Andrzej W. Ordys

Adaptive cruise control with stop&go function using the state-dependent nonlinear model predictive control approach.

In the design of adaptive cruise control (ACC) system two separate control loops - an outer loop to maintain the safe distance from the vehicle traveling in front and an inner loop to control the brake pedal and throttle opening position - are commonly used. In this paper a different approach is proposed in which a single control loop is utilized. The objective of the distance tracking is incorporated into the single nonlinear model predictive control (NMPC) by extending the original linear time invariant (LTI) models obtained by linearizing the nonlinear dynamic model of the vehicle. This is achieved by introducing the additional states corresponding to the relative distance between leading and following vehicles, and also the velocity of the leading vehicle. Control of the brake and throttle position is implemented by taking the state-dependent approach. The model demonstrates to be more effective in tracking the speed and distance by eliminating the necessity of switching between the two controllers. It also offers smooth variation in brake and throttle controlling signal which subsequently results in a more uniform acceleration of the vehicle. The results of proposed method are compared with other ACC systems using two separate control loops. Furthermore, an ACC simulation results using a stop&go scenario are shown, demonstrating a better fulfillment of the design requirements.

Non-linear predictive control of 2 DOF helicopter model

This paper presents the application of non-linear predictive control algorithm to a helicopter model. First, the model of the helicopter is discussed. Next, the nonlinear algorithm is introduced which is based on state-space GPC controller. The non-linearity is handled by converting the state-dependent state-space representation into the linear time-varying representation. The predictions of the future controls are used to calculate predictions of the future states and of the future time varying system parameters. Applied to the helicopter model, the algorithm performs well. It is capable of the stabilizing the system for maneuvers for which its linear counterpart fails.

Predictive control for industrial applications

Abstract An overview is first provided of some of the most common formulations of Predictive Control, such as Dynamic Matrix Control (DMC), Predictive Functional Control (PFC), Preview Control and Generalized Predictive Control (GPC). The main features, advantages and disadvantages are discussed. A new algorithm, based on a so called Linear Quadratic Generalized Predictive Controller (LQGPC) is then introduced. This algorithm combines features of Predictive Control with those of Linear Quadratic Gaussian Control, providing improved stability and robustness properties. Attention then turns to the state-space formulation of the problem. An algorithm called Dynamic Performance Predictive Control is briefly outlined and some links to the Linear Quadratic Generalized Predictive Controller are exploited. Finally, applications of new Predictive Control Techniques are discussed based on industrial examples from the Power and Steel industries. These are industrial fields where Predictive Control has not yet established a leading position. However, the new algorithms, due to their improved stability and robustness properties, have the potential to provide real quality and performance benefits.

Adaptive Cruise Control System: Comparing Gain-Scheduling PI and LQ Controllers

Abstract Over the recent years, a considerable growth in the number of vehicles on the road has been observed. This increases importance of vehicle safety and minimization of fuel consumption, subsequently prompting manufacturers to equip cars, with more advanced features such as adaptive cruise control (ACC)or collision avoidance and collision warning system (CWS). This paper investigates two control applications design namely the gain scheduling proportional-integral (GSPI) control and gain scheduling Linear Quadratic (GSLQ)control for ACC, covering a high range speed. The control system consist of two loops in cascade, with the inner loop controlling the vehicle speed and the outer loop switching between the cruise control (CC) and the ACC mode and calculating the reference speed. A nonlinear dynamic model of the vehicle is constructed and then a set of operating points is determined and then a of linear models is extracted in operating point. For each operating point, PI and LQ controllers are obtained off-line. An integrated Simulink model including the nonlinear dynamic vehicle model and the ACC controller (either PI or LQ) was used to test the controllers in various traffic scenarios. Comparison results between the two controllers applications is provided to show the validity of the design.

Optimized discrete-time state dependent Riccati equation regulator

The state dependent Riccati equation was originally developed for the continuous time systems. In the paper the optimality of a discrete time version of the state dependent Riccati equation is considered. The derivation of the optimal control strategy is based on the Hamiltonian optimal solution for the nonlinear optimal control problem. The new form of the discrete state dependent Riccati equation with a correction tensor is derived. The prediction of the future trajectory is used in the derivation.

