Neural MRAC based on modified state observer

A new model reference adaptive control design method with guaranteed transient performance using neural networks is proposed in this thesis. With this method, stable tracking of a desired trajectory is realized for nonlinear system with uncertainty, and modified state observer structure is designed to enable desired transient performance with large adaptive gain and at the same time avoid high frequency oscillation. The neural network adaption rule is derived using Lyapunov theory, which guarantees stability of error dynamics and boundedness of neural network weights, and a soft switching sliding mode modification is added in order to adjust tracking error. The proposed method is tested by different theoretical application problems simulations, and also Caterpillar Electro-Hydraulic Test Bench experiments. Satisfying results show the potential of this approach --Abstract, page iv

System theory as applied differential geometry

The invariants of input-output systems under the action of the feedback group was examined. The approach used the theory of Lie groups and concepts of modern differential geometry, and illustrated how the latter provides a basis for the discussion of the analytic structure of systems. Finite dimensional linear systems in a single independent variable are considered. Lessons of more general situations (e.g., distributed parameter and multidimensional systems) which are increasingly encountered as technology advances are presented

Observation and control of a ball on a tilting

The ball and plate system is a nonlinear MIMO system that has interesting characteristics which are also present in aerospace and industrial systems, such as: instability, subactuation, nonlinearities such as friction, backlash, and delays in the measurements. In this work, the modeling of the system is based on the Lagrange approach. Then it is represented in the state-space form with plate accelerations as inputs to the system. These have a similar effect as applying torques. In addition, the use of an internal loop of the servo system is considered. From the obtained model, we proceed to carry out the analysis of controllability and observability resulting in that the system is globally weak observable and locally controllable in the operating range. Then, the Jacobi linearization is performed to use the linearized model in the design of linear controllers for stabilization. On the other hand, analyzing the internal dynamics of the ball and plate system turns out to be a non-minimum phase system, which makes it difficult to design the tracking control using the exact model. This is the reason why we proceed to make approximations. Using the approximate model, nonlinear controllers are designed for tracking using different approaches as: feedback linearization for tracking with and without integral action, backstepping and sliding mode. In addition, linear and nonlinear observers are designed to provide full state information to the controller. Simulation tests are performed comparing the different control and observation approaches. Moreover, the effect of the delay in the measurement is analyzed, where it is seen that the greater the frequency of the reference signal the more the error is increased. Then, adding the Smith predictor compensates the delay and reduces the tracking error. Finally, tests performed with the real system. The system was successfully controlled for stabilization and tracking using the designed controllers. However, it is noticed that the effect of the friction, the spring oscillation and other non-modeled characteristics significantly affect the performance of the control.Tesi

Results on data-driven controllers for unknown nonlinear systems

The big data revolution is deeply changing the way we understand and analyze natural phenomena around us. In the field of control engineering, data-driven control enables researchers to explore new intelligent algorithms to model and control complex dynamical systems. Data-driven control is based on the paradigm of learning controllers of an unknown dynamical system by directly using data. The underlying idea is that information about the model can be gathered from experiments, bypassing completely the identification step, which can be impractical or too costly. This thesis presents data-driven control solutions for different families of unknown dynamical systems, with a focus on both linear and special classes of nonlinear ones. In the first part of the thesis, we consider the linear quadratic regulator problem for linear time-invariant discrete-time systems. The system is assumed to be unknown and information on the system is given by a finite set of data. This allows determining the optimal control law in one shot, with no intermediate identification step. Secondly, we present an online algorithm for learning controllers applied to switched linear systems. By collecting data on the fly, the control mechanism can capture any changes in the dynamics of the plant and adapt itself accordingly to achieve stabilization of the running dynamics. Finally, we derive data-driven methods for a more general class of nonlinear systems via nonlinearity cancellation. To this end, we make use of a "dictionary" of nonlinear terms that includes the nonlinearities of the unknown system

