Non-linear predictive control for manufacturing and robotic applications

The paper discusses predictive control algorithms in the context of applications to robotics and manufacturing systems. Special features of such systems, as compared to traditional process control applications, require that the algorithms are capable of dealing with faster dynamics, more significant unstabilities and more significant contribution of non-linearities to the system performance. The paper presents the general framework for state-space design of predictive algorithms. Linear algorithms are introduced first, then, the attention moves to non-linear systems. Methods of predictive control are presented which are based on the state-dependent state space system description. Those are illustrated on examples of rather difficult mechanical systems

Systems And Methods For Parameter Dependent Riccati Equation Approaches To Adaptive Control

Systems and methods for adaptive control are disclosed. The systems and methods can control uncertain dynamic systems. The control system can comprise a controller that employs a parameter dependent Riccati equation. The controller can produce a response that causes the state of the system to remain bounded. The control system can control both minimum phase and non-minimum phase systems. The control system can augment an existing, non-adaptive control design without modifying the gains employed in that design. The control system can also avoid the use of high gains in both the observer design and the adaptive control law.Georgia Tech Research Corporatio

Aircraft adaptive learning control

The optimal control theory of stochastic linear systems is discussed in terms of the advantages of distributed-control systems, and the control of randomly-sampled systems. An optimal solution to longitudinal control is derived and applied to the F-8 DFBW aircraft. A randomly-sampled linear process model with additive process and noise is developed

Variance-constrained multiobjective control and filtering for nonlinear stochastic systems: A survey

The multiobjective control and filtering problems for nonlinear stochastic systems with variance constraints are surveyed. First, the concepts of nonlinear stochastic systems are recalled along with the introduction of some recent advances. Then, the covariance control theory, which serves as a practical method for multi-objective control design as well as a foundation for linear system theory, is reviewed comprehensively. The multiple design requirements frequently applied in engineering practice for the use of evaluating system performances are introduced, including robustness, reliability, and dissipativity. Several design techniques suitable for the multi-objective variance-constrained control and filtering problems for nonlinear stochastic systems are discussed. In particular, as a special case for the multi-objective design problems, the mixed H 2 / H ∞ control and filtering problems are reviewed in great detail. Subsequently, some latest results on the variance-constrained multi-objective control and filtering problems for the nonlinear stochastic systems are summarized. Finally, conclusions are drawn, and several possible future research directions are pointed out

Optimal sliding mode controllers for attitude tracking of spacecraft

This paper studies two optimal sliding mode control laws using integral sliding mode control (ISM) for some spacecraft attitude tracking problems. Integral sliding mode control combining the first order sliding mode and optimal control is applied to quaternion-based spacecraft attitude tracking manoeuvres with external disturbances and an uncertainty inertia matrix. For the optimal control part the state dependent Riccati equation (SDRE) and Control Lyapunov function (CLF) approaches are used to solve the infinite-time nonlinear optimal problem. The second method of Lyapunov is used to show that tracking is achieved globally. An example of multiaxial attitude tracking manoeuvres is presented and simulation results are included to verify the usefulness of these controllers

An investigation study on model reference adaptive techniques as applied to attitude control system for launch vehicles Final report

Self adaptive control techniques for attitude control on Saturn 5 launch vehicle

Modeling, Analysis, and Optimization Issues for Large Space Structures

Topics concerning the modeling, analysis, and optimization of large space structures are discussed including structure-control interaction, structural and structural dynamics modeling, thermal analysis, testing, and design

Nonlinear suboptimal and adaptive pectoral fin control of autonomous underwater vehicle

Autonomous underwater vehicles (AUVs) are used for numerous applications in the deep sea, such as hydrographic survey, sea bed mining and oceanographic mapping, etc. Presently, significant amount of effort, is being made in developing biorobotic AUVs (BAUVs) with biologically inspired control surfaces. However, the dynamics of AUVs and BAUVs are highly nonlinear and the hydrodynamic coefficients are not precisely known. As such the development of nonlinear and adaptive control systems is of considerable importance; We consider the suboptimal dive plane control of AUVs using the state-dependent Riccati equation (SDRE) technique. This method provides effective means of designing nonlinear control systems for minimum as well as nonminimum phase AUV models. Moreover, hard control constraints are included in the design process; We also attempt to design adaptive control systems for BAUVs using biologically-inspired pectoral-like fins. The fins are assumed to be oscillating harmonically with a combined linear (sway) and angular (yaw) motion. The bias (mean) angle of the angular motion of the fin is used as a control input. Using discrete-time state variable representation of the BAUV, adaptive sampled-data control systems for the trajectory control are derived using state feedback as well as output feedback. We develop direct as well as indirect adaptive control systems for BAUVs. The advantage of the indirect adaptive law lies in its applicability to minimum as well as nonminimum phase systems. Simulation results are presented to evaluate the performance of each control system