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In recent years, there has been a growing interest in controlling both active and semi-active automotive suspension systems with a goal of improving ride comfort and vehicle handling. Many such resulting approaches have used linearized models Of the syspension's dynamics, allowing th use of linear (optimal) control theory. In actuality through, these systems and their optimal control are quite nonlinear. In this paper we propose a novel, yet highly practical alternative to such linearized design methods. This alternate optimal design method consists of a modified A* optimal-path, farward-search algorithm which is highly efficient, together with neural networks. The A* search, using a reasonably accurate system model and a given cost function, establishes te nonlinear optimal parametric control Of the suspension. The neural network, as will be shown, learns this nonlinear optimal control function, and in many ways outperforms the search from which it was taught.

Today's advanced microcomputers offer extremely fast processing speed and vibrant high-resolution color graphics capabilities at affordable cost. By using carefully developed courseware (software used for coursework), these technologies can be used to strengthen engineering course delivery and provide laboratory innovation. This paper describes an interactive computer visualization environment in which real-time simulation and animation (RTSA) is used to significantly enhance control engineering curricula. Presented is the potential impact of the proposed environment on control engineering education with illustrative examples. The roles of this new interactive computer learning experience in control engineering curriculum are two-fold: 1) To provide a new method for delivering classroom material whereby real-world control system engineering concepts are introduced via interative RTSA, and 2) To provide an opportunity innovative laboratory assignments where students can analyze, design and modify control engineering systems via interactive RTSA. Positive acceptance and reactions have been widely received from users and audiences of RTSA programs which were incorporated in research projects and control curricula at Oakland University.

A nonlinear Self-Tuning Control (STC) scheme for the nonlinear vehicle active suspension system is investigated and analyzed as an alternative suspension control methodology. The result of the nonlinear STC approach is compared with the linear STC and the model reference adaptive control (MRAC) schemes. The objective of these adaptive control active suspension systems (ACASS) is to tune the controller of the active suspension system automatically such that vehicle disturbance and vibration are reduced to a prespecified level determined by an "ideal" conceptual suspension system. Thus, an ACASS can accommodate for variances in sprung load and suspension component characteristics. The design and performance analysis of a nonlinear STC suspension system is based on a quarter-car model. Our results show significant improvement in the road handling performance of a high-speed vehicle with the STC active suspension system.

Application of Model Reference Adaptive Control (MRAC) to vehicle active suspension subsystem is investigated. The MRAC objective is to automatically self-tune the controller of the active suspension such that disturbance and vibration of a vehicle is reduced to a level which is determined by an "ideal" conceptual suspension system (a reference model). An MRAC vehicle suspension can thus accommodate for variances in sprung load and suspension component characteristics. An MRAC was designed using Lyapunov stability approach and was tested using different scenarios such as ±10% to ±30% variances of suspension components and sprung load. It is shown that the MRAC suspension closely followed the performance of the prespecified reference model and achieve significant improvements over passive suspension. Real-Time Simulation and Animation (RTSA) software was developed to provide an excellent visual aid for understanding and interpreting the better ride performance achieved by the MRAC suspension.

The linear quadratic design of an optimal robust servo–controller for a continuous–time control system is described. It introduces a servo–control performance measure which accommodates the internal model principle. The measure selectively discounts penalties on control effort of desirable frequencies and provides complete flexibility in the selection of weighting matrices. The proposed servo–controller uses plant state feedback, signal state feedforward and a servo–compensator for ensuring robust asymptotic command tracking and disturbance rejection. An informal alternate proof of the internal model principle in the state variable domain is presented using an operator-transformation technique. Where necessary, observers are used to complement the servo-controller. The proposed optimal robust servo–controller yields the expected superior performance in terms of response and error minimization. An illustrative example is given.

The problem of vibration isolation is investigated from the standpoint of modern control and optimization theory. The proposed optimal model-following suspension design was verified experimentally by means of a microcomputerized suspension model. The experimental results are very encouraging and indicate promising potential application of the proposed scheme to real-world systems. This paper describes the modeling and formulation of an optimal control suspension reference model, and the implementation algorithm of the proposed microcomputerized optimal model-following suspension scheme.

This paper presents a unified treatment to the problem of optimal feedback control of discrete-time linear shift-invariant systems using state feedback, output feedback, dynamical output feedback, and observer-controller feedback. A class of generalized optimal feedback controllers is proposed as a basis for obtaining existing results as well as for deriving new results such as the interesting min-max feedback controllers. The essence of the generalized results lies in the minimization of a class of generalized performance measures subject to a flexible controller configuration. The stability aspect of the generalized optimal feedback controlled system is addressed.

The theory of optimal output feedback control is extended to a class of quadratic performance measures whose weighting matrices are functions of frequency. The incorporation of frequency-dependent weighting matrices allows one to emphasise or de-emphasise the importance of the variables being penalised over specific bands of frequencies. Optimization of the frequency-shaped performance measure yields optimal compensators that can be more meaningful for certain minimisation problems. Results are presented for different configurations of frequency-shaping feedback control. An example is presented to illustrate the usefulness of the design technique.

This paper describes a technique for modeling and identifying a class of nonlinear servomechanism systems with stick-slip friction. The physics of the stick-slip friction is considered in modeling the process. Identification of the system parameters is formulated as a nonlinear optimization problem. A modified simplex algorithm is proposed as the optimization procedure. The difficulties encountered in choosing identification algorithm and input signals for the problem are discussed. A simulation example of a servomotor system is provided.

The reaction mechanism of DNA topoisomerase II (TOP2) involves a covalent double-strand break intermediate in which the enzyme is coupled to DNA via a 5′-phosphotyrosyl bond. This normally transient enzyme-bridged break is stabilised by drugs such as mitoxantrone, mAMSA, etoposide, doxorubicin, epirubicin and idarubicin, which are referred to as TOP2 poisons. Removal of topoisomerase II by the proteasome is involved in the repair of these lesions. In K562 cells, inhibiting the proteasome with MG132 significantly potentiated the growth inhibition by these six drugs that target topoisomerase II, and the highest level of potentiation was observed with mitoxantrone. Mitoxantrone also showed the greatest potentiation by MG132 in three Nalm 6 cell lines with differing levels of TOP2A or TOP2B. Mitoxantrone was also potentiated by the clinically used proteasome inhibitor PS341 (Velcade). We have also shown that proteasome inhibition with MG132 in K562 cells reduces the rate of removal of mitoxantrone or etoposide stabilised topoisomerase complexes from DNA, suggesting a possible mechanism for the potentiation of topoisomerase II drugs by proteasomal inhibition.

A class of general structured discrete-time deterministic observers is developed. The one-step predicting or Luenberger observers and the current-update observers may be obtained from this class of general structured observers. Interesting relationships and important properties among various full-order observers are established. Optimal discrete-time observer gains are then derived, and it is shown that the optimal observers can be structually and numerically equivalent to various forms of Kalman filters.