Barrier Lyapunov function-based adaptive fuzzy attitude tracking control for rigid satellite with input delay and output constraint

This paper investigates the adaptive attitude tracking problem for the rigid satellite involving output constraint, input saturation, input time delay, and external disturbance by integrating barrier Lyapunov function (BLF) and prescribed performance control (PPC). In contrast to the existing approaches, the input delay is addressed by Pade approximation, and the actual control input concerning saturation is obtained by utilizing an auxiliary variable that simplifies the controller design with respect to mean value methods or Nussbaum function-based strategies. Due to the implementation of the BLF control, together with an interval notion-based PPC strategy, not only the system output but also the transformed error produced by PPC are constrained. An adaptive fuzzy controller is then constructed and the predesigned constraints for system output and the transformed error will not be violated. In addition, a smooth switch term is imported into the controller such that the finite time convergence for all error variables is guaranteed for a certain case while the singularity problem is avoided. Finally, simulations are provided to show the effectiveness and potential of the proposed new design techniques

Asymptotic Tracking Control of Uncertain MIMO Nonlinear Systems with Less Conservative Controllability Conditions

For uncertain multiple inputs multi-outputs (MIMO) nonlinear systems, it is nontrivial to achieve asymptotic tracking, and most existing methods normally demand certain controllability conditions that are rather restrictive or even impractical if unexpected actuator faults are involved. In this note, we present a method capable of achieving zero-error steady-state tracking with less conservative (more practical) controllability condition. By incorporating a novel Nussbaum gain technique and some positive integrable function into the control design, we develop a robust adaptive asymptotic tracking control scheme for the system with time-varying control gain being unknown its magnitude and direction. By resorting to the existence of some feasible auxiliary matrix, the current state-of-art controllability condition is further relaxed, which enlarges the class of systems that can be considered in the proposed control scheme. All the closed-loop signals are ensured to be globally ultimately uniformly bounded. Moreover, such control methodology is further extended to the case involving intermittent actuator faults, with application to robotic systems. Finally, simulation studies are carried out to demonstrate the effectiveness and flexibility of this method

Fractional Order Fault Tolerant Control - A Survey

In this paper, a comprehensive review of recent advances and trends regarding Fractional Order Fault Tolerant Control (FOFTC) design is presented. This novel robust control approach has been emerging in the last decade and is still gathering great research efforts mainly because of its promising results and outcomes. The purpose of this study is to provide a useful overview for researchers interested in developing this interesting solution for plants that are subject to faults and disturbances with an obligation for a maintained performance level. Throughout the paper, the various works related to FOFTC in literature are categorized first by considering their research objective between fault detection with diagnosis and fault tolerance with accommodation, and second by considering the nature of the studied plants depending on whether they are modelized by integer order or fractional order models. One of the main drawbacks of these approaches lies in the increase in complexity associated with introducing the fractional operators, their approximation and especially during the stability analysis. A discussion on the main disadvantages and challenges that face this novel fractional order robust control research field is given in conjunction with motivations for its future development. This study provides a simulation example for the application of a FOFTC against actuator faults in a Boeing 747 civil transport aircraft is provided to illustrate the efficiency of such robust control strategies

Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms

This book is a reprint of the Special Issue “Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms”,which was published in Applied Sciences

