This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

Given restrictions that comfort conditions in the interior of a building are satisfied, it becomes obvious that the problem of energy conservation is a multidimensional one. Scientists from a variety of fields have been working on this problem for a few decades now; however, essentially it remains an open issue. In the beginning of this article, we define the whole problem in which the topics are: energy, comfort and control. Next, we briefly present the conventional control systems in buildings and their advantages and disadvantage. We will also see how the development of intelligent control systems has improved the efficiency of control systems for the management of indoor environment including user preferences. This paper presents a survey exploring state of the art control systems in buildings. Attention will be focused on the design of agent-based intelligent control systems in building environments. In particular, this paper presents a multi-agent control system (MACS). This advanced control system is simulated using TRNSYS/MATLAB. The simulation results show that the MACS successfully manage the user’s preferences for thermal and illuminance comfort, indoor air quality and energy conservation.

Advanced control systems engineering for energy and comfort management in a building environment—A review

Survey paper: A survey on industrial applications of fuzzy control

This paper proposes a motion planner for autonomous parking. Compared to the prevailing and emerging studies that handle specific or regular parking scenarios only, our method describes various kinds of parking cases in a unified way regardless they are regular parking scenarios (e.g., parallel, perpendicular or echelon parking cases) or not. First, we formulate a time-optimal dynamic optimization problem with vehicle kinematics, collision-avoidance conditions and mechanical constraints strictly described. Thereafter, an interior-point simultaneous approach is introduced to solve that formulated dynamic optimization problem. Simulation results validate that our proposed motion planning method can tackle general parking scenarios. The tested parking scenarios in this paper can be regarded as benchmark cases to evaluate the efficiency of methods that may emerge in the future. Our established dynamic optimization problem is an open and unified framework, where other complicated user-specific constraints/optimization criteria can be handled without additional difficulty, provided that they are expressed through inequalities/polynomial explicitly. This proposed motion planner may be suitable for the next-generation intelligent parking-garage system.

A unified motion planning method for parking an autonomous vehicle in the presence of irregularly placed obstacles

In this paper, a new model-free adaptive integral sliding-mode-constrained control scheme is presented for autonomous four-wheeled mobile vehicle (4WMV) parking systems. The proposed control strategy includes: 1) online identification for the object model based on a data-driven technique is presented for 4WMV and 2) an integral sliding-mode controller with control input constraints and stability analysis is provided. For online data-driven model identification, a compact-form dynamic linearization-based observer formulation is constructed for 4WMV. For integral sliding-mode controller design, a dynamic antiwindup compensator is introduced to solve integral saturation and actuator saturation problems in the autonomous 4WMV parking system. The designed control scheme only utilizes the body angle and steering angle of the vehicle under the condition that the precise mechanism model of the autonomous 4WMV parking system is not available to us. Finally, simulation comparisons between the model-free adaptive integral sliding-mode-constrained control scheme, PID control, and model-free adaptive control algorithms with coordinate compensation are given for two different vehicles. The simulation results show that a better control performance is achieved with the proposed new sliding-mode controller than the traditional control method when actuator or control input saturation exists in the autonomous 4WMV parking system.

Model-Free Adaptive Discrete-Time Integral Sliding-Mode-Constrained-Control for Autonomous 4WMV Parking Systems

Robust Automatic Parallel Parking in Tight Spaces via Fuzzy Logic

An industrial weigh belt feeder is used to transport solid materials into a manufacturing process at a constant feedrate. It exhibits nonlinear behavior because of motor friction, saturation, and quantization noise in the sensors, which makes standard autotuning methods difficult to implement. The paper proposes and experimentally demonstrates two types of fuzzy logic controllers for an industrial weigh belt feeder. The first type is a PI-like fuzzy logic controller (FLC). A gain scheduled PI-like FLC and a self-tuning PI-like FLC are presented. For the gain scheduled PI-like FLC the output scaling factor of the controller is gain scheduled with the change of setpoint. For the self-tuning PI-like FLC, the output scaling factor of the controller is modified online by an updating factor whose value is determined by a rule base with the error and change of error of the controlled variable as the inputs. A fuzzy PI controller is also presented, where the proportional and integral gains are tuned online based on fuzzy inference rules. Experimental results show the effectiveness of the proposed fuzzy logic controllers. A performance comparison of the three controllers is also given.

Fuzzy PI control design for an industrial weigh belt feeder

This paper proposes and experimentally demonstrates an approach to automated PI tuning for an industrial weigh belt feeder that is based on unfalsified control concepts. Unfalsified control is used here as a means of using either open- or closed-loop test data to identify a subset of controllers (from an initial set) that is not proved to violate the multiple objectives specified by the control engineer. A novel feature of the unfalsified approach is that it allows controllers to be eliminated from consideration by predicting their performance without actually inserting the controllers in the loop. In addition, this methodology does not require an explicit model. However, in practice, it does require some closed-loop experimentation to determine the cost functions used to perform the unfalsification. When the unfalsified PI autotuning approach is applied to the industrial weigh belt feeder, it is able to successfully identify a subset of PI control laws that meets the performance specs. A key feature of this paper is the use of a genetic search algorithm to reduce the computational requirements of unfalsified control, especially when the initial set of controllers is large. Copyright © 2001 John Wiley & Sons, Ltd.

A genetic search approach to unfalsified PI control design for a weigh belt feeder

This paper develops and experimentally demonstrates an automatic parallel parking control algorithm for autonomous ground vehicles (AGVs). The automatic parallel parking problem is described and fuzzy logic controllers are designed for each step of the parking process. Detailed membership functions and fuzzy reasoning rules are given for each fuzzy logic controller. The parking algorithm is tested by simulation and experimental implementation based on the model of a skid steering autonomous ground vehicle. Both results demonstrate the ability of the developed algorithm to parallel park AGVs in tight spaces.

Fuzzy parallel parking control of autonomous ground vehicles in tight spaces

This paper proposes and experimentally demonstrates two types of fuzzy logic controllers for an industrial weigh belt feeder. The first type is a PI-like fuzzy logic controller (FLC). A gain scheduled PI-like FLC and a self-tuning PI-like FLC are presented. For the gain scheduled PI-like FLC the output scaling factor of the controller is gain scheduled with the change of setpoint. For the self-tuning PI-like FLC, the output scaling factor of the controller is modified on-line by an updating factor whose value is determined by a rule-base with the error and change of error of the controlled variable as the inputs. A fuzzy PI controller is also presented, where the proportional and integral gains are tuned on-line based on fuzzy inference rules. Experimental results show the effectiveness of the proposed fuzzy logic controllers.

Fuzzy PI control of an industrial weigh belt feeder

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