog, omogućiti i određivanje precizne estimacije niskofrekventne pozicije, smjera napredovanja i brzine plovnog objekta, te estimaciju vjetrovnog i sporopromjenjivog opterećenja koje uzrokuju ostali vanjski poremećaji. U realnim sustavima za pozicioniranje plovnih objekata funkciju observera, tj. estimatora, ima neka od inačica Kalmanovog filtra koji ima već dugu tradiciju u brodskim sustavima upravljanja. U radu su analizirani klasični koncepti na kojima su temeljeni postojeći sustavi za dinamičko pozicioniranje te su istražene značajke dinamičkog pozicioniranja plovnih objekata s teoretske i praktične strane, posebno u dijelu koji se odnosi na problematiku filtriranja, identifikacije, estimacije i predikcije. Uočene su brojne prednosti, ali i nedostaci postojećih rješenja koji se mogu otkloniti primjenom novijih računalnih tehnologija kao što su algoritmi strojnog učenja i računalne inteligencije. Iz navedenih razloga, predložene su i konstruirane strukture statičkih, dinamičkih i hibridnih inteligentnih identifikatora i estimatora za potrebe identifikacije i estimacije u sustavima za dinamičko pozicioniranje. Od posebnog značaja su predloženi hibridni sustavi inteligentnih identifikatora i estimatora s proširenim Kalmanovim filtrom te inteligentni identifikatori za fuziju senzorskih informacija i rekonstrukciju signala u prekidu. Predloženi inteligentni identifikatori i estimatori su verificirani na realnim mjerenjima DP Log arhive dizaličara i cjevopolagača Saipem 7000 tijekom postupka polaganja cijevi na Projektu Ormen Lange (Norveška, 2006.).Dynamic positioning (DP) systems are used for maintaining position, heading and speed of the vessels, but also a predefined motion path, position mooring, etc. To ensure performing of these operations, it is necessary, among other things, to determine an accurate estimation of low-frequency position, heading and speed of the vessel. Additionally, it is necessary to ensure the estimation of wind and slowly-varying loads caused by other environmental disturbances. In actual DP systems, the vessel observer is usually an extended Kalman filter (EKF) which is traditionally used in marine control systems. In this doctoral thesis the classical base concepts of the existing commercial DP systems are analysed. Furthermore, the characteristics of DP systems are analysed both from the theoretical and practical point of view, especially in the part which is closely related to filtering, identification, estimation and prediction. Numerous advantages of existing solutions are identified, but also the several disadvantages which can be eliminated by using modern computational technologies such as machine learning and computational intelligence algorithms are pointed out. For these reasons, structures based on static, dynamic and hybrid intelligent identifiers and estimators have been proposed for the purpose of intelligent identification and estimation in DP systems. Proposed hybrid system of intelligent identifiers and estimators combined with EKF, as well as the intelligent identifiers for the sensor fusion and reconstruction of lost signals, are of particular interest. Intelligent identifiers and estimators are further adjusted, tested, and verified with real measurements from the DP Log archive of the heavy-lift and J-lay pipe vessel Saipem 7000

INTELLIGENT ESTIMATION IN DYNAMIC POSITIONING SYSTEMS OF MARINE VESSELS

Sustavi za dinamičko pozicioniranje plovnih objekata koriste se za održavanje njihove pozicije, smjera napredovanja i brzine, održavanje unaprijed definirane putanje gibanja, potpomognuto sidrenje i sl. Da bi se ove operacije uopće mogle provoditi, nužno je, između ostalog, omogućiti i određivanje precizne estimacije niskofrekventne pozicije, smjera napredovanja i brzine plovnog objekta, te estimaciju vjetrovnog i sporopromjenjivog opterećenja koje uzrokuju ostali vanjski poremećaji. U realnim sustavima za pozicioniranje plovnih objekata funkciju observera, tj. estimatora, ima neka od inačica Kalmanovog filtra koji ima već dugu tradiciju u brodskim sustavima upravljanja. U radu su analizirani klasični koncepti na kojima su temeljeni postojeći sustavi za dinamičko pozicioniranje te su istražene značajke dinamičkog pozicioniranja plovnih objekata s teoretske i praktične strane, posebno u dijelu koji se odnosi na problematiku filtriranja, identifikacije, estimacije i predikcije. Uočene su brojne prednosti, ali i nedostaci postojećih rješenja koji se mogu otkloniti primjenom novijih računalnih tehnologija kao što su algoritmi strojnog učenja i računalne inteligencije. Iz navedenih razloga, predložene su i konstruirane strukture statičkih, dinamičkih i hibridnih inteligentnih identifikatora i estimatora za potrebe identifikacije i estimacije u sustavima za dinamičko pozicioniranje. Od posebnog značaja su predloženi hibridni sustavi inteligentnih identifikatora i estimatora s proširenim Kalmanovim filtrom te inteligentni identifikatori za fuziju senzorskih informacija i rekonstrukciju signala u prekidu. Predloženi inteligentni identifikatori i estimatori su verificirani na realnim mjerenjima DP Log arhive dizaličara i cjevopolagača Saipem 7000 tijekom postupka polaganja cijevi na Projektu Ormen Lange (Norveška, 2006.).Dynamic positioning (DP) systems are used for maintaining position, heading and speed of the vessels, but also a predefined motion path, position mooring, etc. To ensure performing of these operations, it is necessary, among other things, to determine an accurate estimation of low-frequency position, heading and speed of the vessel. Additionally, it is necessary to ensure the estimation of wind and slowly-varying loads caused by other environmental disturbances. In actual DP systems, the vessel observer is usually an extended Kalman filter (EKF) which is traditionally used in marine control systems. In this doctoral thesis the classical base concepts of the existing commercial DP systems are analysed. Furthermore, the characteristics of DP systems are analysed both from the theoretical and practical point of view, especially in the part which is closely related to filtering, identification, estimation and prediction. Numerous advantages of existing solutions are identified, but also the several disadvantages which can be eliminated by using modern computational technologies such as machine learning and computational intelligence algorithms are pointed out. For these reasons, structures based on static, dynamic and hybrid intelligent identifiers and estimators have been proposed for the purpose of intelligent identification and estimation in DP systems. Proposed hybrid system of intelligent identifiers and estimators combined with EKF, as well as the intelligent identifiers for the sensor fusion and reconstruction of lost signals, are of particular interest. Intelligent identifiers and estimators are further adjusted, tested, and verified with real measurements from the DP Log archive of the heavy-lift and J-lay pipe vessel Saipem 7000