Quadcopter: Design, modelling, control and trajectory tracking

A quadcopter is a type of unmanned aerial vehicles (UAV). The industry of this type of UAVs is growing exponentially in terms of new technology development and the increase of potential applications that may cover construction inspections, search and rescue, surveillance, aerial photography, monitoring, mapping, etc. A quadcopter is a nonlinear and under-actuated system that introduces complex aerodynamics properties and create challenges which demands the development of new, reliable and effective control techniques to enhance the stability of flight control, plan and track a desired trajectory while minimizing the effect induced by the operational environment and its own sensors. Hence, many control techniques have been developed and researched. Some of such developments work well with the provision of having an accurate mathematical model of the system while other work is associated with a mathematical model that can accommodate certain level of wind disturbances and uncertainties related to measurement noise. Moreover, various linear, nonlinear and intelligent control techniques were developed and recognized in the literature. Each one of such control techniques has some aspect that excels in under certain conditions. The focus of this thesis is to develop different control techniques that can improve flight control stability, trajectory tracking of a quadcopter and evaluate their performance to select the best suitable control technique that can realize the stated technical flight control requirements. Accordingly, three main techniques have been developed: Standard PID, Fuzzy based control technique that tune PID parameters in real time (FPID) and a Hybrid control strategy that consists of three control techniques: (a) FPID with state coordinates transformation (b) State feedback (c) Sliding mode The configuration of the hybrid control strategy consists of two control loops. The inner control loop aims to control the quadcopter\u27s attitude and altitude while the outer control loop aims to control the quadcopter\u27s position. Two configurations were used to configure the developed control techniques of the control loops. These configurations are: (a) A sliding mode control is used for the outer loop while for the inner loop two control techniques are used to realize it: a Fuzzy gain scheduled PID with state coordinates transformation and a state feedback control. (b) Fuzzy gain scheduled PID control is used for the outer loop while for the inner loop two control techniques are used to realize it using the same formation as in (a) above. Furthermore, in order to ensure a feasible desired trajectory before tracking it, a trajectory planning algorithm has been developed and tested successfully. Subsequently, a simulation testing environment with friendly graphical User Interface (GUI) has been developed to simulate the quadcopter mathematical model and then to use it as a test bed to validate the developed control techniques with and without the effect of wind disturbance and measurement noise. The quadcopter with each control technique has been tested using the simulation environment under different operational conditions. The results in terms of tracking a desired trajectory shows the robustness of the first configuration of control techniques within the hybrid control strategy under the presence of wind disturbance and measurement noise compared to all the other techniques developed. Then, the second configuration of the control techniques came second in terms of results quality. The third and fourth results in the sequence shown by the fuzzy scheduled PID and the standard PID respectively. Finally, Validating the simulation results on a real system, a quadcopter has been successfully designed, implemented and tested. The developed control techniques were tested using the implemented quadcopter and the results were demonstrated and compared with the simulation results

NASA Aircraft Controls Research, 1983

The workshop consisted of 24 technical presentations on various aspects of aircraft controls, ranging from the theoretical development of control laws to the evaluation of new controls technology in flight test vehicles. A special report on the status of foreign aircraft technology and a panel session with seven representatives from organizations which use aircraft controls technology were also included. The controls research needs and opportunities for the future as well as the role envisioned for NASA in that research were addressed. Input from the panel and response to the workshop presentations will be used by NASA in developing future programs

Advanced Computational-Effective Control and Observation Schemes for Constrained Nonlinear Systems

Constraints are one of the most common challenges that must be faced in control systems design. The sources of constraints in engineering applications are several, ranging from actuator saturations to safety restrictions, from imposed operating conditions to trajectory limitations. Their presence cannot be avoided, and their importance grows even more in high performance or hazardous applications. As a consequence, a common strategy to mitigate their negative effect is to oversize the components. This conservative choice could be largely avoided if the controller was designed taking all limitations into account. Similarly, neglecting the constraints in system estimation often leads to suboptimal solutions, which in turn may negatively affect the control effectiveness. Therefore, with the idea of taking a step further towards reliable and sustainable engineering solutions, based on more conscious use of the plants' dynamics, we decide to address in this thesis two fundamental challenges related to constrained control and observation. In the first part of this work, we consider the control of uncertain nonlinear systems with input and state constraints, for which a general approach remains elusive. In this context, we propose a novel closed-form solution based on Explicit Reference Governors and Barrier Lyapunov Functions. Notably, it is shown that adaptive strategies can be embedded in the constrained controller design, thus handling parametric uncertainties that often hinder the resulting performance of constraint-aware techniques. The second part of the thesis deals with the global observation of dynamical systems subject to topological constraints, such as those evolving on Lie groups or homogeneous spaces. Here, general observability analysis tools are overviewed, and the problem of sensorless control of permanent magnets electrical machines is presented as a case of study. Through simulation and experimental results, we demonstrate that the proposed formalism leads to high control performance and simple implementation in embedded digital controllers