Adaptive fuzzy predictive sliding control of uncertain nonlinear systems with bound-known input delay
Mostafa Khazaee1, Amir H D Markazi1, Ehsan Omidi2
1Digital Control Laboratory, School of Mechanical Engineering, Iran University of Science and Technology, Narmak, 16844 Tehran, Iran.
ISA Transactions
|November 4, 2015
Summary
A novel Adaptive Fuzzy Predictive Sliding Mode Control (AFP-SMC) effectively manages nonlinear systems with uncertain dynamics and unknown input delays. This advanced control strategy ensures system stability and accurate performance, validated through simulations and experiments.
Area of Science:
- Control Systems Engineering
- Nonlinear System Dynamics
- Fuzzy Logic Applications
Background:
- Nonlinear systems often exhibit complex dynamics and are susceptible to performance degradation due to unknown input delays.
- Existing control methods may struggle to simultaneously address system uncertainties and time delays effectively.
- Robust control strategies are crucial for maintaining stability and performance in such challenging environments.
Purpose of the Study:
- To introduce a new Adaptive Fuzzy Predictive Sliding Mode Control (AFP-SMC) for nonlinear systems.
- To address challenges posed by uncertain system dynamics and unknown input delays.
- To develop a control approach that ensures stability and compensates for time-varying parameters.
Main Methods:
- Developed an AFP-SMC framework integrating a fuzzy inference system for ideal linearization control approximation.
- Incorporated a switching strategy to mitigate estimation errors.
- Utilized an adaptive fuzzy predictor to estimate future system states and compensate for input delays.
- Employed adaptation laws for tuning controller and predictor parameters, ensuring stability via a Lyapunov-Krasovskii functional.
Main Results:
- The AFP-SMC demonstrated effective control of nonlinear systems with uncertain dynamics and bounded input delays.
- Simulations and experimental results on an overhead crane system validated the proposed method's efficacy.
- The controller successfully compensated for system uncertainties and time delays, maintaining stable operation.
Conclusions:
- The proposed AFP-SMC is a viable and effective solution for controlling uncertain nonlinear systems with input delays.
- The integration of fuzzy logic and predictive control offers significant advantages in handling complex system dynamics.
- The method's robustness and stability guarantees are confirmed through theoretical analysis and practical validation.
Related Concept Videos
01:26Feedback control systems
793
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
793
01:07Time-Domain Interpretation of PD Control
440
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
Consider the example of control of motor torque. Initially, a positive...
440
01:21Linear Approximation in Time Domain
406
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
406
01:26PD Controller: Design
717
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
717
01:21First Order Systems
494
First-order systems, such as RC circuits, are foundational in understanding dynamic systems due to their straightforward input-output relationship. Analyzing their responses to different input functions under zero initial conditions reveals significant insights into system behavior.
When a first-order system is subjected to a unit-step input, its response is characterized by its transfer function. By applying the Laplace transform of the unit-step input to the transfer function, expanding the...
When a first-order system is subjected to a unit-step input, its response is characterized by its transfer function. By applying the Laplace transform of the unit-step input to the transfer function, expanding the...
494
01:22Multi-input and Multi-variable systems
484
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
In the absence of...
484