INTELLIGENT ESTIMATION IN DYNAMIC POSITIONING SYSTEMS OF MARINE VESSELS

Sustavi za dinamičko pozicioniranje plovnih objekata koriste se za održavanje njihove pozicije, smjera napredovanja i brzine, održavanje unaprijed definirane putanje gibanja, potpomognuto sidrenje i sl. Da bi se ove operacije uopće mogle provoditi, nužno je, između ostalog, omogućiti i određivanje precizne estimacije niskofrekventne pozicije, smjera napredovanja i brzine plovnog objekta, te estimaciju vjetrovnog i sporopromjenjivog opterećenja koje uzrokuju ostali vanjski poremećaji. U realnim sustavima za pozicioniranje plovnih objekata funkciju observera, tj. estimatora, ima neka od inačica Kalmanovog filtra koji ima već dugu tradiciju u brodskim sustavima upravljanja. U radu su analizirani klasični koncepti na kojima su temeljeni postojeći sustavi za dinamičko pozicioniranje te su istražene značajke dinamičkog pozicioniranja plovnih objekata s teoretske i praktične strane, posebno u dijelu koji se odnosi na problematiku filtriranja, identifikacije, estimacije i predikcije. Uočene su brojne prednosti, ali i nedostaci postojećih rješenja koji se mogu otkloniti primjenom novijih računalnih tehnologija kao što su algoritmi strojnog učenja i računalne inteligencije. Iz navedenih razloga, predložene su i konstruirane strukture statičkih, dinamičkih i hibridnih inteligentnih identifikatora i estimatora za potrebe identifikacije i estimacije u sustavima za dinamičko pozicioniranje. Od posebnog značaja su predloženi hibridni sustavi inteligentnih identifikatora i estimatora s proširenim Kalmanovim filtrom te inteligentni identifikatori za fuziju senzorskih informacija i rekonstrukciju signala u prekidu. Predloženi inteligentni identifikatori i estimatori su verificirani na realnim mjerenjima DP Log arhive dizaličara i cjevopolagača Saipem 7000 tijekom postupka polaganja cijevi na Projektu Ormen Lange (Norveška, 2006.).Dynamic positioning (DP) systems are used for maintaining position, heading and speed of the vessels, but also a predefined motion path, position mooring, etc. To ensure performing of these operations, it is necessary, among other things, to determine an accurate estimation of low-frequency position, heading and speed of the vessel. Additionally, it is necessary to ensure the estimation of wind and slowly-varying loads caused by other environmental disturbances. In actual DP systems, the vessel observer is usually an extended Kalman filter (EKF) which is traditionally used in marine control systems. In this doctoral thesis the classical base concepts of the existing commercial DP systems are analysed. Furthermore, the characteristics of DP systems are analysed both from the theoretical and practical point of view, especially in the part which is closely related to filtering, identification, estimation and prediction. Numerous advantages of existing solutions are identified, but also the several disadvantages which can be eliminated by using modern computational technologies such as machine learning and computational intelligence algorithms are pointed out. For these reasons, structures based on static, dynamic and hybrid intelligent identifiers and estimators have been proposed for the purpose of intelligent identification and estimation in DP systems. Proposed hybrid system of intelligent identifiers and estimators combined with EKF, as well as the intelligent identifiers for the sensor fusion and reconstruction of lost signals, are of particular interest. Intelligent identifiers and estimators are further adjusted, tested, and verified with real measurements from the DP Log archive of the heavy-lift and J-lay pipe vessel Saipem 7000

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field