Multi-objective optimization has been adopted in many engineering problems where a set of requirements must be met to generate successful applications. Among them, there are the tuning problems from ...control engineering, which are focused on the correct setting of the controller parameters to properly govern complex dynamic systems to satisfy desired behaviors such as high accuracy, efficient energy consumption, low cost, among others. These requirements are stated in a multi-objective optimization problem to find the most suitable controller parameters. Nevertheless, these parameters are tough to find because of the conflicting control performance requirements (i.e., a requirement cannot be met without harming the others). Hence, the use of techniques from computational intelligence and soft computing is necessary to solve multi-objective problems and handle the trade-offs among control performance objectives. Meta-heuristics have shown to obtain outstanding results when solving complex multi-objective problems at a reasonable computational cost. In this survey, the literature related to the use of multi-objective meta-heuristics in intelligent control focused on the controller tuning problem is reviewed and discussed.
•Conflicting control requirements demand multi-objective meta-heuristic optimization for tuning.•Multi-objective meta-heuristics are powerful techniques for controller tuning.•The review covers the multi-objective metaheuristic optimization in controller tuning.•The full steps of the controller tuning process in works published from 2001 to 2019 are reviewed.•Possible research trends for multi-objective controller tuning are stated.
•An indirect adaptive control based on multi-objective optimization is proposed for the four-bar mechanism.•A Novel Online Hypervolume-Based Differential Evolution is proposed to handle the ...conflicting control requirements.•Conflicting control requirements demand multi-objective meta-heuristic optimization for tuning.•Experimental tests show the reliability of the proposal with the 99% Confidence Interval test.•The effectiveness of the proposal is also compared with state-of-the-art controllers.
Four-bar mechanisms have increased their use in current applications from industrial to rehabilitation systems. These applications become more demanding over time, and the control systems are required to provide them higher accuracy, lower energy consumption, and an extended lifetime, among other conflicting features. In addition to the previously mentioned demands, four-bar mechanisms have highly nonlinear dynamics and are often subject to external loads that make them difficult to control. In this paper, an indirect adaptive control based on online multi-objective optimization is proposed to regulate the speed of the four-bar mechanism and increase its lifetime by smoothing the control action under the effects of uncertainties. This consists of a multi-objective optimization process for the online identification of the model parameters that fulfill the performance demands of the mechanism. In this process, a multi-objective optimization problem is stated and then solved by the novel Online Hypervolume-based Differential Evolution (O-HV-MODE) in such a way that several promising model parameter configurations are found in real-time, with different trade-offs among the performance demands. O-HV-MODE takes advantage of the past problem knowledge to accelerate the search for new solutions and uses the Hypervolume metric to increase their convergence and diversity. Then, a single model parameter configuration is selected based on the application necessities and is further used in the nonlinear compensator of the computed-torque controller, while a fixed-gain PD control loop is used for stabilization. The proposed control is validated through experimental tests and the reliability of the results with the 99% Confidence Interval test. Also, the proposal is compared with state-of-the-art linear and non-linear control approaches.
The complexity in the design of bipedal robots has motivated the use of simple mechanisms to accomplish the desired locomotion task with a minimum control effort. Nevertheless, a diverse set of ...conflictive design criteria must be met to develop the bipedal gait. In this paper, the synergy in the eight-bar mechanism design criteria to satisfy the bipedal lower-limb behavior is promoted by proposing a Pareto-based Nonlinear Mixed Discrete-Continuous Constrained Multiobjective Optimization Problem and by improving the search in the optimizer through the inclusion of the Multiselection Strategy into the multiobjective differential evolution algorithm, where the conflictive design objectives are the continuous path generation based on an approximation error to track twenty precision points of the bipedal gait and also the force transmission exerted when the foot reaches the ground. The manufacture of a prototype with a specific design trade-off experimentally validates the obtained synergistic design with the proposed design approach. In addition, the empirical analysis in simulation through a statistical validation indicates that the Multiselection Strategy explores and exploits the design solutions promoting the diversity, convergence, and capacity features of the obtained Pareto front with respect to other four multiobjective optimizers and consequently improves the reconfigurability in the design such that more alternatives result to the designer decision making.
A solution to achieve global asymptotic tracking with bounded velocities in an omnidirectional mobile robot is proposed in this paper. It is motivated by the need of having a useful in-practice ...motion control scheme, which takes into account the physical limits of the velocities. To this end, a passive nonlinear controller is designed and combined with a tracking controller in a negative feedback connection structure. By using Lyapunov theory and passivity tools, global asymptotic tracking with desired bounded velocities is proved. Simulations and experimental results are provided to show the effectiveness of the proposal.