Tutorial introduction to the modelling and control of hybrid systems

A tutorial introduction is provided to the relatively new subject of hybrid systems. The modelling of hybrid systems is assuming ever greater importance for systems where the combination of continuous control and with logical decision making is required. This arises in some of the most critical operating regions where systems are under start-up, shutdown or are undergoing major planned changes. There is recognition that separate independent design of these functions will reduce achievable performance and cause unpredictable behaviour in some of the most safety critical areas of operation. The introduction provided is not exhaustive but it introduces some of the main concepts and motivates the use in applications of this relatively new area of control design. The theoretical results are illustrated using engineering examples.

Position control of flexible manipulator using non-linear H∞ with state-dependent Riccati equation

Abstract The paper is concerned with the control of the tip position of a single-link flexible manipulator. The non-linear model of the manipulator is derived and tested, assuming the number of model shape functions to be two. It is known that the assumed modes method introduces uncertainty to the model by neglecting higher-order dynamics. There are other sources of uncertainty, such as friction. In addition, the model is non-linear. Therefore, for the next task, which is the controller design, the H∞ approach is proposed to deal efficiently with uncertainties, and the non-linear nature of the problem is addressed by the use of the state-dependent Riccati equation (SDRE) technique. Following the SDRE approach, the state-feedback non-linear control law is derived, which minimizes a quadratic cost function. This solution is then mapped into the H∞ optimization problem. The resulting control law has been tested with the simulation model of the flexible manipulator and the results are discussed in the paper.

End-point control of a flexible-link manipulator using H/sub /spl infin// nonlinear control via a state-dependent Riccati equation

The problem of modeling and controlling the tip position of a single-link flexible manipulator is considered. In a flexible-link manipulator in general the effect of some parameters such as payload, friction amplitude and damping coefficients can not be exactly measured, One possibility is to consider these parameters including uncertainty. Recent results may then be applied on nonlinear robust regulators using a nonlinear H/sub /spl infin// via state dependent Ricatti equation (SDRE) design method. Lagrangian mechanics and the assumed mode method have been used to derive a proposed dynamic model of a single-link flexible manipulator having a control joint. The full state feedback nonlinear H/sub /spl infin// SDRE control law is derived to minimize a quadratic cost function that penalizes the states and the control input torques. Simulation results are presented for a single-link flexible manipulator to achieve the desired angular rotation of the link whilst simultaneously suppressing structural vibrations. The effect of payload on the system response and vibration frequencies is also investigated. The results are illustrated by a numerical example.

Simulation Validation of Three Nonlinear Model-Based Controllers in the Adaptive Cruise Control System

In this paper, the simulation validation of the hierarchical two-loop Adaptive Cruise Control (ACC) system is considered as a candidate for the application in the Advanced Driver Assistance Systems (ADAS), which aims to ensure driving safety and comfort as well as to improve fuel efficiency. Three different nonlinear model-based approaches for the inner-loop controllers are investigated for this system: the conventional Proportional-Integral Gain Scheduling controller (PI + GS) and two other strategies based on the simplified modelling of the vehicle dynamics: Balance-Based Adaptive Controller (B-BAC) and Nonlinear Model Predictive Controller (NMPC). The performance of each considered ACC system is tested by simulation under the same realistic scenarios for distance tracking mode and switching mode. The comparative criteria include driving safety, comfort of the driver and passengers and the fuel-economy aspect defined as BSFC (Brake Specific Fuel Consumption) index. The simulation results demonstrate that all the considered control algorithms meet the safety and car-following requirements while they provide slightly different level of driving comfort and fuel consumption, depending on the traffic situation and operating mode.

State space generalized predictive control incorporating direct through terms

A new formulation of the multivariable state space generalised predictive controller is presented, which utilises a model with direct feed-through action. Such models can arise as a consequence of neglecting the fast dynamics of a system or as a consequence of discretization of continuous-time models. Implementation of the algorithm and application to upper level control of a power plant gas turbine simulation is discussed.