A COLLISION AVOIDANCE SYSTEM FOR AUTONOMOUS UNDERWATER VEHICLES

The work in this thesis is concerned with the development of a novel and practical collision avoidance system for autonomous underwater vehicles (AUVs). Synergistically, advanced stochastic motion planning methods, dynamics quantisation approaches, multivariable tracking controller designs, sonar data processing and workspace representation, are combined to enhance significantly the survivability of modern AUVs. The recent proliferation of autonomous AUV deployments for various missions such as seafloor surveying, scientific data gathering and mine hunting has demanded a substantial increase in vehicle autonomy. One matching requirement of such missions is to allow all the AUV to navigate safely in a dynamic and unstructured environment. Therefore, it is vital that a robust and effective collision avoidance system should be forthcoming in order to preserve the structural integrity of the vehicle whilst simultaneously increasing its autonomy. This thesis not only provides a holistic framework but also an arsenal of computational techniques in the design of a collision avoidance system for AUVs. The design of an obstacle avoidance system is first addressed. The core paradigm is the application of the Rapidly-exploring Random Tree (RRT) algorithm and the newly developed version for use as a motion planning tool. Later, this technique is merged with the Manoeuvre Automaton (MA) representation to address the inherent disadvantages of the RRT. A novel multi-node version which can also address time varying final state is suggested. Clearly, the reference trajectory generated by the aforementioned embedded planner must be tracked. Hence, the feasibility of employing the linear quadratic regulator (LQG) and the nonlinear kinematic based state-dependent Ricatti equation (SDRE) controller as trajectory trackers are explored. The obstacle detection module, which comprises of sonar processing and workspace representation submodules, is developed and tested on actual sonar data acquired in a sea-trial via a prototype forward looking sonar (AT500). The sonar processing techniques applied are fundamentally derived from the image processing perspective. Likewise, a novel occupancy grid using nonlinear function is proposed for the workspace representation of the AUV. Results are presented that demonstrate the ability of an AUV to navigate a complex environment. To the author's knowledge, it is the first time the above newly developed methodologies have been applied to an A UV collision avoidance system, and, therefore, it is considered that the work constitutes a contribution of knowledge in this area of work.J&S MARINE LT

Discrete Time Systems

Discrete-Time Systems comprehend an important and broad research field. The consolidation of digital-based computational means in the present, pushes a technological tool into the field with a tremendous impact in areas like Control, Signal Processing, Communications, System Modelling and related Applications. This book attempts to give a scope in the wide area of Discrete-Time Systems. Their contents are grouped conveniently in sections according to significant areas, namely Filtering, Fixed and Adaptive Control Systems, Stability Problems and Miscellaneous Applications. We think that the contribution of the book enlarges the field of the Discrete-Time Systems with signification in the present state-of-the-art. Despite the vertiginous advance in the field, we also believe that the topics described here allow us also to look through some main tendencies in the next years in the research area

Cooperative Control Reconfiguration in Networked Multi-Agent Systems

Development of a network of autonomous cooperating vehicles has attracted significant attention during the past few years due to its broad range of applications in areas such as autonomous underwater vehicles for exploring deep sea oceans, satellite formations for space missions, and mobile robots in industrial sites where human involvement is impossible or restricted, to name a few. Motivated by the stringent specifications and requirements for depth, speed, position or attitude of the team and the possibility of having unexpected actuators and sensors faults in missions for these vehicles have led to the proposed research in this thesis on cooperative fault-tolerant control design of autonomous networked vehicles. First, a multi-agent system under a fixed and undirected network topology and subject to actuator faults is studied. A reconfigurable control law is proposed and the so-called distributed Hamilton-Jacobi-Bellman equations for the faulty agents are derived. Then, the reconfigured controller gains are designed by solving these equations subject to the faulty agent dynamics as well as the network structural constraints to ensure that the agents can reach a consensus even in presence of a fault while simultaneously the team performance index is minimized. Next, a multi-agent network subject to simultaneous as well as subsequent actuator faults and under directed fixed topology and subject to bounded energy disturbances is considered. An H∞ performance fault recovery control strategy is proposed that guarantees: the state consensus errors remain bounded, the output of the faulty system behaves exactly the same as that of the healthy system, and the specified H∞ performance bound is guaranteed to be minimized. Towards this end, the reconfigured control law gains are selected first by employing a geometric control approach where a set of controllers guarantees that the output of the faulty agent imitates that of the healthy agent and the consensus achievement objectives are satisfied. Then, the remaining degrees of freedom in the selection of the control law gains are used to minimize the bound on a specified H∞ performance index. Then, control reconfiguration problem in a team subject to directed switching topology networks as well as actuator faults and their severity estimation uncertainties is considered. The consensus achievement of the faulty network is transformed into two stability problems, in which one can be solved offline while the other should be solved online and by utilizing information that each agent has received from the fault detection and identification module. Using quadratic and convex hull Lyapunov functions the control gains are designed and selected such that the team consensus achievement is guaranteed while the upper bound of the team cost performance index is minimized. Finally, a team of non-identical agents subject to actuator faults is considered. A distributed output feedback control strategy is proposed which guarantees that agents outputs’ follow the outputs of the exo-system and the agents states remains stable even when agents are subject to different actuator faults