In recent years, mobile robots have been helpful systems to perform a wide variety of complex tasks in daily life applications from industry, academy, and home. These robots carry out mobility on ...flat terrains, mainly in narrow spaces that are difficult to access or dangerous for humans. Therefore, increasing the efficiency of their movements through control technologies has become a topic of great interest for researchers. Among controllers, the linear ones are widely used to improve the efficiency of mobile robots because of their simplicity, reliability, and practicality, notwithstanding advanced control strategies. A well-tuned linear controller can show outstanding performances in controlled environments where the modeled and simulated conditions used for its adjustment are not too far from reality. However, actual operating environments are subject to uncertainties and disturbances that can hardly be accounted for during the controller tuning process. The above compromises the performance of the mobile robot in practice, and finding the appropriate controller parameters that enhance robustness becomes a crucial task. Therefore, this work presents a robust tuning approach for the controller of an omnidirectional mobile robot based on the solution of a nonlinear dynamic optimization problem through meta-heuristics. Robustness is incorporated in the optimization problem by minimizing the sensitivity to the control performance indexes. Simultaneously, this is included through dynamic and stochastic variations in the meta-heuristic optimizer hyperparameters. A comparative statistical analysis is performed using robust and non-robust tuning approaches. Based on simulated and experimental tests, the proposed robust approach shows notable performance improvements regarding the non-robust one while minimizing operation errors in the presence of different uncertainty magnitudes.
The presence of parametric uncertainties decreases the performance in controlling dynamic systems such as the DC motor. In this work, an adaptive control strategy is proposed to deal with parametric ...uncertainties in the speed regulation task of the DC motor. This adaptive strategy is based on a bio-inspired optimization approach, where an optimization problem is stated and solved online by using a modification of the differential evolution optimizer. This modification includes a mechanism that promotes the exploration in the early generations and takes advantage of the exploitation power of the DE/best class in the last generations of the algorithm to find suitable optimal control parameters to control the DC motor speed efficiently. Comparative statistical analysis with other bio-inspired adaptive strategies and with linear, adaptive and robust controllers shows the effectiveness of the proposed bio-inspired adaptive control approach both in simulation and experimentation.
•The proposed bio-inspired adaptive approach efficiently controls the DC motor under parametric uncertainties.•The proposed algorithm finds changing optimum solutions in dynamic environment at each sampling time.•Comparative experimental results show the outperform performance with respect to other advanced control strategies.•Statistical analysis validates the proposal.
Mobile robots with omnidirectional wheels are expected to perform a wide variety of movements in a narrow space. However, kinematic mobility and dexterity have not been clearly identified as an ...objective to be considered when designing omnidirectional redundant robots. In light of this fact, this article proposes to maximize the dexterity of the mobile robot by properly locating the omnidirectional wheels. In addition, four hybrid differential evolution (DE) algorithm based on the synergetic integration of different kinds of mutation and crossover are presented. A comparison of metaheuristic and gradient-based algorithms for kinematic dexterity maximization is also presented.
An interesting problem in robotics is to minimize the required time to force a manipulator to travel between two specific points (positioning time). In this paper a concurrent structure-control ...redesign approach is proposed in order to find the minimum positioning time of an underactuated robot manipulator, by considering a synergetic combination between the structural parameters and a bang–bang control law. The problem consists in finding the structural parameters of the system and the switching intervals of the bang–bang control that simultaneously minimize the positioning time, subject to the input constraint and the structural parameter constraint. The concurrent structure-control redesign approach is stated as a dynamic optimization problem (DOP). The projected gradient method is used to solve the DOP.
The effectiveness of the proposed concurrent redesign approach is shown via simulation and experimental results on an underactuated system called the Pendubot.
This paper proposes a synergetic approach to design a planar parallel robot with its control system. In this proposal, the design problem is stated as a dynamic optimization problem with dynamic and ...static constraints on both the robot parameters and the control input to the robot. Control parameterization via PID controllers is used to rewrite the dynamic optimization problem as a nonlinear programming problem, which is solved by using a hybrid gradient-evolutionary optimization technique. The dynamic optimization problem presents singularity regions in the design space requiring the use of the proposed hybrid gradient-evolutionary optimization technique. The rationale behind the proposed hybrid algorithm lies in using a exploratory search mechanism for finding the initial guess to the fine search mechanism, which is used to search in a local region of a solution. We discuss both the results of the proposed optimization technique and the experimental results of the robot designed with the proposed approach. In addition, the result provided by the proposed synergetic design approach is compared with a sequential design approach, showing the advantages of the synergetic approach.
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