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

State estimation and the equivalence of the regulatory and supervisory predictive control law

This paper discusses the effect of state estimation on the equivalence between the regulatory and supervisory predictive control strategies for linear time invariant systems. The analysis presented here shows that in the presence of model-system mismatch, the use of a state estimator rather than the actual state in the feedback loop does not affect the equivalence between the two strategies

Wiener modelling and model predictive control for wastewater applications

The research presented in this paper aims to demonstrate the application of predictive control to an integrated wastewater system with the use of the wiener modeling approach. This allows the controlled process, dissolved oxygen, to be considered to be composed of two parts: the linear dynamics, and a static nonlinearity, thus allowing control other than common approaches such as gain-scheduling, or switching, for series of linear controllers. The paper discusses various approaches to the modelling required for control purposes, and the use of wiener modelling for the specific application of integrated waste water control. This paper demonstrates this application and compares with that of another nonlinear approach, fuzzy gain-scheduled control

Performance assessment of MIMO systems under partial information

Minimum variance (MV) can characterize the most fundamental performance limitation of a system, owing to the existence of time-delays/infinite zeros. It has been widely used as a benchmark to assess the regulatory performance of control loops. For a SISO system, this benchmark can be estimated given the information of the system time delay. In order to compute the MIMO MV benchmark, the interactor matrix associated with the plant may be needed. However, the computation of the interactor matrix requires the knowledge of Markov parameter matrices of the plant, which is rather demanding for assessment purposes only. In this paper, we propose an upper bound of the MIMO MV benchmark which can be computed with the knowledge of the interactor matrix order. If the time delays between the inputs and outputs are known, a lower bound of the MIMO MV benchmark can also be determined

Models of stochastic systems in control systems robotics and automation

This entry looks at models of stochastic systems in control systems robotics and automatio

The optimal non-linear generalised predictive control by the time-varying approximation

This paper looks at the optimal non-linear generalised predictive control by the time-varying approximatio

Application of efficient nonlinear predictive control to a hot strip finishing mill

The application of the efficient nonlinear predictive control algorithms, emerging from recent research, to hot strip finishing mills is discussed. The method of controlling profile is based on explicit manipulation of the strip thickness across its width. An efficient model-based predictive control algorithm is employed to generate the control signal for the stands and to coordinate the stands for optimal profile and shape control. Simulation results are presented to demonstrate the performance of the proposed coordinated control scheme