Optimal nonlinear control and estimation using global domain linearization

Alan Turing teaches that cognition is symbol processing. Norbert Wiener teaches that intelligence rests on feedback control. Thus, there are discrete symbols and continuous sensory-motor signals. Sensorimotor dynamics are well-represented by nonlinear differential equations. A possible construction of symbols could be based on equilibria. Language is a symbol system and is one of the highest expressions of cognition. Much of this comes from spatial reasoning, which requires embodied cognition. Spatial reasoning derives from motor function. This thesis introduces a novel generalized non-heuristic method of linearizing nonlinear differential equations over a finite domain. It is used to engineer optimal convergence to target sets, a general form of spatial reasoning. Ordinary differential equations are ubiquitous models in physics and engineering that describe a wide range of phenomena including electromechanical systems. This thesis considers ordinary differential equations expressed in state-space form. For a given initial state, these equations generate signals that are continuous in both time and state. The control engineering objective is to find input functions that steer these states to desired target sets using only the measured output of the system. The state-space domain containing the target set, along with its cost, can be thought of as a symbol for high-level planning. Consider a basis state equal to a vector of nonlinear basis functions, computed from the state, where the state is generated from a multivariate nonlinear dynamic system. The basis state derivative can be expressed as a linear dynamic system with an additional error term. This thesis describes radial basis functions that minimize the error over the entire state-space domain, where the basis state equals zero if and only if the state is in a desired target set. This gives an approximate linear-dynamic system, and if the basis state goes to zero, then the state goes to the target set. This form of linear approximation is global over the domain. Careful selection of the basis gives a fully generalizable relationship between linear stability and nonlinear stability. This form of linearization can be applied to optimal state feedback and state estimation problems. This thesis carefully introduces optimal state feedback control with an emphasis on optimal infinite horizon solutions to linear-dynamic systems that have quadratic cost. A thorough introduction is also given to the optimal output feedback of linear-dynamics systems. The detectable and stabilizable subspaces of a linear-dynamic system are expressed in a generalized closed form. After introducing optimal control for linear systems, this thesis explores adaptive control from several different perspectives including: tuning, system identification, and reinforcement learning. Each of these approaches can be characterized as an optimal nonlinear output feedback problem. In each case, generalized representations can be found using a single layer of appropriately chosen nonlinear basis functions with linear parameterization. The primary focus of this thesis is to select these basis functions, in a fully generalized way, so that they have linear-dynamics. When this can be achieved, infinite horizon state feedback and state estimation can be computed using well-known closed-form solutions. This thesis demonstrates how multivariate nonlinear dynamic systems defined on a finite domain can be approximated by computationally equivalent high-dimensional linear-dynamic systems using a generalized basis state. This basis state is computed with a single layer of biologically inspired radial basis functions. The method of linearization is described as "global domain linearization" because it holds over a specified domain, and therefore provides a global linear approximation with respect to that domain. Any optimal state estimation or state feedback is globally optimal over the domain of linearization. The tools of optimal linear control theory can be applied. In particular, control and estimation problems involving under-actuated under-measured nonlinear systems with generalized nonlinear reward can be solved with closed-form infinite horizon linear-quadratic control and estimation. The controllable, uncontrollable, stabilizable, observable, unobservable, and detectable subspaces can all be described in a meaningful generalized way. State estimation and state feedback can then be implemented in computationally efficient low-dimensional highly nonlinear form. Generalized optimal state estimation and state feedback for continuous-time continuous-state systems is necessary machinery for any high-level symbolic planning that might involve unstable electromechanical systems. Symbols naturally form in the presence of more than one target state. This could provide a natural method of language acquisition. Given a state, all symbolic domains that intersect the state would have equilibrium and cost. These intersections define the legal grammar of symbol transition. An engineer or agent can design these symbols for high-level planning. Generalized infinite horizon state feedback and state estimation can then be computed for the continuous system that each symbol represents using traditional linear tools with domain linearization